State-Dependent Control of Neural Activity in the Olfactory Cortex

Home , Olfactory system, Visual processing

STATE-DEPENDENT CONTROL OF NEURAL ACTIVITY IN THE OLFACTORY CORTEX

KAITLIN S. CARLSON

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Thesis Advisor: Dr. Daniel Wesson

Department of Neurosciences

CASE WESTERN RESERVE UNIVERSITY

August, 2018

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the dissertation of

Kaitlin S. Carlson

candidate for the degree of Doctor of Philosophy*

Committee Chair……………………………………………... Ben W. Strowbridge, Ph.D.

Member………………………………………………………… Daniel W. Wesson, Ph.D.

Member…………………………………………………………… Evan S. Deneris, Ph.D.

Member……………………………………………………… Brian M. McDermott, Ph.D.

Date of Defense

May 30th, 2018

*We also certify that written approval has been obtained for any proprietary material contained herein.

Table of Contents List of Figures……………………………………………………………………...... iv Acknowledgments…………………………………………………………………….. viii Abstract………………………………………………………………………………...... 1 CHAPTER 1: INTRODUCTION……………………………………………………. 3 1.1 Sensory Systems………………………………………………………………… 3 1.1.1 Why we need sensory systems……………………………………………… 3 1.1.2 The mammalian olfactory system………………………………………….. 6 1.2 State-Dependent Influences on Sensory Processing…………………………….. 8 1.2.1 Arousal, sleep, anesthesia, and the olfactory system………………………. 8 1.3 Cognitive Influences on Sensory Systems………………………………………. 10 1.3.1 Historical context……………………………………………………...... 10 1.3.2 Attention…………………………………………………………………… 12 1.3.3 Modulation in vision and audition…………………………………………. 14 1.3.4 Modulation in chemosensory systems……………………………………… 16 1.4 Network vs Single-Unit Activity………………………………………………... 17 1.5 Olfactory Cortex Anatomy……………………………………………………… 19 1.5.1 The olfactory tubercle……………………………………………………… 19 1.6 Studying Attention in Non-Human Animal Models………………………...... 21 1.6.1 Types of attention…………………………………………………………... 21 1.6.2 Psychophysics and attention in olfaction…………………………………... 21 1.6.3 Techniques to study attention in rodents…………………………………… 22 1.7 Questions and Hypotheses: State Influences on Network and SUA…………….. 24 1.7.1 State-dependent local network level modulation…………………………… 24 1.7.2 Attentional influences on behavior………………………………………… 26 1.7.3 Attention-dependent changes in neural coding……………………………. 26 CHAPTER 2: ODOR- AND STATE-DEPENDENT OLFACTORY TUBERCLE LOCAL FIELD POTENTIAL DYNAMICS IN AWAKE RATS…………………... 33 2.1 Abstract……………………………………………………………………...... 33 2.2 Introduction……………………………………………………………………... 34 2.3 Results…………………………………………………………………………... 37 2.3.1 Theta-band power dominates spontaneous network activity………………. 37 2.3.2 Odor stimulation evokes beta- and gamma-band increases in the OT……………………………………………………………………...... 38 2.3.3 Spontaneous and odor-evoked LFP activity within the OT is similar to the upstream OB……………………………………………………………. 40 2.3.4 Both spontaneous and odor-evoked activity correspond with functional coherence across all spectral bands……………………………………….. 42 2.3.5 Inhalation-triggered OT theta cycles lag behind those of the OB……….. 42 2.3.6 Sleep-like and anesthesia-dependent modulation of spontaneous activity……………………………………………………………………… 43 2.3.7 Limited influences of sleep-like states and anesthesia on odor-evoked activity……………………………………………………………………… 43 2.4 Discussion………………………………………………………………………. 47 2.5 Conclusions……………………………………………………………………. 53 2.6 Methods……………………………………………………………………...... 54

2.7 Acknowledgments……………………………………………………………... 62 CHAPTER 3: SELECTIVE ATTENTION CONTROLS OLFACTORY DECISIONS AND THE NEURAL ENCODING OF ODORS…………………….. 83 3.1 Abstract………………………………………………………………………... 83 3.2 Introduction……………………………………………………………………... 84 3.3 Results…………………………………………………………………………. 86 3.3.1 Individual rat behavioral shaping on the CAT…………………………….. 87 3.3.2 Rats can selectively attend to odors, which dictates discrimination accuracy………………………………………………………………...... 87 3.3.3 Rats improve their ability to shift attention to odors with experience……… 88 3.3.4 Increased perceptual difficulty delays odor-directed shifts in attention…. 90 3.3.5 Additional influences of enhanced cognitive demand, trial congruency, and multisensory input on behavioral responses…………………………… 90 3.3.6 Odor-directed selective attention bi-directionally sculpts the encoding of odors in the OT………………………………………………………… 93 3.3.7 Odor-excited neurons increase their FRs, while odor-inhibited neurons further decrease their FRs with odor-directed attention…………………… 93 3.3.8 Odor-directed attention enhances the signal-to-noise ratio of odor- evoked units……………………………………………………………….. 96 3.3.9 FRs of units unmodulated by odor are not significantly different with attention……………………………………………………………………. 97 3.4 Discussion……………………………………………………………………... 98 3.5 Methods………………………………………………………………………... 102 3.6 Acknowledgments...... 114 CHAPTER 4: DISCUSSION………………………………………………………... 139 4.1 Major Conclusions……………………………………………………………. 139 4.2 Influences of Selective Attention on Odor Coding…………………………….. 140 4.3 Caveats……………………………………………………………………...... 143 4.3.1 CAT design………………………………………………………………. 143 4.3.2 Attention and reward……………………………………………………... 147 4.3.3 Limits of extracellular techniques……………………………………….. 148 4.4 Future Directions………………………………………………………………. 150 4.4.1 Influences of increased attentional demand……………………………... 150 4.4.2 Multimodal influences……………………………………………………. 152 4.4.3 Active sampling………………………………………………………...... 152 4.4.4 Neuromodulatory influences……………………………………………. 153 4.4.6 Goal cells………………………………………………………………… 156 Bibliography………………………………………………………………………….. 169

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List of Figures

Figure 1-1. Hypotheses: Odor- and state-dependent modulation of local field potential activity within the OB and OT…………………………………………………29

Figure 1-2. Hypotheses: Odor-directed selective attention modulates performance accuracy, shapes sampling durations, and sculpts the underlying odor coding………….31

Figure 2-1. Experimental timeline and electrode tip locations for olfactory tubercle

(OT) and olfactory bulb (OB) recordings...………...……………………………………63

Figure 2-2. Physiological classification of sleep state for state-dependent analysis...….65

Figure 2-3. OT LFP activity in awake rats……………………………………………...67

Figure 2-4. Odor-evoked modulation of OT LFP activity in awake rats…..…………...69

Figure 2-5. Spontaneous LFP activity within the OT compared with the upstream

OB……………………………………………………………………………………….71

Figure 2-6. Similar odor-evoked power in the OT and OB………………………….....73

Figure 2-7. Spectral coherence of OT and OB LFP activity………………………...…75

Figure 2-8. Temporal dynamics of theta cycles in the OT and OB relative to respiration…………………………………………………………………………..…..77

Figure 2-9. Behavioral and anesthetic state impact the spontaneous LFP activity in the OT………….………………………………………………………………..……...79

Figure 2-10. Impact of anesthetic state on odor-evoked LFP in the OT….………...…81

Figure 3-1. Odor-directed attention dictates discrimination accuracy….………...…..115

Figure 3-2. Rats make fewer incongruent errors as they shift their attention, which leads to increased performance accuracy………………………………………..……117

Figure 3-3. OT units are bidirectionally modulated by odor-directed attention….….119

Figure 3-4. Odor-directed attention controls odor coding…………………….....…..121

Figure 3-5. Attention yields enhanced signal-to-noise among odor coding neurons…………………………………………………………………………….....123

Figure S3-1. Detailed structure of CAT shaping phases………………………….....125

Figure S3-2. Behavioral performance during task shaping……………………...... 127

Figure S3-3. Shifting to odor-directed attention is delayed when perceptual demand is enhanced…..…………………………………...…………………..……129

Figure S3-4. Selective attention influences subtle, yet critical aspects of olfactory behavior…………………………………….…………………..…….…..131

Figure S3-5. The majority of odor-unmodulated units have firing rate and signal-to-noise ratios that remain unchanged by attention…………………..….….133

Table S3-1. Total number of blocks and sessions to reach criterion across shaping phases 1-4…………………………………………………………..……..135

Table S3-2. Total number of blocks and sessions to reach criterion across multimodal and attention phases………………..…………………………..……..136

Table S3-3. Descriptive summary of single-neuron data used for analyses…...….137

Table S3-4. Body weights (bwts) during shaping and performance………..…….138

Figure 4-1. Future experiment: Task structure for elucidating neural effects of increased attentional demand……………………………………………...………159

Figure 4-2. The frequency of theta cycles increases during anticipatory hold and odor across task types………………………………….…..…………...…….161

Figure 4-3. Proposed experiment: Inhibiting cholinergic input from the HDB to the OT during performance of the CAT…………………………...…………..163

Figure 4-4. Example of a goal-directed unit that increases its FR with leftward movement toward the reward port……...……………………………….…..……165

Figure 4-5. Goal-directed units within the OT encode leftward movement…..…167

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Acknowledgments

I am deeply grateful to my advisor, Dr. Daniel Wesson, for his guidance, advice, and assistance throughout my doctoral studies. I was fortunate to have been able to train under his supervision and to have shared his enthusiasm for science. In the first few years,

Dan offered an enriching laboratory environment, wherein he eagerly provided me with all of the tools and training I needed to succeed in his lab. This hands-on instruction was crucial to my development as a scientist, and no doubt, allowed me to excel early on.

Perhaps most importantly, in the latter years that I was in the lab, after having established a strong foundation, Dan provided me with the room to grow and think creatively and independently. These problem-solving skills I will take with me for the rest of my life.

Without his continued support and guidance throughout the years, I know that the quality of my science would have suffered tremendously. Apart from being an excellent mentor in the lab, Dan also encouraged me to pursue educational opportunities beyond the university- required courses. These opportunities tremendously helped to shape me into the person I am today, both professionally and personally, for which I am deeply indebted. In addition to Dan, I am also grateful to my committee members Drs. Ben Strowbridge, Evan Deneris,

Brian McDermott, and Christopher Ford, for their questions, guidance, and expertise that they brought throughout my committee meetings and exams. I am also incredibly thankful for their support and encouragement.

This project would not have been possible without many members of the Wesson lab. I owe an inconceivable amount of gratitude to Dr. Marie Gadziola, who was not only a great scientific mentor but was also a beautiful friend. She tirelessly taught me how to code in numerous software programs, supplied the base of the programming code for the

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Carlson olfactory Attention Task (CAT), and showed me how to analyze the neural data.

Without her MATLAB knowledge, analyses of the data from the CAT would have been arduous. Her expertise and input on the attentional project were extremely valued. I will not forget all of those office chats about life (pretending to chat about data whenever we’d hear Dan’s footsteps), the time that we shared together outside of the lab, and the advice she gave. I admire her on many levels and strive to follow the mantra, “What would Marie do?” every day. Thank you also to Emma Dauster for her humor, persistent effort in handling the rats, and working through the early stages of the attentional project; Maggie

Dillione for the local field potential analyses, particularly of the olfactory bulb, and for sharing all those excel sheets and brownies; Christina Xia for her enthusiasm and work on the trigeminal project, and for pushing that manuscript to be published in the Journal of

Neuroscience; Kate White for her humor and reprieves from lab life to the beach and to see the manatees; Luke Stetzik for moral support, outlandish stories, and discussions on life, consciousness, free will, and lucid dreaming; and Adrienn Varga-Wesson for direction in my earliest days as a rotation student. I am further grateful to Tucker-Davis Technologies

(Mark Hanus and Chris Walters) for helping out with the code when I got stuck.

Finally, I would like to thank my family and friends, who have all, in their individual ways, contributed to the successful completion of this dissertation. My parents,

John and Donna Carlson, for their unending support in my quest for knowledge; my brother for always competing with me to do well, though, given his salary, I think he might have won; and my best friend and confidant, Samik Upadhaya, for unconditional love and support. Thank you, Samik, for the past decade of encouragement when I most needed it, particularly through the first few years of graduate school. I am also very thankful to my

childhood friends, Shany Briggs and Jessica Warren, for lending an ear to my complaints, helping me procrastinate, and always being there for me; Byron Van Nest for cultivating an honest and caring friendship that developed in the Neural Systems and Behavior course at the Marine Biological Laboratory in Woods Hole, MA; and my Spirit Guide and friend,

Justin Kurian, who humbly took the time to listen to me and make me laugh hysterically while I spent the last year of my studies in Gainesville. And last, but not least, my cat,

Sophie Victoria, for her constant inspiration, fodder for my jokes, and to which I justly dedicate the CAT.

State-Dependent Control of Neural Activity in the Olfactory Cortex

Abstract

KAITLIN S. CARLSON

The ability to survive, experience, navigate, and make critical decisions depends upon an organism’s capacity to receive and interpret vital information from its surroundings. Though an incredible amount of sensory information is available in the world, an organism is capable of adjusting this input, for example, by dampening it while resting or by selectively enhancing aspects relevant for goal-directed behaviors. Olfaction is a critical mediator of behaviors related to safety, socializing and eating, and contributes significantly to the aesthetics of our daily lives. Model organisms, such as rodents, utilize their olfactory systems to guide critical life decisions based on the detection of food odors, territory-marking, predator-prey relationships, and mating. Despite its relevance, how neural activity is sculpted by state in the olfactory system, particularly within the olfactory tubercle (OT), has been incredibly underexplored. Here, we utilize local field potential

(LFP) recordings to explore how the internal state of the rodent (awake, asleep, under anesthesia) modulates network activity within and across olfactory structures. We define how the olfactory tubercle (OT) and the upstream olfactory bulb (OB) have paralleled increases in odor-evoked LFP power and strong spectral coherence during awake states.

We show that sleep and anesthesia significantly decrease spontaneous activity across specific frequency bands within and across structures. In addition, to probe how the higher- order cognitive state of selective attention sculpts behavior and single neuron activity, we

develop a novel behavioral paradigm that allows us to determine for the first time in rodents that selective attention facilitates accurate odor-guided decisions and enhances neural activity during heightened odor anticipation. We also illustrate that selective attention enhances odor representation by increasing its contrast within single neurons in a signal- to-noise type coding scheme. Collectively, this research aids in filling a gap in our understanding that has existed for decades.

Chapter 1: Introduction

1.1 Sensory Systems

1.1.1 Why we need sensory systems

To survive, experience, and interact with the world, organisms have adapted sensory systems that allow for the transduction of environmental energy into electrical impulses. Life itself depends upon correctly assessing vital information from the environment such that an organism can then make an informed behavioral decision. For example, organisms have developed highly tuned sensory systems to avoid predation

(Ruxton, Sherratt and Speed, 2004). To successfully evade an impending predator's descent, a rodent's visual system detects a looming cue, while its auditory system detects small changes in air pressure (sound waves). After determining where the threat arises from, it can then make an informed decision to begin its escape route immediately. In contrast, many mammalian species communicate via olfactory signals in urine smears left behind for mate choice or territorial marking (Hurst and Beynon, 2004). Despite signaling within their species, these cues can also be used by the sensory systems of predators to guide them to prey (Hughes, Price and Banks, 2010).

How these different sensory systems function has been explored in the field of neuroscience for decades (Adrian, 1928, 1954; Pfaffmann, 1957). These basic sensory functions span and vary dramatically across life forms – from c. elegans, a lower-order organism with one of the simplest sensory systems, wherein 2/302 neurons are responsible for chemotaxis, to higher-order organisms such as humans, reported to be able to discriminate between more than one trillion olfactory stimuli (Bushdid et al., 2014).

Describing and measuring the perceptive capabilities of organisms to define how normal

perception occurs (gaining much insight from those lower order organisms with fewer neurons), allows us to understand how a perceptual loss may occur as a result of aging, injury, or disease, providing insight to the potential rescuing of such loss.

The five commonly defined sensory systems include vision, audition, somatosensation, gustation, and olfaction. However, sensory systems exist for nociception, thermoreception, proprioception, hygroreception, equilibrioception, kinesthesia, magnetoreception, and electroreception. Shaped by years of evolution and specific to each organism's needs and ecosystem, the world an organism experiences is based upon the physical properties of its sensory systems that takes in the information. Among the incredibly diverse and complex stimuli that exist in the environment, we are able to perceive only the aspects of the external world that are collected through these sensing capacities.

External stimuli interact with receptors (i.e., chemoreceptors, photoreceptors, mechanoreceptors, thermoreceptors, etc.) which transduce this sensory information to neural potentials. Action potentials are then sent along afferent neurons to areas of the brain (e.g., visual, auditory, olfactory cortices) where they can undergo neural processing.

Sensory systems encode stimulus aspects such as the modality, intensity of the stimulus, location of the stimulus, and the duration of the stimulus. They do so with rate, pattern, or magnitude differences in their firing (Victor and Purpura, 1997a, 1997b; Parker and

Newsome, 1998; Cohen and Newsome, 2009). Ultimately, these codes are thought to culminate in perception, the conscious sensory experience of the stimulus. However, perhaps even independent of conscious processing or the development of a ‘percept' of the environment, in simpler organisms, this information is meant to merely control navigation,

detect predators, and catch prey (Milner and Goodale, 1995). Once the brain has processed these stimuli, this often results in the production of a behavioral action, including goal- directed or motor activities, such as a change in the frequency of sniffing or locomoting through the environment.

Animals must continually sample their ever-evolving environment, taking in information and reacting to it. Rhythmic sampling, whereby the animal actively adjusts and controls the information that enters its brain, can occur, for example, in the form of visual saccades (Land, 1999) or via sniffing (Wachowiak, 2011). Indeed, as the needs of the organism and its environment are continually changing, it must reorganize its priorities to bring in sensory information dependent upon whether it needs to find resources, avoid predators, seek pleasurable activities, or rest. This organization of goal-directed priorities often takes the form of top-down modulation, whereby given the astounding amount of information available in the world, organisms can focus their attentional reserves to enhance certain aspects. This attention allows for the direction of their receptors to be physically positioned to receive incoming information and through decreasing the responsiveness of their other sensory systems which may not be as relevant in the moment.

How these top-down states provide filtering of the external world to influence information processing and perception has been investigated across many sensory systems, particularly those of vision and audition. Understanding how such top-down states influence sensory processing provides insights into how multimodal tasks can be influenced by sensory demands. Defining how this occurs allows for the application of methods to mitigate the effects that take away from the task at hand or how to enhance the reception of it. This becomes increasingly important as humans age in a world where

technology has provided an excess of sensory stimulation, very different from our ancestors. The olfactory system, critical for odor-guided behaviors, is not immune to such influences, though effects of state and cognitive modulation have indeed been less well- explored in the literature. To yield further fundamental insights into olfactory function, the research herein focuses on these influences of state (sleep, anesthesia) and cognitive modulation (attention) within the olfactory sensory system.

1.1.2 The mammalian olfactory system

Though humans often underappreciate the sense of smell, it is a critical mediator of behavior and is heavily relied upon by other organisms such as rodents. Humans use olfactory information to guide decisions based on safety and health, to avoid sources of contamination or disease, and for making decisions related to consumption (Engen, 1983;

Stevenson, 2009; Regenbogen et al., 2017). Olfaction contributes significantly to many aesthetic aspects of our daily lives. It is a mechanism for promoting affiliative behaviors, such that it even brings people together to eat. Rodents utilize their olfactory systems to guide decisions based on detection of food odors, territory-marking, predator-prey relationships, and mating (Arakawa et al., 2008; Hughes, Price and Banks, 2010; Ferrero et al., 2011; Petrulis, 2013; Li and Liberles, 2015). Olfaction is in fact extremely important in the lives of many species, even apart from mammals, and it is often their primary window by which they access the world (Ache, 1991).

In mammalian olfactory systems, transduction occurs as odorant chemicals flow through the nasal passages and across the turbinates, binding to olfactory receptor proteins

(ORPs). These ORPs are located on cilia at the ends of olfactory receptor neurons (ORNs)

across distributed regions of the nasal epithelium (Ressler, Sullivan and Buck, 1993; Mori,

Nagao and Yoshihara, 1999). ORPs are G-protein-coupled receptors (Buck and Axel,

1991; Firestein, 1991), and traverse the ORN membrane seven times. When the odorants reach the olfactory receptors, this triggers a cascade of events, leading ion channels within the membrane to open, resulting in depolarization and the generation of an action potential

(Firestein and Werblin, 1989). ORN axons extend their processes to the olfactory bulb

(OB), at which point they fan out, synapsing within small round structures termed glomeruli. Each glomerulus receives projections from one type of ORN (Buck, 1996).

From these spatially restricted glomeruli, odor information is then sent via mitral and tufted cells to be processed further downstream in cortical areas such as the piriform cortex (PCX) and olfactory tubercle (OT).

This pathway from odorant receptors to the cortex provides the basis for the transduction of an odorant into an electrical code. Much of this information processing has been well-characterized (Friedrich and Korsching, 1998; Haberly, 2001; Spors and

Grinvald, 2002; Laurent, 2002; Illig and Haberly, 2003; Neville and Haberly, 2004;

Wachowiak and Shipley, 2006; Wilson and Mainen, 2006; Johnson and Leon, 2007;

Rennaker et al., 2007; Kay et al., 2011; Payton, Wilson and Wesson, 2012; Carlson, Xia and Wesson, 2013; Xia, Adjei and Wesson, 2015), though many outstanding questions remain regarding how different states and cognitive processes influence odor processing.

The goal of this thesis is to provide evidence of modulation of olfactory cortex neural activity via state and how this compares to upstream structures, as well as define how such fundamental aspects of odor coding are changed with the cognitive state of the animal.

1.2 State-Dependent Influences on Sensory Processing

1.2.1 Arousal, sleep, anesthesia and the olfactory system

All of the incoming sensory information, as described above, is subject to modulation by the state of the animal in a highly dynamic fashion. For instance, an animal in a state of vigilant arousal may experience a heightened response to an environmental stimulus, though this same stimulus may fail to evoke sensory perception and a subsequent response in a sleeping or anesthetized animal. Why is this? Does the brain process this information differently and therefore lead to differences in the way the environment is experienced?

Arousal is defined as the physiological and psychological state of being awake, alert, and reactive to stimuli, ranging along a continuum from drowsiness to wakefulness (Duffy,

1957; Humphreys and Revelle, 1984). Organisms evolved arousal systems to capture novelty – changes in sound, light, vibration, etc., that alert the animal to orient in a manner whereby they are better able to focus on stimuli that are relevant in the moment. An increased heart rate and blood pressure, along with the activation of the reticular activating system, places the animal in a position of global sensory alertness. Such states of vigilance are critical for survival, finding and capturing prey, and mating.

Influences of arousal on the olfactory system have been studied for decades (Lavin,

Alcocer-Cuaron and Hernández-Peón, 1959; Yamamoto and Iwama, 1961; Sanbonmatsu and Kardes, 1988). A lone cat in an outdoor environment hears the sound of a prey species in the grass far away, driving it into a heightened state of arousal. Given that its processing resources are now directed towards obtaining this nutritive subject, the way a subsequent odor emanating from the mouse is perceived may likely be enhanced, along with the underlying neural correlates. How arousal influences sensory processing at the earliest

stage of processing in the OB was investigated by Lavin et al., in a 1959 study published in Science (Lavin, Alcocer-Cuaron and Hernández-Peón, 1959). The authors chronically implanted multipolar electrodes into the OBs of cats to record activity while they were awake and unrestrained, and presented them with a variety of stimuli to answer this question. They found that sensory stimulation, which produced alertness or arousal, resulted in bursts of rhythmic activity within the OB. Importantly, these responses were elicited by visual, auditory, somatosensory, and gustatory stimuli, suggesting that arousal was a global process.

Apart from arousal, which often facilitates behavioral responses in the awake state

(Kim, Lokey and Ling, 2017), animals also experience states of rest in which they sleep.

Sleep occurs naturally, decreasing the state of arousal of the animal, and modulates incoming sensory input. It is generated by centers with the brainstem, hypothalamus, and basal forebrain (Brown et al., 2012). Sleep states are known to modulate activity in olfactory cortical structures, such as the PCX (Murakami et al., 2005; Wilson and Yan,

2010; Barnes et al., 2011) and spontaneous activity within the OT (Narikiyo, Manabe and

Mori, 2014).

Further, differing from the natural state of sleep, anesthesia, is a drug-induced state which produces unconsciousness within a subject. Though its mechanisms are still not entirely understood, it impacts OB and PCX network activity and functional connectivity

(Fontanini, Spano and Bower, 2003; Fontanini and Bower, 2005; Murakami et al., 2005).

It is likely that the structural and spectral changes within the local field potentials arise through noradrenergic locus coeruleus input (Solano-Flores, Aguilar-Baturoni and

Guevara-Aguilar, 1980; Inokuchi et al., 1988) or serotonergic input from the raphe nucleus

(Hadley and Halliwell, 2010). How anesthetic state shapes spontaneous and odor-evoked

OT activity, and how its network activity changes in accordance with other olfactory structures and with respiration, is unknown. Furthermore, while many studies have utilized anesthetized preparations, much less has been explored in the awake state. Thus, one of the primary goals of Chapter 2 will be to define these changes and compare odor-evoked network activity in the OT between anesthetized and awake states within the same animal.

1.3 Cognitive influences on sensory systems

1.3.1 Historical context

The effects of cognitive influences, such as attention, expectation, and perceptual task on sensory processing, are well-known and have been studied for decades (Neuron, 2007

Gilbert and Sigman). Shortly after their study on influences of arousal in the OB (Lavin,

Alcocer-Cuaron and Hernández-Peón, 1959), as described above, Hernandez-Peon et al., investigated the influences of alerting stimuli on lower order sensory neurons across sensory pathways (Hernández-Peón et al., 1961). In this study, they recorded spontaneous and evoked activity from the OB, the trigeminal nucleus, and the dorsal cochlear nucleus.

They used clicks, electrical shocks, and the presentation of a mouse as stimuli. In an awake, relaxed state, the OB had consistent and uniform activity, and the auditory and tactile stimuli evoked potentials that were consistent in amplitude. When this awake cat was then presented with a mouse, and "attentively" looking at it, the authors found that the auditory and tactile stimuli elicited potentials that were markedly reduced, while the OB responses reached their maximal amplitude. Removing the mouse led to a subsequent return to baseline activity and evoked responses. Furthermore, the reduction of sensory-evoked

potentials unrelated to the mouse stimulus (i.e., tactile, auditory) had more variable evoked responses when the cat was alert, but not explicitly attending to the mouse. This study is arguably the earliest physiological evidence, albeit with minimal stimulus control, for how selective attention influences olfaction.

The earliest theories on the mechanisms of this sensory filtration began with Donald

Broadbent’s filter model for selective attention, in which he proposed a bottleneck through which unattended stimuli are blocked out (Broadbent, 1958). This theory was later built upon by Anne Treisman in her model on attenuation of sensory responses (Treisman,

1969). Broadbent postulated that sensory information from the periphery would reach the sensory organs, and then briefly remain in a sensory store. The brain, capable of handling only a limited number of stimuli at one time, would then have a mechanism in place that would act as a ‘filter,’ separating incoming sensory information to attended and unattended channels, guided through top-down attention, permitting only selectively attended information to be stored in working memory, after passing through higher level processing.

Treisman refined this model, theorizing that sensory information is processed via a similar filter mechanism, but that instead of being completely blocked out, it is attenuated.

Attenuation of the response would hence still allow it to pass through, albeit unconsciously.

Deutsch and Deutsch, and Norman offered a second filter that could reject information on the unattended channel before it reached working memory, that was also dependent on the strength of the stimuli (Deutsch and Deutsch, 1963; Norman, 1968). These theories have driven experimental approaches to how selective attention influences neural processing.

To date, no evidence for the modulation of olfactory cortex neurons by selective attention has been reported. The data that stem from Chapter 3 of this thesis elucidate how

the brain encodes olfactory sensory information dependent upon the animal’s attentional state.

1.3.2 Attention

Attention (and expectation) largely influence the way our brains perceive the world.

For example, expectations about the taste of a stimulus are heightened as one hears the sound of the coffee being poured into the cup and after the subsequent odor makes it ways to the nose, emphasizing the strong relationship between expectation and taste (Samuelsen,

Gardner and Fontanini, 2012). Such processes of expectation also involve aspects of attention (i.e., attending to specific aspects of that coffee because the sound directed our attention to it, may change the way we perceive its flavor), and can be difficult to disentangle. However, with carefully controlled stimulus delivery and perceptual tasks, we can begin to answer questions regarding how expectation or attentional state influence sensory processing.

Chapter 3 of this thesis focuses on attention, defined as the cognitive process that affords an organism the ability to prepare for and select relevant aspects of the environment by which it may utilize to guide its behavioral decisions. It allows for the organism to allocate processing resources to enhance its ability to respond. In The Principles of

Psychology (1950), William James defined attention as:

“The taking possession by the mind, in clear and vivid form, of one out of what may

seem several simultaneously possible objects of trains of thought, localization,

concentration, of consciousness are of its essence. It implies withdrawal from some

things in order to deal effectively with others…”

This apt description holds true even when considering how attention may influence neural responses and we can ask, what happens to neural function when attention “grabs hold of the brain?”

James writes further:

“Millions of items of the outward order are present to my senses which never properly

enter into my experience. Why? Because they have no interest for me. My experience

is what I agree to attend to. Only those times which I notice shape my mind – without

selective interest, experience is utter chaos. Interest alone gives accent and emphasis,

light and shade, background and foreground intelligible perspective, in a word. It

arises in every creature, but without it the consciousness of every creature would be

a gray chaotic indiscriminateness, impossible for us to even conceive.”

How relevant and meaningful attention is to life! These few showcased sentences bring into context the two ways that attention is driven – through bottom-up and top-down processing. When salient external stimuli capture attention rapidly, this is considered bottom-up processing. In this case, processing resources need to be directed immediately to detect and identify potentially dangerous situations (Baluch and Itti, 2011). Alternately, goal-directed attentional processes driven by top-down processing comes from previous

expectations and knowledge. Brain areas involved in cognition, memory, and motor planning allow incoming sensory information to be interpreted within the context of past experience and current goals related to task demands (Hopfinger, Buonocore and Mangun,

2000). Such effects of top-down attention are primarily thought to be controlled by the prefrontal cortex, and recently hippocampal-prefrontal synchrony (Tan et al., 2018).

Increased attention is thought to turn on top-down modulatory systems to enhance sensory processing (Lavin, Alcocer-Cuaron and Hernández-Peón, 1959; Linster and Cleland, 2002;

Wilson and Stevenson, 2006).

1.3.3 Modulation in vision and audition

In contrast to the commonly used anesthetized preparations, awake animals can be used to investigate the top-down modulatory control of the bottom-up sensory processing.

Attention can direct sensory receptors to better receive stimuli, influencing the way information is processed once the receptors are stimulated. The majority of the effects of attention on neural activity have been studied in the visual and auditory systems, though some studies have focused on chemosensory systems. Much of our knowledge regarding attentional modulation at the cellular-level stems from monkey, cat, and ferret studies in the visual (Oatman, 1971; Desimone and Duncan, 1995), auditory (Hubel et al., 1959;

Hocherman et al., 1976; Miller et al., 1980; Otazu et al., 2009), and somatosensory systems

(Spence and Gallace, 2007). Even some evidence exists for selective attention in the insect brain (Wiederman and O’Carroll, 2013; De Bivort and Van Swinderen, 2016). In these systems, attention has often been shown to modulate neural response magnitudes

(Reynolds and Chelazzi, 2004; Mitchell, Sundberg and Reynolds, 2007).

Some of the earliest experiments demonstrating the effects of attention on sensory processing were in the visual system of macaques. Several studies found that attention to a preferred stimulus within the receptive field of a neuron caused a strong response, while attention to a non-preferred stimulus caused a weaker response (Moran and Desimone,

1985; Mcadams and Maunsell, 2000). Similar to Broadbent's theory, they found that selective attention filters unattended information from the receptive fields of neurons

(Moran and Desimone, 1985). In many cases, attention increases the firing rates of sensory neurons when stimuli are attended (Assad, 2003; Yantis and Serences, 2003; Reynolds and

Chelazzi, 2004; Maunsell and Treue, 2006). These enhanced responses often improve the signal-to-noise ratio (sensory evoked response relative to background activity) of neural responses (Tolhurst, Movshon and Dean, 1983; McAdams and Maunsell, 1999). Efforts in the auditory system have shown both excitatory and inhibitory effects on neural activity, relative to attentional direction. In ferrets, primary auditory cortex displays facilitated responses with attention (Fritz et al., 2003). Higher-order auditory cortex displays enhanced responses to stimuli that are targets, above and beyond responses to stimuli that are irrelevant distractors (Atiani et al., 2014). However, when mice are engaged in an auditory task, tone-evoked responses in the auditory cortex are dampened (Otazu et al.,

2009).

Different aspects of these tasks can be modified to elucidate underlying neural effects that occur as a result of differences in attentional or perceptual demand. Creating a more difficult visual attention task with interleaved trials from an easier task, enhanced the discriminative abilities and neural responses, such that they became larger and more selective during the difficult task (Spitzer, Desimone and Moran, 1988a). Lowering the

contrast of visual stimuli created more significant differences between attended and unattended responses (Reynolds, Pasternak and Desimone, 2000). In many of these studies, the authors found that nearly half of the neurons they recorded were modulated by attention, with many unmodulated neurons showing limited effects of attentional information (Reynolds, Pasternak, Desimone, 2000).

More recently, efforts in the visual system have been given to understanding the reliability of neuronal representations, population variability, and oscillatory dynamics (i.e.

(Lakatos et al., 2016), given that signal-to-noise changes are often quite small and may not entirely account for the psychophysical benefits of attention. In 2009, Cohen and Maunsell found that attention decreases the correlated variability of neurons in V4 in a change- detection task (Cohen and Maunsell, 2009). The reduction of trial-to-trial variability of individual neurons with attention suggests that interactions between neurons are also very critical in understanding attentional influences on population coding. Further explorations have continued to gain insights on how neurons communicate across cortical areas within the system (Ruff and Cohen, 2016).

1.3.4 Modulation in chemosensory systems

Surprisingly little is known about the role of attention in the olfactory system despite its importance in guiding behavioral choices relevant to environmental navigation, food selection, and threat avoidance (Brennan, Kaba and Keverne, 1990; Youngentob et al.,

1991; Beauchamp and Yamazaki, 2003). What we do know arises from psychophysical studies in monkeys and humans (Spence et al., 2000, 2001; Cameron, Tai and Carrasco,

2002; Fan et al., 2002; Pestilli and Carrasco, 2005; Carrasco, 2006). Of particular note,

Charles Spence has spent decades investigating the attentional modulation of multisensory perception. He determined that there are shared attentional resources for the processing of visual and chemosensory information in humans (Spence et al., 2000) (see also Section

1.6.2). Attention to odors influences responses in other modalities as well, suggesting an overlap of these resources (Chen et al., 2013). For example, odors highlight visual saliency maps and modulate the attentional blink (Colzato et al., 2014).

Much of the physiological evidence related to attention in chemosensory systems stems from olfactory event-related potential (OERP) studies. With odor-directed attention, the early OERP component has a reduced latency (Krauel et al., 1998), while the later

OERP component has a greater amplitude (M. W. Geisler and Murphy, 2000; Morgan and

Murphy, 2010). Furthermore, two prominent human fMRI studies investigated the effects of selective attention on olfactory processing. The first showed that attention to odors enhances their responses in the olfactory cortex, both in the PCX an OT (Zelano et al.,

2005) and the second study described a strengthened interaction between the OFC and the posterior piriform cortex through the mediodorsal thalamus (Plailly et al., 2008). In a detection task, attention to taste activated primary taste cortex without influencing the olfactory cortex, and vice versa (Veldhuizen and Small, 2011). Despite these studies, there is virtually no information regarding how single units in the olfactory cortex respond under selectively directed attention, which is a significant aim of Chapter 3.

1.4 Network versus Single-Unit Activity

One can investigate how the brain's response changes relative to the external world, across behavioral states and under different cognitive influences, at a variety of levels. The

brain is never in a steady state, as its responses are determined by its ongoing and continuously changing functional connectivity, which is evident among oscillatory activity fluctuations that coordinate at micro- and macroscopic levels (Buzsaki, 2006). Over 75 years ago in the early 1930s, Bishop recorded activity in the rabbit optic tract (Bishop,

1932) and raised the proposition that “neuroelectric” oscillations reflect cyclical variations in neuronal excitability. Indeed, oscillatory fluctuations in the brain reflect synchronous membrane potential fluctuations of neuronal ensembles between depolarized and hyperpolarized states, which are thought to coordinate or synchronize operations within and across neuronal networks.

Local field potentials reflect neural network activity at the population level and have been frequently exploited to investigate sensory network function and the temporal relationships between interconnected neuronal groups (Freeman, 1975; Salinas and

Sejnowski, 2001; Fontanini and Bower, 2006; Gervais et al., 2007; Kay et al., 2009). The functional roles and their relation to the olfactory system have been investigated for decades (Adrian, 1942; Freeman and Viana Di Prisco, 1986; Gray and Skinner, 1988;

Gelperin and Tank, 1990; Chapuis et al., 2009; Kay et al., 2009). Different spectral bands of the LFPs (with subtle variations between studies) have been hypothesized to play different roles within a system (theta, beta, and gamma; discussed in Chapter 2). Sampling the high-frequency changes in these fields can reveal the single- and multi-unit spiking, of which the membrane currents of individual neurons underlie the fluctuations. Modern studies have combined single-unit recordings with LFPs to elucidate information on spike- timing relative to the oscillations. Both single-unit activity and network oscillations can be exploited to glean information related to sensory processing within and across systems, and

both will be used, separately – local field potentials in Chapter 2 and single-unit activity in Chapter 3.

1.5 Olfactory Cortex Anatomy

Once the olfactory bulb receives odor information, it is processed and refined, and then sent along the olfactory tract to higher-order brain structures, including the anterior olfactory nucleus, the piriform cortex, and the olfactory tubercle, bypassing the thalamus.

1.5.1 The olfactory tubercle

One part of the olfactory cortex is the olfactory tubercle, which is a tri-laminar structure located in the basal forebrain (Cleland and Linster, 2003; Wesson and Wilson,

2011). Monosynaptic input is received from the mitral and tufted cells of the OB (White,

1965; Haberly and Price, 1977; Scott, McBride and Schneider, 1980; Schwob and Price,

1984b; Nagayama et al., 2010). The majority of the mitral and tufted cells project into layer

I of the OT (Schwob and Price, 1978, 1984b; Scott, McBride and Schneider, 1980;

Nagayama et al., 2010), while the input to layers II and III of the OT arrives via association fibers in the PCX, the dorsal peduncular cortex, and the ventral tenia tecta (Luskin and

Price, 1983; Schwob and Price, 1984a). The OT also receives bisynaptic input from association fibers from the PCX (Luskin and Price, 1983; Johnson et al., 2000; Carriero et al., 2009). OT single-units display odor-evoked activity, respiratory coupling, and narrow breadths of tuning, similar to the PCX (Wesson and Wilson, 2010; Payton, Wilson and

Wesson, 2012).

There are several distinct types of cells within the OT, with the most common being medium-spiny neurons in layer II (Meredith, 1999). These cells project to the dorsal multiform layer (Millhouse and Heimer, 1984), the nucleus accumbens, and the caudate putamen (Fallon, 1983). They express either D1- or D2-type dopamine receptors (Gerfen et al., 1990; Zhang et al., 2017). GABAergic granule cells compose the Islands of Calleja

(Calleja, 1893; Millhouse, 1987) (see (Wesson and Wilson, 2011) for a review of other cell types). Acetylcholinergic interneurons also tile the OT but are quite sparse in number compared to the more common medium spiny neurons.

Interestingly, lesions of the OT affect the spontaneous and odor-evoked activity of mitral cells within the OB. Furthermore, odor presentation during sleep in lesioned rats shows increased cortical desynchronization, reflective that the OT may be involved in modulating state-dependent control over the OB (Gervais, 1979). The OT is situated within the ventral striatum, which is a network of brain regions that also includes the nucleus accumbens and the ventral pallidum (Heimer and Wilson, 1975), considered to be in a position important for the evaluation of information to be acted upon in the context of goal- driven behaviors. Interestingly, the ablation of cholinergic interneurons in the ventral striatum (though not including the OT) results in performance deficits in a set-shifting task

results in performance deficits in a set-shifting task (Aoki et al., 2015). While frontal cortex structures are linked, and in some cases, essential for different aspects of attention, it is unclear whether and if so, how, attention shapes the ventral striatum's encoding of behaviorally-relevant sensory information.

1.6 Studying attention in non-human animal models

1.6.1 Types of attention

Cleary attention is extremely valuable to an animal's perception of the world and is critical for guiding behavioral choices. There are, however, many types of attention that one could investigate empirically. Attentional orientation, bottom-up driven, is the direction of attention to a particular stimulus that captures attention, such as the call of a predatory hawk; sustained attention, occurs when a subject attends to one stimulus over an increasing duration of time, such as how a cat patiently holds its attention as it waits for a bird to land; spatial attention, directs attentional resources to an area within the stimulus space, such as one word on this paper amongst the paragraph. The most thoroughly studied type of attention, however, is selective attention (Stroop, 1935; Cherry, 1953). Selective attention provides the ability to select specific stimuli of importance from among multiple stimuli and is central to many behavioral functions (Raz and Buhle, 2006). These can be intermodal, across different stimulus modalities, or intramodal, within a single modality, wherein the subject directs attention to certain aspects of that stimulus. The term attention has been expressed loosely in the chemosensory community, and this is likely the case because there are many types of attention that one can semantically define. Arguably some form of attention or basal arousal is needed to complete any goal-defined task but distilling the effects of selective attention on specific neural responses has remained unresolved.

1.6.2 Psychophysics and Attention in Olfaction

Psychophysical studies relate an environmental stimulus to perception.

Quantitative methods for psychophysical techniques to measure this were introduced by

Gustav Fechner in 1860 (see (Boring, 1961)). Through careful and precise control of environmental stimuli, it is possible to associate neural responses with changes of activity in the brain. Across many sensory systems and in the areas of psychology and physiology, influences of selective attention have been explored (James, 1890; Broadbent, 1958;

Deutsch and Deutsch, 1963; Treisman, 1964; Norman, 1968; Yost, 1997; Spence et al.,

2000, 2001). The most well-known studies to probe psychophysical relations of selective attention in olfaction have been performed in humans (Spence et al., 2001). When attention is directed to olfaction (versus vision) in an intermodal attention task, participants respond more rapidly when determining if one odor is stronger than another. Other psychophysical tasks have been utilized in combination with ERP and fMRI in humans (Laing &

Glemarec D G, 1992; Geisler and Murphy, 2000; Spence et al., 2000; Plailly et al., 2008;

Veldhuizen and Small, 2011). The majority of these have been intermodal, (between vision and olfaction, audition and olfaction, or trigeminal and olfaction), and have used an

‘occasion setter' (i.e., a verbal instruction of what to attend to), on the upcoming trial.

Occasion setters, while recruiting attention, also likely incite expectation. Though intramodal selective attention tasks for rodents have been used to probe whether or not they can selectively attend to and detect components in a mixture (Takiguchi et al., 2008; Rokni et al., 2014), evidence for how odor-directed selective attention on an intermodal task regulates behavioral perception is unknown.

1.6.3 Techniques to Study Attention in Rodents

Why have the influences of selective attention on odors – both at the level of perception and odor processing been largely unexplored? One major technical

disadvantage is the lack of a precise psychophysical assay designed for rodents which allows one to test and neatly distill the effects of odor-directed selective attention. There is a long history of tasks for the study of olfaction and cognitive aspects in rodents, including odor preference (Carr, Krames and Costanzo, 1970; Pankevich, Baum and Cherry, 2004), habituation/dishabituation (Sundberg et al., 1982; McNamara et al., 2008), associative learning (Darling and Slotnick, 1994; Chapuis et al., 2007), odor detection via sand- digging (Sorwell, Wesson and Baum, 2008), and even very precise tasks to probe odor- guided behaviors (Friedrich, 2006; Mainen, 2006). Rodent attentional tasks also exist and have been utilized for decades. Such tasks include 5-choice serial reaction time tasks

(5CSRTT), attentional set-shifting tasks with odors, intramodal tasks to identify a component within a mixture, and even an intermodal attentional task to probe auditory attention that utilizes both tones and odors.

Designed by Robbins in the 1980s, the 5CSRTT probes visuospatial attention as an animal faces multiple ports (Robbins, 2002). One of the ports lights up for a specified length of time and the rodent reports which port it was. Shorter illumination requires greater attention. In attentional set-shifting tasks, rodents face two pots of sand, wherein they can dig for food rewards (Birrell and Brown, 2000). One of the pots contains the reward; which one it is depends upon experimenter-controlled contextual cues, such as the digging medium texture or odor. These tasks are incredibly useful in determining how pharmacological manipulations shape attentional behavior over extended time periods.

However, they do not allow for precise stimulus control, which would allow experiments to directly relate neurophysiological changes to the external environment and state of the animal.

Perhaps two of the most well-controlled attentional tasks for rodents that utilize odors are the intramodal cocktail party task, wherein rodents identify components in a mixture

(Rokni et al., 2014) and the intermodal two-alternative choice (2AC) task for auditory attention (Otazu et al., 2009), wherein rodents attend to and make decisions based on either tones or odors, when both are presented simultaneously. In the latter task, however, stimuli from both modalities are always present, such that multimodal effects are difficult to rule out. Therefore, despite these tasks designed for rodents, a task which allows the pairing of electrophysiological recordings with precisely defined stimulus control to distill processes of selective attention had not yet been developed. With this design, it would be difficult to determine the neural mechanisms that underlie any potential influences of selective attention on odor processing. In Chapter 3, we describe the design of an intermodal 2AC olfactory attention task to probe these exact questions.

1.7 Questions and Hypotheses: State Influences on Network and Neural Coding

1.7.1 State-dependent local field potential modulation

Chapter 2 of this thesis investigates the question of how states, like sleep and anesthesia, influence sensory processing and the communication between brain structures within the olfactory system. We also investigate how these changes relate to respiration in the rodent. The strategy to answer these questions was to simultaneously implant different regions of the olfactory system (OB and OT) and insert a thermocouple to measure respiratory transients within the nasal cavity, allowing us to determine how spontaneous and odor-evoked activity was modulated across state and between structures.

Given the effects of odors and sleep/anesthesia within regions of the olfactory system

(Murakami et al., 2005; Wilson, 2010; Wilson and Yan, 2010; Wilson et al., 2011), we hypothesized that odors would significantly enhance theta, beta, and gamma oscillations

(Fig 1-1A). Theta power increases would likely be reflective of the increased respiratory frequency with the delivery of an odor, while beta-evoked increases would reflect sensory processing, similar to other olfactory structures (Neville and Haberly, 2003; Lowry and

Kay, 2007; Kay et al., 2009). We expected an increase in gamma; however, this increase would likely be much greater in a more demanding cognitive task (Beshel, Kopell and Kay,

2007). We further predicted an increase in spectral coherence between the OB and OT with odor presentation (Fig 1-1B). Second, regarding the temporal dynamics of the oscillations, as OB theta lags behind respiration, we hypothesized that this would also be the case within the OT (Fig 1-1C). Given that OB sends inputs to the OT, we expected that theta peaks following respiration within the OT would occur at a greater latency. Third, we hypothesized that state would influence the spontaneous and odor-evoked activity. We predicted that sleep and anesthesia would result in a broad-spectrum power decrease (Fig

1-1D) and that this would also decrease the power of odor-evoked activity (Fig 1-1E).

Answers to these questions would provide the first characterization of odor-evoked

LFP activity within the OT of awake rats, determine how OT activity couples with upstream OB activity and respiration, and further illustrate how network oscillations change with the internal state of the animal and how this affects olfactory processing.

1.7.2 Attentional influences on behavior

The overarching question driving this section of work is: how does attentional state influence olfactory processing, perception, and ultimately, behavior? In the first half of

Chapter 3, we define how attention influences olfactory acuity. To do this, we developed an intermodal attentional task by which we could manipulate the animal’s selective attention to odors. We hypothesized that odor-directed attention would enhance olfactory acuity. In an intermodal attention task, we expect that as an animal shifts its attention from tones to odors, performance would initially decline, but improve as it shifts its attention to odors (Fig 2-1A). Second, we hypothesized that an increase in perceptual demand, by decreasing odor intensity, would result the need for a greater number of trials to reach high performance (Fig 1-2B). We further hypothesized that rats would make more errors on incongruent trials (Fig 1-2C) and that these incongruent trials would require greater sampling times in comparison to easier (congruent) trials (Fig 1-2D). We hypothesized that a greater attentional load (on the multimodal attentional task) would require increased sampling durations in comparison to the olfactory discrimination task alone (Fig 1-2E).

1.7.3 Attention-dependent changes in neural coding

No cellular-level investigations are available regarding the attentional modulation of odor coding in the olfactory cortex – nor anywhere else in the olfactory system for that matter. This represents a major void in our understanding of the fundamental principles whereby behavioral state shapes olfaction. In Chapter 3 we aimed to answer: what are the effects of selective attention on the encoding of odors in the olfactory cortex? Of particular importance, the intermodal task we designed is an organic manipulation of the animal’s

state, just as an optogenetic experiment would be a manipulation of the internal circuit. We hypothesized that odor-directed attention would enhance odor signal-to-noise ratios in the

OT.

There are many ways that selective attention could control single-unit activity. For example, similar to what Broadbent may expect, unattended odors could be filtered out with tone attention, recruited with enhanced firing rates (FRs) only while the stimulus is attended (Fig 1-2F). While odor-evoked FRs could be attenuated while rodents attend to tones, as Treisman would propose, it is also possible that they could be enhanced with odor attention (Fig 1-2G). Alternatively, the background of the FR could be attenuated with odor-directed attention, while the odor-evoked FR remains the same (Fig 1-2H). In all three of these cases, the signal to noise is enhanced. Finally, apart from signal to noise changes, the temporal dynamics of the neuron’s response could change, such that the neuron’s FR would occur earlier relative to stimulus onset (Fig 1-2I), perhaps reflective of the system gearing up early to respond to the stimulus.

In Chapter 3, we provide evidence supporting enhanced signal-to-noise ratios within the olfactory cortex, groundbreaking as the first data of its kind showing a cellular mechanism whereby attention influences odor processing. This research sets the stage for how selective attention may control odor perception to direct odor-guided behavior. It also lends credence to explore the question of how selective attention sculpts activity within the olfactory system in the absence of a thalamic relay. By establishing the task and validating our hypothesis, this research will facilitate further questions regarding the mechanisms of olfactory attention, which could be further teased apart via specific neural perturbations.

Altogether, this work addresses how state (sleep/anesthesia) controls spontaneous and odor-evoked activity within and across olfactory structures, develops a novel intermodal 2AC task to probe odor-guided selective attention, determines how odor- directed selective attention regulates odor-guided behavior, and defines how the cognitive state of selective attention sculpts neural activity among single neurons. We provide critical insights into how state and attention shape cortical network activity and the cortical coding of odor information in a manner that may be relevant to perception and behavior.

Figure 1-1

Figure 1-1. Hypotheses: Odor- and state-dependent modulation of local field potential activity within the OB and OT. (A) Beta and gamma power is enhanced with odor presentation, relative to a shuffled background of spontaneous activity. (B) The spectral coherence between the OB and OT is enhanced with odor presentation relative to its coherence during spontaneous activity. (C) OT theta peaks follow respiratory theta, but lag behind theta peaks of the OB. (D) Sleep and anesthesia decrease the power across bands within the OT. (E) Odor-evoked power within the OT is enhanced under anesthesia.

Figure 1-2

Figure 1-2. Hypotheses: Odor-directed selective attention modulates performance accuracy, shapes sampling durations, and sculpts the underlying odor coding. (A)

Performance declines as the experimenter-controlled switch to odor attention occurs, but increases as rats shift their attention. (B) Increased perceptual demand, by decreasing odor intensities, leads to a delay in attentional shifting. (C) Rats make more errors on incongruent trials than congruent trials and require (D) greater sampling durations to make these decisions. (E) Greater sampling times are required to make decisions on the more cognitively demanding 2AC intermodal task vs the less demanding 2AC odor discrimination task. Odor-directed selective attention could sculpt neural activity via (F) enhanced FR to odors with odor-directed attention, (G) facilitation of odor responses when attended, (H) a decrease in the background FR, or (I) a shift in the temporal dynamics of the odor response.

Chapter 2: Odor- and state-dependent olfactory tubercle local field potential dynamics in awake rats

2.1 Abstract

The olfactory tubercle (OT), a trilaminar structure located in the basal forebrain of mammals, is thought to play an important role in olfaction. While evidence has accumulated regarding the contributions of the OT to odor information processing, studies exploring the role of the OT in olfaction in awake animals remain unavailable. In the present study, we begin to address this void through multiday recordings of local field potential (LFP) activity within the OT of awake, freely exploring Long-Evans rats. We observed spontaneous OT LFP activity consisting of theta- (2-12 Hz), beta- (15-35 Hz) and gamma- (40-80 Hz) band activity, characteristic of previous reports of LFPs in other principle olfactory structures. Beta- and gamma-band powers were enhanced upon odor presentation. Simultaneous recordings of OT and upstream olfactory bulb (OB) LFPs revealed odor-evoked LFP power at statistically similar levels in both structures. Strong spectral coherence was observed between the OT and OB during both spontaneous and odor-evoked states. Furthermore, the OB theta rhythm more strongly cohered with the respiratory rhythm, and respiratory-coupled theta cycles in the OT occurred following theta cycles in the OB. Finally, we found that the animal's internal state modulated LFP activity in the OT. Together, these data provide initial insights into the network activity of the OT in the awake rat, including spontaneous rhythmicity, odor-evoked modulation, connectivity with upstream sensory input, and state-dependent modulation.

2.2 Introduction

The olfactory tubercle (OT) is a trilaminar structure located in the basal forebrain, where it is a part of the olfactory cortex (Cleland and Linster, 2003; Wesson and Wilson,

2011). The OT receives dense monosynaptic input from mitral and tufted cells in the olfactory bulb (OB) (White, 1965; Haberly and Price, 1977; Scott, McBride and Schneider,

1980; Schwob and Price, 1984b; Nagayama et al., 2010), as well as bisynaptic input from piriform cortex association fibers (Luskin and Price, 1983; Johnson et al., 2000; Carriero et al., 2009). Precisely, mitral and tufted cells project into layer I of the OT (Schwob and

Price, 1978, 1984a; Scott, McBride and Schneider, 1980; Nagayama et al., 2010), while the input to layers II and III originates from association fibers in the piriform cortex, dorsal peduncular cortex, and the ventral tenia tecta (Luskin and Price, 1983; Schwob and Price,

1984b).

The role of the OT in olfactory processing is presently unknown. However, it is becoming increasingly clear that the OT shares similarities in odor information processing with other principle olfactory structures. Of these, recent studies from our group have demonstrated that single units within the OT of anesthetized mice display odor-evoked activity in a similar manner as those within the piriform cortex, showing short latencies to odor-evoked spiking, respiratory coupling, and sometimes even narrow breadths of tuning

(Wesson and Wilson, 2010; Payton, Wilson and Wesson, 2012). Major questions still exist, however, including how odors are represented in the OTs of awake animals, and whether the OT is tightly coupled with odor output from the OB.

To investigate the function of this highly understudied structure in odor processing in the awake animal, we sought to examine spontaneous and odor-evoked local field

potentials (LFPs). LFPs reflect neural network activity at the population level and are commonly exploited to investigate sensory network function and the temporal relationships between interconnected neuronal groups (Freeman, 1975; Salinas and Sejnowski, 2001;

Fontanini and Bower, 2006; Gervais et al., 2007; Kay et al., 2009). The functional roles of

LFPs have been of great interest for decades (e.g., (Adrian, 1942; Freeman and Viana Di

Prisco, 1986; Gelperin and Tank, 1990; Ravel et al., 2003; Fletcher et al., 2005; Chapuis et al., 2009; Kay et al., 2009)). Olfactory system LFPs have historically been divided into three spectral bands (with subtle variations between studies), including theta- (2–12 Hz), beta- (15–35 Hz), and gamma-bands (40–80 Hz) (for review of olfactory system LFPs, see

(Kay et al., 2009)). These LFP bands are each hypothesized to play unique roles within the olfactory system. Theta oscillations are driven largely by intranasal sensory input and are often termed respiratory oscillations ((Komisaruk, 1970; Kay and Stopfer, 2006); for review see(Buonviso, Amat and Litaudon, 2006)). Beta oscillations are robustly evoked by odors (Vanderwolf and Zibrowski, 2001) and can last for two to four inhalation cycles

(Kay and Beshel, 2010). Beta oscillations are believed to reflect cooperative activity between interconnected structures(Kopell et al., 2000; Kay and Beshel, 2010), and, possibly related to this, they may play a role in odor learning (Ravel et al., 2003; Martin et al., 2004). The highest frequency band, gamma, is smaller in power than beta and especially theta oscillations (Bressler and Freeman, 1980) and originates near the end of an inhalation cycle (Rojas-Líbano and Kay, 2008). The functional role of gamma oscillations has been investigated in the OB and olfactory cortex since the first studies conducted by Adrian (Adrian, 1942). These oscillations are believed to stem from the reciprocal dendrodendritic synapse between granule and mitral and tufted cells in the OB

(Shepherd, 1972; Neville and Haberly, 2003). While growing literature is available regarding LFPs in both the OB and piriform cortex, much less is understood in other olfactory structures. In the present study, we sought to test specific questions regarding the nature of LFPs in the OT, including whether odor information shapes the structure of LFP activity in the OT, and if the OT LFP reflects similar aspects of information as do LFPs in the OB.

Odor information processing in the OT is also likely modulated by the internal state of the animal (Wesson and Wilson, 2011; Narikiyo, Manabe and Mori, 2014). This may occur by means of noradrenergic locus coeruleus input (Solano-Flores, Aguilar-Baturoni and Guevara-Aguilar, 1980; Inokuchi et al., 1988) and/or serotonergic input from the raphe nucleus (Hadley and Halliwell, 2010). Moreover, sleep-like states modulate activity in the olfactory cortex (Murakami et al., 2005; Wilson and Yan, 2010; Barnes et al., 2011), and sleep states shape spontaneous OT activity (Narikiyo, Manabe and Mori, 2014). In addition, anesthesia impacts OB and piriform cortex network activity and functional connectivity (Fontanini, Spano and Bower, 2003; Fontanini and Bower, 2005; Murakami et al., 2005; Wilson and Yan, 2010), suggesting that odor information processing within the OT is likely dependent upon the internal state of the animal (i.e., sleep, anesthesia, arousal), although this has yet to be investigated.

In the present study, we sought to characterize the odor-evoked LFP activity within the OTs of awake, freely exploring rats. We hypothesized that odor information in awake rats is represented by structural and spectral changes in LFP activity. Our results reveal robust theta-, beta-, and gamma-band activities within the OT, which become enhanced during odor inhalation. We further found that LFP activity in the OT coheres with that in

the OB. Finally, internal state significantly shaped spontaneous OT LFPs, but not odor- evoked LFPs. Our data provide the first characterization of odor-evoked LFP activity within the OT of awake rats and illustrate the likely importance of network oscillations and internal state to odor information processing in the OT.

2.3 Results

In the present study, we recorded the spontaneous and odor-evoked LFP activity in the OTs of awake rats using bipolar stainless-steel electrodes and radio telemetry. In this design, the majority of rats contributed to within subjects' measures of both spontaneous and odor-evoked activity.

Spontaneous LFP activity in the OT of awake rats.

For an initial description of LFP activity in the OT of awake rats, we recorded spontaneous activity during free exploration of the behavioral arena following a period of acclimation. We found OT LFP activity to consist of high-frequency (>15 Hz) activity riding on top of phasic slow-wave oscillations (<6 Hz) (Figure 2-3A). Comparison of OT

LFP with intranasal respiration reflects that the slow oscillations often coincide phasically with respiratory transients, as seen in other olfactory structures, including the OB (Kay and

Laurent, 1999; Cenier et al., 2009; Courtiol et al., 2011) and piriform cortex (Buonviso,

Amat and Litaudon, 2006; Litaudon, Garcia and Buonviso, 2008; Poo and Isaacson, 2009).

Furthermore, occasional bursts of both beta- and gamma-band oscillations appeared to correspond with respiration (Figure 2-3A). For an initial quantification of this LFP activity, we employed an FFT analysis across data from 11 rats with quality OT LFP

signals (see materials and methods) to quantify the ongoing spontaneous spectral power within the OT (Figure 2-3B). Theta-band activity was significantly greater within the OT than either beta- [F(1,20) = 15.278, P = 0.0009] or gamma-band activity [F(1,20) = 17.718,

P = 0.0004] (Figure 2-3B), with beta-band power largely dominating over that of gamma- band [F(1,20) = 13.254, P = 0.0016]. Furthermore, low gamma-band was significantly greater than high gamma-band activity [F(1,20) = 11.118, P = 0.0033] (data not shown).

Odor-evoked LFP activity in the awake OT

A hallmark of the olfactory system is the prominent increase in theta-, beta-, and gamma-band LFP activity upon odor inhalation (Adrian, 1950; Freeman, 1975; Laurent and Davidowitz, 1994; Buonviso et al., 2003; Kay et al., 2009). Therefore, we next addressed how LFP activity within the OT is shaped through odor stimulation by presenting odors to five rats freely exploring the behavioral arena (same day as spontaneous recording in Figure 2-3).

We found that odor stimulation evoked increases in OT LFP activity in both the beta- and gamma-bands. The example in Figure 2-4A displays OT LFP activity and respiration from an awake rat during presentation with the odor 2-heptanone. Following odor onset (and diffusion of the odor through the arena), the rat initiated high-frequency sniffing, reflecting identification of the stimulus (Figure 2-4A, arrow) (Wesson et al.,

2008). Soon after this sniffing response, the rat was observed orienting to the stimulus, indicated by the prominent increases in the accelerometer signal acquired from the head transmitter (“movement”; Figure 2-4A, bottom). Overall, the behavioral and LFP activity

was relatively conserved across rats, with subtle variability in the latency to sniffing and

LFP responses inherent in our presentation paradigm.

We calculated odor-evoked power ratios between a 2-s during-odor epoch and a 2- s preodor epoch (Figure 2-4B). This method accounts for minor variations in electrode placement and electrode impedance present in LFPs which can otherwise contribute variability to across-animal analyses (Kay and Beshel, 2010). In addition to calculating odor-evoked power ratios, we also computed power ratios of spontaneous LFP activity by pseudorandomly selecting consecutive 2-s epochs during the spontaneous activity. These

“shuffled” power ratios allow for direct statistical comparisons between odor-evoked and spontaneous (shuffled) LFP power. As predicted, the shuffled power ratios were typically values close to zero (Figure 2-4B), reflecting little fluctuations in ongoing spontaneous

LFP activity. Shuffled power ratios were most variable in the theta-band, displaying moderate fluctuations relative to zero (Figure 2-4B), possibly resultant from the impact of free exploration (sniffing, whisking, movement) on theta-band activity (Macrides,

Eichenbaum and Forbes, 1982; Vanderwolf, 1992; Kay, 2005). As suggested by the traces in Fig 2-4A, we found positive odor-evoked power ratios across all rats and all spectral bands (Figure 2-4B). Compared with the shuffled power ratios, we found a significant increase in odor-evoked activity in the beta- [F(1,8) = 33.033, P = 0.0004] and gamma- bands [F(1,8) = 12.159, P = 0.0082], and an increase in the theta-band, although this was not significant [F(1,8) = 5.292, P = 0.0504] (Figure 2-4B). Odor-evoked power ratios in the low and high gamma-bands were statistically similar [F(1,8) = 3.244, P = 0.1094].

These data demonstrate that OT LFP activity in awake rats, specifically that within the beta- and gamma-bands, is strongly modulated by odors.

LFP activity within the OT compared with the OB.

While a considerable amount of information is available on odor information processing in the OB and on the transmission into and coupling of this information with downstream structures (e.g., piriform cortex, entorhinal cortex, hippocampus) (Staubli, Le and Lynch, 1995; Chapuis et al., 2009, 2013), whether or not this occurs between the OB and the OT is unknown. We predicted that both the OT would share aspects of spontaneous activity with the OB (i.e., prominent theta rhythm), and spontaneous and odor-evoked LFP activity within the OT would mimic that within the OB. To test this, we implanted seven rats with bipolar electrodes in both the OB and OT, in addition to intranasal thermocouples to access respiratory transients (see materials and methods).

We first explored spontaneous LFP activity in both regions. Both the OT and OB displayed mostly similar timing of theta-band oscillations and bursts in beta- and gamma- band activity (Figure 2-5A). The overall power of these bands, however, did differ in a qualitative manner, especially within the theta-band. Furthermore, in some rare instances, the OT LFP theta rhythm deviated from respiration, whereas the OB did not. For example, in Figure 2-5A, the OB displays two large theta cycles (green arrows) coinciding with the timing of two discrete respiratory cycles. In contrast, the OT displays two additional theta cycles that do not correspond with obvious rhythmicity in either OB LFP or respiration

(Figure 2-5A, red arrows).

FFT analysis of this spontaneous data revealed that both the OT and OB display quantitatively similar distributions of power across all three spectral bands, albeit with subtle variations (Figure 2-5B). Although the OB displayed qualitatively greater power of

high theta (∼10 Hz) and low beta (∼20 Hz) spectra than the OT in some cases (Figure 2-

5 power spectrum), theta- [F(1,16) = 1.676, P = 0.214], beta- [F(1,16) = 0.0004, P = 0.986] and gamma-band power [F(1,16) = 0.427, P = 0.523] (Figure 2-5B, inset), both low

[F(1,16) = 0.400, P = 0.5359] and high [F(1,16) = 0.501, P = 0.4891], were not statistically different between regions.

Next, we examined odor-evoked LFP activity within both structures by presenting the rats with odors while they were freely exploring the behavioral arena. The example traces in Figure 2-6A display evoked activity in the OT and OB upon presentation with the odorant, 2-heptanone. While there are subtle differences in spontaneous theta-band power, both structures displayed noticeable increases in not only theta-, but also beta- and gamma-band power upon odor presentation (Figure 2-6A). Figure 2-6B shows the odor- evoked power ratios from the OB, along with the previously quantified OT odor-evoked power ratios (Figure 2-4B) from the same rats for comparison. In both the OT and OB, odor presentation elicited increases in odor-evoked power ratios across all three spectral bands compared with the shuffled power ratios. This was significant in both the beta- and gamma-bands in the OT [theta: F(1,8) = 5.292, P = 0.0504; beta: F(1,8) = 33.033, P =

0.0004; gamma: F(1,8) = 12.159, P = 0.008] with no difference in odor-evoked activity between low and high gamma-band activity [F(1,8) = 3.244, P = 0.1094], and across all bands in the OB [theta: F(1,8) = 6.724, P = 0.032; beta: F(1,8) = 13.178, P = 0.007; gamma:

F(1,8) = 22.754, P = 0.0014], with low gamma- dominating high gamma-band activity

[F(1,8) = 19.439, P = 0.0023]. Odor-evoked modulation was not statistically different between the OT and OB within each spectral band [theta: F(1,8) = 2.598, P = 0.146; beta:

F(1,8) = 0.838, P = 0.387; gamma: F(1,8) = 1.259, P = 0.294 (Figure 2-6B); low gamma:

F(1,8) = 1.019, P = 0.3423; high gamma: F(1,8) = 2.863, P = 0.1291]. These results suggest that odor-evoked activity in the OB is mostly well-maintained by the OT.

Does the similarity of odor-evoked activity between these monosynaptically coupled structures reflect functional coherence? To test this, we calculated the spectral coherence between both structures in the awake rats (same sessions as in Figure 2-6). Two second epochs were selected both before (“spontaneous”) and during odor [n = 5–9 epochs/rat, 7 ± 1.6 (mean ± SD)] and subjected to a spectral coherence analysis, where 1 indicates completely coherent waveforms and 0 indicates completely incoherent waveforms in their frequency content. We found that both spontaneous and odor-evoked epochs corresponded with functional coherence across all spectral bands (>0.6) (Figure 2-

7). No significant effects were observed within bands when comparing spontaneous to odor-evoked conditions, nor across bands within condition (odor vs spontaneous, P > 0.1,

ANOVA followed by Fisher's paired least-significant difference). Thus, in both spontaneous and odor-evoked conditions, the functional connectivity of the OT and OB aligns in a manner that would allow for the coupling of information during both spontaneous and odor processing modes.

The strong coherence found within the theta-band (Figure 2-7) suggests that OB and OT LFP couple on a cycle-to-cycle basis, perhaps relative to respiration. To test this, we analyzed theta cycle peaks in the OB and OT relative to the inhalation onset from five rats (>3K inhalation-triggered events/rat). As shown in Figure 2-8A from a single rat, and across all rats in Figure 2-8B, inhalation-triggered OT theta cycles lagged behind those observed in the OB. OT theta cycle onset was broadly distributed over time, possibly reflecting both dual arrival of bisynaptic input from the piriform cortex and/or OB mitral

and tufted cells (Ishikawa et al., 2007) and potential centrifugal influences. The latency of inhalation-triggered OT theta cycles was significantly slower than those in the OB [F(1,8)

= 7.385, P = 0.0264] (Figure 2-8C). Given the strong relationship between the OT and OB theta peaks and respiration, we also performed spectral coherence analysis between respiration (0–15 Hz) and OT and OB theta-band activity (Figure 2-8D). This analysis revealed, as suggested by Figures 2-5A and 2-8, that the OB theta rhythm differentially coheres with respiration compared with the OT theta rhythm (P = 0.0086, Kolmogorov-

Smirnov test), with the OB rhythm being more coherent [F(1,16) = 12.615, P = 0.0027]

(coherence across all frequency bins in OB vs. OT) (Figure 2-8D).

Impact of anesthetic- and behavioral-state on spontaneous OT LFP activity

Sensory processing, including that within the olfactory system, is highly state- dependent (Lavin, Alcocer-Cuaron and Hernandez-Peon, 1958; McDonald, Johnson and

Hord, 1964; Bouret and Sara, 2002; Fontanini and Katz, 2006, 2008; Li et al., 2012;

Wachowiak et al., 2013). Recent work in the OT has demonstrated state-dependent changes in LFP activity, where occasional sharp waves occur during spontaneous sleep states

(Narikiyo, Manabe and Mori, 2014). To further explore whether spontaneous activity is shaped by internal state, and to test whether odor-evoked activity in the OT is also influenced by internal state, we monitored OT and OB LFP activity during transitions in and out of sleep and also, separately, under urethane anesthesia. This design allowed within-subjects comparisons of awake, sleep-like, and anesthetic-induced states (see materials and methods). We hypothesized that the OT would display considerable state- dependent dynamics like those observed in the OB and piriform cortex (Murakami et al.,

2005; Wilson et al., 2011; Li et al., 2012; Narikiyo, Manabe and Mori, 2014). Particularly, we predicted that both urethane and sleep-like states would quantitatively impact the levels of spontaneous power across all LFP spectral bands and also odor-evoked power in the OT.

We found striking state-dependent differences in spontaneous LFP activity in the

OT and, as reported elsewhere, in the OB (Li et al., 2012) (Figure 2-9A). Prominent differences in theta-band structure were observed, likely resultant from differential patterns of respiration throughout these states (Figure 2-9A). Furthermore, in both sleep-like and urethane anesthesia states, we observed sharp-wave activity in the OT (Figure 2-9A, arrows). Since the prominence and role of sharp-wave activity in the OT has been reported elsewhere (Narikiyo, Manabe and Mori, 2014), we did not analyze its occurrence here.

To quantify possible state-dependent changes in OT LFP activity, we averaged two

200-s epochs of spontaneous activity from each rat (n = 6–11) in awake, sleep-like, and anesthetized states (Figure 2-9B). OT beta-band activity was significantly greater in the awake [F(1,20) = 10.193, P = 0.005] and sleep-like states [F(1,15) = 14.913, P = 0.002] compared with the anesthetized state. OT beta-band activity in the awake state was similar to that in the sleep-like state [F(1,15) = 1.596, P = 0.226]. Similarly, OT gamma-band activity was significantly greater in the awake [F(1,20) = 11.892, P = 0.003] and sleep-like states [F(1,15) = 14.610, P = 0.002] compared with the anesthetized state. Similar to that seen in beta power, OT gamma-band activity in the awake state did not differ from that in the sleep-like state [F(1,15) = 3.433, P = 0.084] (Figure 2-9B, left). OT theta-band activity was significantly greater in the sleep-like state [F(1,15) = 7.971, P = 0.013], but not in the awake state [F(1,20) = 4.278, P = 0.052] compared with the anesthetized state. Finally,

awake and sleep-like spontaneous state theta-band activity were similar [F(1,15) = 0.053,

P = 0.821] (Figure 2-9B, left).

In contrast to the OT, state-dependent effects in the OB were less frequently observed (Figure 2-9B, right). OB beta-band activity did not significantly differ across states [awake vs. urethane: F(1,12) = 2.877, P = 0.116; sleep-like vs. urethane: F(1,11) =

3.137, P = 0.104; awake vs. sleep-like: F(1,11) = 1.263, P = 0.285] and, similarly, OB gamma-band activity did not differ across states [awake vs. urethane: F(1,12) = 3.926, P =

0.071; sleep-like vs. urethane: F(1,11) = 2.100, P = 0.175; awake vs. sleep-like: F(1,11) =

1.275, P = 0.283] (Figure 2-9B, right). OB theta-band activity was, however, significantly greater than urethane in the awake state [F(1,12) = 5.140, P = 0.043], but, compared with the sleep-like state, it was not [F(1,11) = 0.762, P = 0.401]. Our findings confirm and extend previous observations of state-dependent influences on spontaneous OT LFP activity (Narikiyo, Manabe and Mori, 2014) and suggest a greater impact of internal state on OT dynamics relative to OB dynamics.

Impact of anesthesia on odor-evoked LFP activity.

Do these observed state-dependent changes in spontaneous OT LFP activity also impact odor-evoked activity? To test this, we directly compared odor-evoked power ratios in the awake state to those acquired under urethane anesthesia within the same recording session. Given that odor-evoked responses in the OB are prominently impacted by anesthesia (Adrian, 1950; Rinberg, Koulakov and Gelperin, 2006; Wachowiak et al.,

2013), and in some cases can be heightened (Rinberg, Koulakov and Gelperin, 2006), we

hypothesized that, in the anesthetized state, odor-evoked power ratios in the OT would be greater than when awake.

The example traces in Figure 2-10A are from a single rat both before and 20 min after injection with urethane. Most noticeably, urethane resulted in a slowing of respiratory frequency, which altered the temporal structure of odor-evoked activity in the OT, likely resultant from changed odor inhalation patterns. Nevertheless, between states, odor-evoked activity appeared qualitatively different (Figure 2-10A). To quantify anesthesia-dependent changes in odor-evoked LFP activity, we calculated odor-evoked power ratios (5 s pre vs.

5 s during, see materials and methods) from both conditions. Odor-evoked power ratios were similar between OT and OB during both the awake state (same awake-state data and stats as in Figure 2-6B) and the anesthetized state [theta: F(1,8) = 0.167, P = 0.694; beta:

F(1,8) = 0.002, P = 0.964; and gamma: F(1,8) = 0.178, P = 0.684] (Figure 2-10B). Across both structures and spectral bands, however, there was an overall effect of anesthesia on odor-evoked power ratios [F(1,28) = 12.275, P = 0.0009], which was similarly observed within the OT itself [F(1,28) = 4.69, P = 0.039] and also in the OB [F(1,28) = 7.434, P =

0.011]. Within OT spectral bands specifically, though, LFP odor-evoked power ratios were similar between awake and anesthesia conditions [theta: F(1,8) = 0.552, P = 0.479; beta

F(1,8) = 4.413, P = 0.069; and gamma: F(1,8) = 4.037, P = 0.079] (Figure 2-10B). The restricted effect of anesthesia on the power ratios was recapitulated in the OB, but, in this structure, a significant difference in the theta-band was observed [theta: F(1,8) = 8.767, P

= 0.018; beta: F(1,8) = 1.664, P = 0.233; gamma: F(1,8) = 3.411, P = 0.102]. Odor-evoked low and high gamma-band activity were not statistically different in the OT [F(1,8) = 0.122,

P = 0.7360] or the OB [F(1,8) = 0.146, P = 0.7125].

To overcome the prominent inter-animal variations and allow for a more clear assessment of the effect of anesthesia on odor-evoked activity, we also explored the change in each band's power at the individual rat level. In both the OT and OB, across all spectral bands, most rats displayed increases in odor-evoked power ratios under anesthesia compared with the awake condition (Figure 2-10C). In fact, only one rat (out of 5) displayed reduced odor-evoked activity while anesthetized. Finally, while odor-evoked power was qualitatively greater in the OB than the OT (Figure 2-10C), both regions again displayed statistically similar levels of change (%change, awake vs. urethane) in the theta

[F(1,8) = 1.486, P = 0.2576], beta [F(1,8) = 0.699, P = 0.4273], and gamma-bands [F(1,8)

= 1.236, P = 0.2986]. These results suggest that, at least within the context of this odor presentation paradigm, the representation of odors by LFP activity in the OT is largely independent of internal state.

2.4 Discussion

In the present study, we examined odor information processing in the OT of awake rats by means of LFP recordings. As part of this objective, we also determined whether

LFP responses in the OT coincide with those in the upstream OB and how these may be shaped by the internal state of the animal. Specifically, the present results provide novel evidence that the OT LFP in awake animals 1) represents odors with strikingly similar spectral powers compared with the OB, 2) is strongly coherent with the OB during both spontaneous and odor-evoked states, and 3) is shaped by the internal state of the animal.

Together, our results provide new insights into how spontaneous and odor-evoked LFP

activity is represented in the OT of awake animals, supporting and, in some cases, raising new hypotheses regarding the role of the OT in mammalian olfaction.

Odor processing in the OT of awake animals

We hypothesized that odor information in the OTs of awake rats is represented by changes in LFP spectral power. This straightforward hypothesis follows suit with known aspects of odor-evoked LFP activity in the OB and piriform cortex, with each displaying increases in spectral power during odor inhalation (e.g., (Freeman and Baird, 1987;

Buonviso et al., 2003; Cenier et al., 2008; Chapuis et al., 2009; Kay and Beshel, 2010)).

In addition, this hypothesis agrees with the prominent recruitment of neurons within the

OT upon odor presentation (Payton, Wilson and Wesson, 2012; Rampin, Bellier and

Maurin, 2012; Carlson, Xia and Wesson, 2013). Among the spectral bands that have received the most attention in terms of being shaped by odor and odor quality, are beta and gamma (Martin et al., 2006; Lowry and Kay, 2007; Martin, Beshel and Kay, 2007; Cenier et al., 2008; Lepousez and Lledo, 2013). We found that the OT is also characterized by considerable beta-band power and detectable, yet minor, gamma-band power.

Furthermore, we found that the power of these bands becomes significantly elevated during odor presentation, a finding that is in agreement with studies in other olfactory structures, including the OB, piriform cortex, and anterior olfactory nucleus (e.g., (Adrian, 1950;

Bressler and Freeman, 1980; Neville and Haberly, 2003; Beshel, Kopell and Kay, 2007;

Manabe and Mori, 2013)). While our analysis did not allow for a direct test of how these high-frequency oscillations are coupled with respiration, the timing of the beta- and gamma-band bursts suggests that these oscillations may on occasion coincide with

respiration, as seen elsewhere in the olfactory system (Roux et al., 2007; Cenier et al.,

2009; Manabe and Mori, 2013). Similar experiments in the future using wavelet-based LFP analysis (Roux et al., 2007) would be useful in testing this prediction.

An additional significance of our observations of both spontaneous and odor- evoked beta- and gamma-band oscillations resides in manners whereby these spectral bands may impact functional connectivity of information transfer. Based upon the hypothesized roles for beta-band oscillations in the olfactory network (Kay et al., 2009), our results confirm that the OT is involved in interregional processing of odor information in behaving animals, as suggested by previous anatomical and physiological studies (Scott,

McBride and Schneider, 1980; Schwob and Price, 1984a; McNamara, Cleland and Linster,

2004; Chiang and Strowbridge, 2007; Carriero et al., 2009). Whether LFP oscillations aid in the coupling of odor-evoked and/or behaviorally relevant spiking activity into downstream structures remains to be tested.

Based on our present results, we hypothesize that odors may be represented by

LFPs in the OT in terms of their identity, concentration, and timing (present or not). We also predict that the robust input of odor information into the OT, together with the OT's connectivity with numerous structures necessary for decision-making and motivation (for review, see (Wesson and Wilson, 2011)), may support the role of the OT as a critical node in a network linking odor information with higher-order emotional and cognitive aspects.

It will be informative in future work to determine whether the patterning and power of these oscillations are modified based on cognitive and perceptual demands, as they are in the OB (Beshel, Kopell and Kay, 2007). Data to support cognitive modulation of odor- evoked activity in the OT will be essential in assessing the behavioral-relevance of this

structure and further may provide insights into possible roles of OT neural dysfunction in neurological disorders associated with impaired ventral striatum function.

Similarities and differences of odor processing in the OT and the OB

How might OT LFP activity be reflecting upstream influences from initial odor information processing in the OB? To investigate this question, we recorded LFPs simultaneously in the OT and OB. While this method cannot account for, nor rule out, possible centrifugal influences on OT processing from those originating from within the

OB, several findings are worth discussing. First, we found that odor presentation elicited statistically similar increases in beta- and gamma-band powers in both structures. While the level of significance in the beta-band was greater within the OT vs. in the OB (P <

0.0001 vs. P < 0.01) (Figure 2-6), there were no differences when comparing between the regions. The strong spectral power similarities between the OT and OB raise the possibility that the OT LFP may indeed represent odors in a similar manner as the OB, at least at the network level. Second, the spectral coherence between the OB and OT was considerably strong during both spontaneous and odor-evoked epochs (Figure 2-7). This level of coherence is comparable to that reported previously between other primary olfactory structures (Kay and Beshel, 2010; Wilson and Yan, 2010; Wesson et al., 2011).

Importantly, however, some aspects of the LFP differed between the OT and OB in the awake animal. Specifically, our results demonstrate that the OB is more tightly coupled to the respiratory cycle than the OT. Whereas the bulk of the theta cycles in the OB corresponded with respiratory events immediately preceding them, the theta cycles in the

OT were characterized by greater diffusion relative to respiration, and a longer latency.

This could be interpreted as reflecting bisynaptic input into the OT from principle OB output neurons and/or input via piriform cortex association fibers which are known to functionally impact OT activity over distributed time scales (Carriero et al., 2009).

Alternatively, this could reflect differential centrifugal input to the OT (Solano-Flores,

Aguilar-Baturoni and Guevara-Aguilar, 1980; Inokuchi et al., 1988; Hadley and Halliwell,

2010), which may modulate the structure's intrinsic rhythmicity. Perhaps supporting this alternative, we observed occasional OT theta cycles which occurred independent of obviously corresponding theta cycles present in the OB and/or respiration (Figure 2-5).

State-dependent changes in OT network activity.

We further recorded OT activity in the rats while awake, in sleep-like states, and under urethane anesthesia. Sleep-like states have been shown to modulate several types of neural activity in the olfactory cortex (Murakami et al., 2005; Wilson and Yan, 2010;

Barnes et al., 2011; Manabe and Mori, 2013), and, relevant to this particular study, spontaneous OT activity is altered during sleep (Narikiyo, Manabe and Mori, 2014). The network activity in the OB and piriform cortex is also well known to be impacted by anesthesia, including urethane (Fontanini, Spano and Bower, 2003; Fontanini and Bower,

2005; Murakami et al., 2005; Wilson and Yan, 2010). Therefore, we allowed rats to fall asleep and used combinations of respiratory frequency, video-based measures

(immobility), and accelerometer signal to assay time points which we defined as “sleep- like.” It is important to note that we cannot concretely validate that the animals were in fact asleep without additional measures (e.g., neocortical EEG). Nevertheless, our data from this sleep-like state replicated specific previous findings of OT physiology in sleeping rats,

including the presence of sharp waves (Narikiyo, Manabe and Mori, 2014) (see Fig. 2).

Indeed, contributing to this growing body of literature on state-dependent influences on olfactory system function, we found that spontaneous OT activity is strongly shaped by state. The most prominently affected spontaneous activity was observed in the beta- and gamma-bands, with each modulated by both sleep-like and urethane states (Figure 2-9).

Thus our present results are in agreement with the hypothesis that gamma oscillations, modulated in their presence by the state of the animal, may be important for shaping odor processing (Manabe and Mori, 2013; Mori et al., 2013). In contrast, no significant changes were observed in the OB among these spectra under urethane, although all spectra showed decreases in power. Mostly in agreement with this, decreased OB beta- and gamma-band powers have been reported as a consequence of urethane (Li et al., 2012). Taken together, our results suggest that the OT might be more greatly affected by urethane anesthesia than the OB.

While urethane significantly affected spontaneous OT LFP activity, it did not significantly alter odor-evoked activity (Figure 2-10). Only odor-evoked OB theta rhythm was significantly affected by urethane; all other spectral bands showed only insignificant trends in increasing odor-evoked power during urethane anesthesia. It is interesting to consider how state can dramatically alter spontaneous OT activity while sparing major influences on odor-evoked activity. One reason that this may be the case is that odor- evoked changes in OT dynamics are proportionally small on top of the already dramatically impacted spontaneous LFP activity. Separately, as seen also during spontaneous activity

(Figure 2-9), OT sharp waves, which are believed to originate from the piriform cortex

(Narikiyo, Manabe and Mori, 2014), were observed during the course of odor presentation

while animals were under urethane anesthesia (Figure 2-10). Their quantitative investigation, however, was beyond the scope of this study. Whether these sharp waves more frequently occur during odor and/or impact the processing of odor information within the OT requires further investigation.

2.5 Conclusions

Together, these data provide initial insights into the network activity of the OT in the awake rat, including spontaneous rhythmicity, odor-evoked modulation, connectivity with upstream sensory input, and state-dependent modulation. We should note that our results do not show that the OT itself is necessary in odor processing or if it is just simply reading-out information from the OB. To answer this question, experiments specifically targeting OT activity need to be performed, along with accompanying behavioral assays for changes in olfactory processing. Nevertheless, our results describing odor information processing in the OT of awake animals, together with the established connectivity of the

OT with areas essential for motivation, emotion, and higher-order functions (Alheid and

Heimer, 1988; Ikemoto, 2007), add to the hypothesis that the OT may play a unique role in shaping and relaying odor information into behaviorally relevant downstream structures

(Wesson and Wilson, 2011). We anticipate, based on these observations, that these features of odor-evoked activity in the OT will be essential for odor-guided behaviors.

2.6 Methods

Surgical procedures and animal care

Long Evans female rats (n = 11, 2–4 mo of age, 210–250 g) were obtained from

Charles River Laboratories (Wilmington, MA) and maintained within the Case Western

Reserve University School of Medicine animal facility. Rats were housed on a 12:12-h

(light-dark) cycle with food and water available ad libitum. All experiments were performed during the light cycle and conducted in accordance with the guidelines of the

National Institutes of Health and approved by Case Western Reserve University

Institutional Animal Care and Use Committee.

Rats were anesthetized with isoflurane anesthesia (3.5–1% in 1.5 l/min O2) and mounted in a stereotaxic frame with a water-filled heating pad (38°C) positioned beneath to maintain body temperature. Anesthesia depth was verified by the absence of the toe- pinch reflex at which time rats were injected with Carprofen (Rimadyl, Pfizer Animal Care,

5 mg/kg sc). The scalp was shaved, cleaned with betadine and 70% ethanol, and an injection of lidocaine (0.1 ml of 1% in dH2O sc) was given in the surrounding scalp area prior to incision. A midline incision was then made from ∼4 mm posterior to the nose, until

∼2 mm posterior to lambda. The connective tissue was cleared away to expose the skull, which was subsequently cleaned with 3% H2O2, allowed to dry, and covered with Vetbond

(3M, St. Paul, MN).

Following focal craniotomies, each animal (n = 11) was implanted with a s/s bipolar electrode (0.005-in. outer diameter, Teflon coated to 0.007 in. outer diameter, A-M

Systems, Carlsborg, WA) into the OT (2.0 mm anterior bregma, 1.5 mm lateral, 8.5 mm ventral). A subset of these animals (n = 7) also received a bipolar electrode implant (same

material as above) into the ipsilateral OB (center of OB, 1.4 mm ventral) and/or a thermocouple (n = 5) into the contralateral dorsal nasal recess to access respiratory transients (catalog no. 5TC-TT-K-36-36, Omega, Stanford, CT) (Uchida and Mainen,

2003; Wesson, 2013). Thermocouples (0.9 mm lateral of the midline, on nasal fissure) were lowered to extend ∼0.3 mm into the nasal cavity and secured in place along with the electrodes using dental cement (Teets Cold Cure, Diamond Springs, CA). All rats were implanted with four 0–80 s/s screws into the skull for anchoring of the cement. A bare s/s wire (0.008 in., A.M. Systems) was connected to a contralateral skull screw to serve as a reference electrode. Finally, the entire assembly, which consisted of all leads presurgically soldered onto gold pins (A-M Systems) and inserted into a plastic female screw-plug adaptor (Emka Technologies, Falls Church, VA), was cemented onto the skull.

After surgery, rats were returned to their home cages and allowed to recover on a heating blanket overnight. All rats received daily injections of Carprofen for 4–5 days post-surgery and were singly housed for the remainder of the study.

Data acquisition

Recordings of OT and OB LFP activity, intranasal respiration, and movement were collected via a screw-on radio-frequency transmitter (13 g weight, Emka RodentPACK,

Falls Church, VA) secured to the head plug. Movement from an accelerometer, intrinsic to the transmitter, was also collected throughout all recordings. The movement signal was composed of integrated head movement along the x-, y-, and z-axes. LFP electrode activity was amplified and filtered (200-Hz cutoff) with respiration (20-Hz cutoff), and digitized/stored at 3 kHz, along with odor presentation events using a Tucker Davis

Technologies amplifier and software (Alachua, FL). In the awake state, respiration was measured via the intranasal thermocouple. In the anesthetized state, the thermocouple was not sensitive enough to differentiate respiratory transients that were only weakly present among the shallow respiration; instead, a piezo foil (Parallax, Rocklin, CA) was placed under the rat's chest to measure respiration (100× gain, 20-Hz cutoff). Video was used to supplement behavioral state recordings and was digitized via a high-resolution USB camera positioned above the behavioral arena (to be defined below).

Recordings

A summary timeline of the recording procedures is displayed in (Figure 2-1)

Animals were allotted at least 1 wk of surgical recovery time prior to experiments.

Following this, recordings were gathered over several days to allow assessment of spontaneous and odor-evoked LFP activity throughout changes in behavioral (awake, sleep-like) and anesthesia-induced states (urethane) within the same animal (Figure 2-1).

All behavioral procedures were performed in a dimly-lit, well-ventilated room maintained at 20–22°C, except when the animals were left to fall asleep, in which case, the lights were kept on to aid in video-based indication of sleep-like states.

Following surgical recovery, the rats were acclimated to the behavioral arena

(Figure 2-1A). The radio-frequency transmitter was screwed onto the head plug, and the animals were gently lowered into the glass testing arena (25 deep × 30 high × 30 wide; in cm) to freely explore for 20–30 min and to acclimate to the experimental conditions. For all further recording sessions, the rats were similarly acclimated for 5 min prior to the start of the data acquisition.

To acquire sleep-like state data, rats were left undisturbed and allowed to freely explore the behavioral arena for 3–4 h, providing sufficient time to reach a sleep-like state.

No odors were presented to animals in this session (Figure 2-1A). Awake spontaneous data was acquired on the same day odors were presented. Rats were allowed to explore the behavioral arena prior to odor presentation, with exploration consisting of both external

(rearing, sniffing) and self-directed (grooming) exploratory behaviors, as well as locomotion. Following 5 min of spontaneous data acquisition, odors were presented in recording sessions that typically lasted 15–20 min (see Stimulus delivery).

Within 10 min following the awake spontaneous and odor-evoked recording, all rats were immediately anesthetized with urethane (1.5 g/kg ip) and positioned on a water- filled heating pad (38°C) to maintain body temperature for recordings of spontaneous and odor-evoked LFPs under urethane anesthesia (Figure 2-1A). Five minutes of spontaneous activity was recorded before odor presentations. The odors were delivered in the same manner as in the awake paradigm (see Stimulus delivery).

Following anesthetized recordings, rats were overdosed with an additional injection of urethane (1.5 g/kg ip) and transcardially perfused with 4°C 0.9% NaCl, followed by

10% formalin (Fisher Scientific, Waltham, MA). After perfusion, brains were removed and stored in 30% sucrose formalin at 4°C until sectioning.

Stimulus delivery

Odors were presented through a custom air-dilution olfactometer with independent stimulus lines up to the point of entry into a Teflon odor port, to eliminate any potential cross-contamination. In addition to a blank stimulus (clean air, N2), novel odor stimuli

included isopentyl acetate, 2-heptanone, 1,7-octadiene, and ethyl butyrate (Sigma Aldrich,

St. Louis, MO). These molecularly diverse odors were diluted in their liquid state to 133.32

Pa (1 Torr) in mineral oil and were then further diluted to ∼10% (vol/vol) in the air-dilution olfactometer by mixing 206 ml odor vaporized N2 with 1,694 ml clean N2 (Medical grade;

Airgas, Independence, OH). Thus stimuli were delivered at a total flow rate of 1.9 l/min.

The same olfactometer and odor port were utilized throughout all experiments.

During awake recordings, the odor port was positioned at the top corner of the arena to provide optimal odor delivery, while still allowing space for rapid clearance. During recordings under anesthesia, the odor port was positioned ∼2 cm in front of the nose. In both states, 5 min of spontaneous activity were recorded, followed by novel stimulus presentation, which consisted of 5-s presentations of all four odors, presented pseudorandomly and counterbalanced, a total of four times each (n = 16 total trials) with an intertrial interval of 30 s. A mass flow exhaust vent was positioned ∼1 m over the behavioral arena to provide clearance of the odor prior to the next trial. The odor delivery paradigm in the awake state allowed odor presentation independent of training and/or learning and therefore was well-suited for an initial characterization of odor-evoked LFP activity in the OT.

Electrode placement verification

Electrode tip locations were verified with postmortem histological examinations, referencing a rat brain atlas (Paxinos and Watson, 1997). Coronal brain sections (40 µm) were mounted on gelatin-subbed slides and stained with 0.1% cresyl violet. OB recordings sites (n = 7, total of 7 rats) were found primarily in the granule cell layer (Figure 2-1B),

and OT recording sites (n = 11, total of 11 rats) were located mostly within layers I or II of the anterior aspect of the OT (Figure 2-1C).

Data analysis

Extraction and analysis of LFP data was performed in Spike2 (Cambridge

Electronic Design), similarly across all brain structures. LFP (0.1–100 Hz) and respiration

(0.1–50 Hz) were filtered using a second-order low-pass Butterworth filter. Using these filtered data, we performed several specific analyses as outlined below. Throughout all of these analyses, LFP spectral bands were defined as theta (2–12 Hz) (Leslie M Kay, 2003), beta (15–35 Hz) (Kay and Beshel, 2010), and gamma (40–80 Hz) (Cenier et al., 2009; Kay et al., 2009). We additionally analyzed for differences in both low (40–65 Hz) and high

(65–80 Hz) gamma-band activity (L M Kay, 2003).

For analysis of spontaneous activity (awake, sleep-like, anesthetized), two ∼200-s time epochs of fast Fourier transform (FFT) power spectra (1.34-s Hanning window, 0.75-

Hz resolution) were taken from each rat in each state and were averaged to obtain spontaneous state spectra. The epochs were taken from the freely exploring activity in the awake state (n = 11 for OT, n = 7 for OB), the sleep-like state (n = 6 for OT, n = 6 for OB), and before odor presentation when the animal was anesthetized (n = 11 for OT, n = 7 for

OB). Sleep-like states were defined by minimal accelerometer movement, which was confirmed by video analysis, in addition to prominent changes in LFP amplitude, including the occurrence of sharp waves which are present in slow-wave sleep in the OT (Narikiyo,

Manabe and Mori, 2014) (see Figure 2-2). To quantify sharp waves, full-band LFP was filtered to 2–20 Hz and events greater than 4 SDs below the mean were trough detected

(Narikiyo, Manabe and Mori, 2014). The average of the power spectra for each rat in each state was normalized to the minimum/maximum and finally averaged across all rats to obtain a single mean power spectrum for spontaneous awake, sleep, and anesthetized states in both the OB and OT.

For analysis of odor-evoked spectral activity (1.34-s Hanning window, 0.75-Hz resolution), prestimulus epochs were compared with during stimulus epochs. In the awake state, the animals moved freely within the arena as odors were presented, and thus the odors reached the rats at variable times, depending upon rat position and snout orientation.

Manual inspection of each rat's sniffing behavior indicated that novel odor orienting responses did not occur with a fixed latency following odor onset. Therefore, when investigating odor-evoked LFP modulation, upon visual inspection of traces, we selected

2-s epochs prior to odor-evoked activity to compare to 2-s epochs during odor-evoked activity. Time epochs usually included the last 2 s of odor presentation. For comparison of odor-evoked LFP modulation to non-odor-evoked modulations in spontaneous LFP, we selected consecutive 2-s epochs during spontaneous activity (prior to odor presentation), which we refer to as the “shuffle.” Any trial confounded by an artifact (grooming, head bumping into the arena wall or floor, etc.) was excluded from analysis. In the urethane- anesthetized rats, the stimulus epochs were selected based directly on the odor presentation and duration windows. The intervals chosen for comparison were preodor (−5 to 0 s) and during odor (0 to 5 s) epochs.

From these prestimulus and during stimulus epochs, odor-evoked magnitudes were calculated within each spectral band (theta, beta, and gamma) using established methods

(Kay and Beshel, 2010). For this, we normalized the power spectra during odor (2 s in the

awake state, 5 s in the anesthetized state) to the power spectra before odor (2 or 5 s, respectively). These values were used to calculate the band powers (theta, beta, gamma), which were then converted to decibel power ratios using the formula 10 × log10 (band power during odor/band power preodor) (Kay and Beshel, 2010). A value close to zero would indicate little change in odor-evoked band power, while a negative or positive value would indicate a decrease or increase in odor-evoked band power, respectively (Kay and

Beshel, 2010).

LFP coherence calculations (Chabaud et al., 1999) were created using the COHER open-ware script freely available at (http://www.ced.co.uk), as described previously

(Wilson and Yan, 2010; Wesson et al., 2011). For the COHER performance, the OB (for

OT-OB analysis) or respiration (for respiration-OB/OT analysis) was set as the reference signal.

Data from all rats with appropriate signals (i.e., confirmed electrode tracks, no artifacts present, consistent signal) contributed to our data analyses. Furthermore, data that contained artifacts within epochs of interest (odors) were omitted from analyses. In no other instances were any animals excluded from analysis. For all statistical comparisons, individual spectral band activity means or power ratio means across trials for each rat were averaged to obtain the final mean spectral band activity or final mean power ratio. Each individual rat thus contributed a single mean value in its respective band or power ratio for use in statistical comparisons. Residual degrees of freedom may vary considerably based on the number of animals being compared. All statistical analyses were performed in

Microsoft Excel, StatVIEW (SAS Institute, Cary, NC) or MATLAB (Mathworks,

Waltham, MA), and all data are reported as means ± SE unless noted otherwise.

Acknowledgments

This work was supported by National Science Foundation Grant IOS – 1121471 to

D. W. Wesson. We thank Dr. Marie Gadziola for comments on an earlier version of this manuscript. We would also like to acknowledge that this work was published in the article entitled, “Odor- and state-dependent olfactory tubercle local field potential dynamics in awake rats,” in the Journal of Neurophysiology in 2014 (Carlson, Dillione and Wesson,

2014).

Figure 2-1

Figure 2-1. Experimental timeline and electrode tip locations for olfactory tubercle

(OT) and olfactory bulb (OB) recordings. (A) rats were allowed one week to recover following surgical implantation of the electrodes. Following this, all rats were acclimated to the behavioral arena and the head-mounted wireless transmitter for physiological recordings (outlined in order following “data” subheading). Recordings of spontaneous and odor-evoked local field potential (LFP) data occurred during awake, sleep-like, and anesthetized (urethane) states. Rat coronal stereotaxic panels displaying the location of recording electrode tips in the OB (B) and OT (C), obtained after histological staining and verification (n = 11 in OT, and n = 7 in OB). [Adapted from (Paxinos and Watson, 1997)]

Figure 2-2

Figure 2-2. Physiological classification of sleep state for state-dependent analysis.

Example full-band OT LFP (0–100 Hz), sharp wave quantification, and movement trace from a single rat during free-exploration of the behavioral arena, including epochs identified as sleep. Movement from the head-mounted accelerometer displays an integrated head movement on the x-, y-, and z-axes. Data taken for analyses from this rat include two epochs of 200 s each (“sleep”). During these times, OT LFP amplitude and the rate of sharp waves increased (Narikiyo, Manabe and Mori, 2014), while accelerometer movement ceased. Sleep states were further confirmed by video.

Figure 2-3

Figure 2-3. OT LFP activity in awake rats. (A) Example respiratory and OT LFP traces from a rat during a period of free exploration. Shown are band-pass filtered traces of beta

(15–35 Hz) and gamma (40–80 Hz) activity, as well as full band LFP (0–100 Hz). A magnified trace from this data is also shown (right) to illustrate high-frequency activity

(>15 Hz) riding on top of phasic slow-wave oscillations (<6 Hz). Black bar indicates point of trace used in example. Respiration (50 Hz low-pass) was acquired from a nasal thermocouple; movement was acquired from head-mounted accelerometer. Vertical gray lines indicate respiration peaks (maximum inhalations). (B) Mean normalized fast Fourier transform (FFT) power spectrum showing the distribution of energy from 0 to 80 Hz across

11 rats, clipped at 0.6 power due to a large theta-band peak at ∼4 Hz. Inset: histogram (left) displays quantification of normalized power in the theta-, beta-, and gamma-bands. A histogram of gamma power on a finer scale (right) is included to illustrate the inclusion of gamma-band power in the OT LFP. Data were normalized to min/max with two ∼200-s epochs from each rat. ***P < 0.001, **P < 0.01, ANOVA followed by Fisher's paired least-significant difference (PLSD).

Figure 2-4

Figure 2-4. Odor-evoked modulation of OT LFP activity in awake rats. (A) example respiratory and OT LFP traces from a rat just prior to and during presentation with the odor,

2-heptanone. Shown are band-pass filtered traces of beta (15–35 Hz) and gamma (40–80

Hz) activity, as well as full band LFP (0–100 Hz) and respiration (50 Hz low-pass). Arrow indicates onset of odor-evoked sniffing. (B) mean odor-evoked power ratios across four odors (dark gray bars) compared with “shuffle” spontaneous power ratios (light gray bars, see Materials and Methods). Significant increases in beta- and gamma-band power were observed in response to odor compared with shuffled data. ***P < 0.001, **P < 0.01:

ANOVA followed by Fisher's PLSD. n.s., not significant.

Figure 2-5

Figure 2-5. Spontaneous LFP activity within the OT compared with the upstream

OB. (A) example respiratory and LFP traces of awake spontaneous activity in the OT and

OB. Traces are band-pass filtered for beta (15–35 Hz) and gamma (40–80 Hz) activity.

Full band LFP (0–100 Hz) and respiration (50 Hz low-pass) are also displayed. Green arrows indicate moments of theta cycles that cohere between the OT and OB and discrete respiratory cycles. Red arrows indicate theta cycles in the OT without obvious relationships to those in the OB or respiratory cycles. Gray vertical lines indicate respiration peaks. (B) mean FFT power spectrum and band power comparison (inset) for OT and OB. FFT averaged from two ∼200-s spontaneous epochs/rat in OB and OT. The power spectrum is clipped at 0.6 power due to a large theta-band peak at ∼4 Hz. ANOVA followed by Fisher's

PLSD.

Figure 2-6

Figure 2-6. Similar odor-evoked power in the OT and OB. (A) example respiratory and

LFP traces of awake odor-evoked activity in the OT and OB. Shown are band-pass filtered traces of beta (15–35 Hz) and gamma (40–80 Hz) activity, as well as full band LFP (0–100

Hz) and respiration (50 Hz low-pass). (B) mean odor-evoked power ratios across four odors (dark shaded bars) compared with “shuffle” spontaneous power ratios (light shaded bars, see materials and methods). n = 12–24 shuffle or odor epochs/rat. Significant increases in beta- and gamma-band power were observed in response to odors compared with shuffled data. No significant differences were found between OT and OB in any bands. *P < 0.05, **P < 0.01, ***P< 0.001: ANOVA followed by Fisher's PLSD.

Figure 2-7

Figure 2-7. Spectral coherence of OT and OB LFP activity. Mean spectral coherence of OT and OB waveforms, organized by LFP band, in both spontaneous and odor-evoked conditions (2-s epochs) is shown. 1 = most coherent, 0 = no coherence. n = 4 rats.

Figure 2-8

Figure 2-8. Temporal dynamics of theta cycles in the OT and OB relative to respiration. (A) raster plot of theta cycles which were peak detected in the OB (red) and

OT (blue) and plotted across and relative to the onsets of ∼5,000 inhalation cycles from a single recording in 1 rat. Averaged respiration waveform for the data in A is displayed on top of the raster plot. Vertical dashed line = moment of inhalation onset. (B) staircase plot and cumulative probability (dashed lined) of theta cycle peaks across 5 rats (>4,000 inhalation cycles/rat, 1 session each), including the rat used for A. These population-level data illustrate that theta cycle peaks appear in the OB prior to the OT relative to inhalation onset. Red and blue vertical arrows indicate moment of theta cycles which occurred prior to the aligned inhalation onset, likely driven by the preceding inhalation. (C) mean peak theta cycle latency relative to inhalation onset across all 5 rats (1 peak value/rat). As expected by the data in B and C, theta cycle peaks occurred first in the OB followed by the

OT. P value = ANOVA followed by Fisher's PLSD. (D) mean spectral coherence between respiration and OB (red) or OT (blue) theta rhythm across 5 rats (same data as in B and C)

(data filtered between 0.1 and 15 Hz prior to coherence analysis). OB theta rhythm displayed differential coherence with respiration than that in the OT. P value =

Kolmogorov-Smirnov test.

Figure 2-9

Figure 2-9. Behavioral and anesthetic state impact the spontaneous LFP activity in the OT. (A) example OT and OB traces of spontaneous LFP activity in awake (left), sleep- like (middle), and urethane-anesthetized (right) states. Shown are band-pass filtered traces of beta (15–35 Hz) and gamma (40–80 Hz) activity, as well as full band LFP (0–100 Hz) and respiration (50 Hz low-pass). Downward vertical arrows indicate occurrence of sharp- wave activity. Respiration in the sleep-like state possessed low-amplitude noisy transients from the nasal thermocouple due to minute air transients during sleep. Respiration during urethane was recorded with a chest piezo foil for this reason of difficulty detecting nasal respiration in non-actively respiring rats with the thermocouple. (B) mean FFT power spectrum and band power comparison (inset) for OT (left) and OB (right) organized by state. FFT was averaged from two 200-s spontaneous epochs/rat in OB and OT. The power spectrum is clipped at 0.6 power due to a large theta-band peak at ∼4 Hz. Different rat numbers contributed to each state, depending on signal quality. *P < 0.05, **P < 0.01:

ANOVA followed by Fisher's PLSD.

Figure 2-10

Figure 2-10. Impact of anesthetic state on odor-evoked LFP in the OT. (A) example

OT and OB traces of odor-evoked LFP activity in the awake (left) and urethane- anesthetized (right) states. Shown are band-pass filtered traces of beta (15–35 Hz) and gamma (40–80 Hz) activity, as well as full band LFP (0–100 Hz) and respiration (50 Hz low-pass). Black bar indicates duration of odor presentation. (B) histogram displaying overview of odor-evoked power ratio comparison for OT and OB in the awake and anesthetized states. In all cases, mean activity was greater in the anesthetized state than during the awake state, albeit not always significantly. *P < 0.05, ANOVA followed by

Fisher's PLSD. (C) line graph comparison of changes in theta-, beta-, and gamma-band activity in the OT (blue) and OB (red) from the awake state to the anesthetized state for individual rats (n = 5).

Chapter 3: Selective attention controls olfactory decisions and the neural encoding of odors

Abstract

Critical animal behaviors, especially among rodents, are guided by odors in remarkably well-coordinated manners, yet many extramodal sensory cues compete for cognitive resources in these ecological contexts. That rodents can engage in such odor- guided behaviors suggests that they can selectively attend to odors. Indeed, higher order cognitive processes, such as learning and memory, decision making and action selection, rely on the proper filtering of sensory cues based on their relative salience. We developed a behavioral paradigm to reveal that rats are capable of selectively attending to odors in the presence of competing extramodal stimuli. We found that this selective attention facilitates accurate odor-guided decisions, which become further strengthened with experience.

Further, we uncovered that selective attention to odors adaptively sharpens their representation among neurons in the olfactory tubercle, an olfactory cortex region of the ventral striatum considered integral for evaluating sensory information in the context of motivated behaviors. Odor-directed selective attention exerts influences during moments of heightened odor anticipation and enhances odorant representation by increasing stimulus contrast in a signal-to-noise type coding scheme. Together, these results reveal that rats engage selective attention to optimize olfactory outcomes. Further, our finding of attention- dependent coding in the olfactory tubercle challenges the notion that a thalamic relay is integral for the attentional control of sensory coding.

Introduction

A fundamental task for our sensory systems is to facilitate behavior towards salient stimuli. In countless cognitive functions, including learning and memory, decision making, and action selection, sensory information must be appropriately filtered in adaptive manners (Berridge and Aldridge, 2008; Griffith and Ejima, 2009; Ogawa et al., 2013). One of the mechanisms the brain employs to filter sensory information is through selective attention – attending towards one aspect of sensory information at the expense of another.

The effects of selective attention on behavior and sensory processing have been widely studied in the visual and auditory systems (Hubel et al., 1959; Hocherman et al., 1976;

Spitzer, Desimone and Moran, 1988a; Desimone and Duncan, 1995; Otazu et al., 2009), though much less is known about the role of attention in the olfactory system (Spence et al., 2001).

From neonatal attachment and suckling responses (Blass and Teicher, 1980; Logan et al., 2012), to selecting mates, finding food sources, and avoiding predators (Galef, 1985;

Isles et al., 2001; Ferrero et al., 2011), rodent behavior is guided by odors in remarkably well-coordinated manners. The fact that these behaviors can be successfully orchestrated lends reason to believe that rodents must be selectively attending to odors at the expense of competing extramodal cues. As a rat forages for food, it must simultaneously ‘filter’ out competing auditory and visual stimuli arising from irrelevant sources. Rodents readily display shifting of attentional sets, including those involving odors (Birrell and Brown,

2000), and can direct attention towards information from other modalities (Otazu et al.,

2009; Wimmer et al., 2015), but definitive evidence of selective attention regulating olfactory perception in rodents has yet to be produced and tested in a laboratory setting.

This is important given the prevalence of rodents as models for olfactory function and due to the powerful control of olfactory perception by attention in humans (Spence et al., 2001;

Zelano et al., 2005).

Given the aforementioned relevance of olfaction for survival, we reasoned that the olfactory system adaptively encodes odor information in manners dependent upon attentional demands, providing the brain with a mechanism to adjust the processing of incoming odor information based upon behavioral demands and context. We predicted that selective attention would shape the representation of odors within the ventral striatum’s olfactory tubercle (OT). This is likely given that the ventral striatum is important for evaluating sensory information in the context of motivated behaviors (Haber, 2011), a function integral for attention (Gottlieb, 2012). Offering precedence for this is evidence provided by human functional imaging for increased hemodynamic responses to odors in the ventral striatum during attention (Zelano et al., 2005; Plailly et al., 2008), particularly in the OT, a region extensively innervated by olfactory input (Schwob and Price, 1984a) that encodes odors in behaviorally-relevant manners (Gadziola et al., 2015). The coding strategy OT neurons engage in, which may underlie this attention-dependent phenomenon

(Zelano et al., 2005; Plailly et al., 2008), is unknown.

Defining the control of olfactory processing by selective attention has been hindered by the lack of a behavioral task that precisely manipulates intermodal odor- directed selective attention in rodents. Here we developed such a task, and combined it with single-unit recordings, to uncover fundamental principles of how rats utilize selective attention in manners advantageous for olfactory decision-making. Our results indicate that selective attention to odors facilitates engagement in accurate olfactory decisions and

enhances the contrast of odor representation in the OT by amplifying odor signal-to-noise ratios.

Results

We first sought to demonstrate that rats are capable of displaying selective attention to odors by developing a behavioral paradigm to probe intermodal selective attention. In this modified standard two-alternative choice (2AC) task, termed the Carlson olfactory

Attention Task (CAT) (Figure 3-1A&B), rats were shaped to nose-poke into a center port, receiving simultaneously presented tones and odors, and learned to attend to the modality

(auditory or olfactory) that signaled reward availability in either of two neighboring side ports.

The CAT was designed with several important features, distinguishing it from other available tasks and affords the ability to rigorously distill influences of intermodal selective attention on behavior and olfactory physiology. Main-stream attentional set-shifting tasks utilizing odors do not provide robust, controlled stimulus presentations nor do they allow for hundreds of trials, throughout which all conditioned stimuli are assigned equal valence, to be completed within a single session (e.g., (Birrell and Brown, 2000)). With the four possible trial types available in the CAT, odors may be either unattended or attended

(Figure 3-1B, ‘tone attention’ vs ‘odor attention’). Half of these trials do not include a tone

(Figure 3-1B, bottom half of trials), eliminating potential multisensory confounds (unlike the Wisconsin-Card Sorting Task (Grant and Berg, 1948) or (Otazu et al., 2009)), particularly important given that multisensory responses are observed in the OT (Wesson and Wilson, 2010; Varga and Wesson, 2013). The rewarded value, stimuli presented, and

sensorimotor aspects for each trial type are identical, with the only difference being the rat’s intermodally-directed selective attention. Rats also learned to perform both the single- modality 2AC odor discrimination and the more challenging multi-modal CAT in the same session, allowing for questions related to task demand to be addressed. Furthermore, as cue-related anticipation influences neural activity in chemosensory cortices (Samuelsen,

Gardner and Fontanini, 2012), anticipatory cues were not utilized before each trial or as

‘occasion setters’ at the beginning of the switch to indicate which modality should be attended. This allowed us to monitor the progress of the attentional shift within and across sessions, such that behavioral flexibility and odor coding relative to the attentional switch could be tracked.

Rats selectively attend to odors and this dictates discrimination accuracy.

We shaped 7 water-motivated rats to perform the CAT (see STAR methods for details). Over several phases of behavioral training, across blocks (20 trials/block) and sessions (1-2 hours), rats were shaped to criterion performance (≥85% correct responses/block) on 2AC tone detection (Figures S3-1A to D, S3-2) and odor discrimination tasks (Figures S3-1E&F, S3-2). Once achieving criterion on these single- modality tasks, cues from both modalities were presented simultaneously and rats were shaped to shift their selective attention from tones to odors in the final CAT (Figures 3-

1B, S3-1G&H). The first half of a session consisted of auditory attention blocks (‘tone attention,’ Figure S3-1H, left), wherein rats attended to the presence or absence of a tone, while odors were presented simultaneously. The tone and odor cues were either congruent

(non-competing, signaling the same reward-port side) or incongruent (competing,

signaling the opposing reward-port side). After reaching criterion performance (≥80% correct responses/block for ≥ 6 blocks), the task was switched to the ‘odor attention’ blocks

(Figure S3-1H, right) within the same session, and rats then had to direct their attention to the conditioned odors, ignoring the competing auditory information to which they had previously been attending. It took the rats 392.6±44.6 blocks, across 24.9±1.3 sessions, to reach the first criterion switch (Tables S3-1&2). To establish robust behavioral performance, rats were then over-trained on numerous successive sessions. Among the last four sessions of this over-training, they took an average of 10.5±0.8 and 9.7±0.5 blocks to reach session criterion (≥80% correct responses/block for ≥6 blocks) for the tone attention and odor attention tasks, respectively.

Several significant findings emerged from the rats’ CAT performance. First, we found that task accuracy is dependent upon the animal’s attentional strategy. Following shaping, rats performed the ‘tone attention’ task, despite the presence of competing conditioned odors, with an average of 85.48% correct responses per block (±1.14 SEM, inter-animal range: 82.92-91.25%). Directly after the task was changed from ‘tone attention’ to ‘odor attention,’ there was an immediate decrease in performance (t(6)=9.78, p<0.0001, block -1 vs block 1, early blocks; Figures 3-1C & 3-2A). The rats initially made perseverative errors on incongruent trials (t(6)=-10.87, p<0.0001; block -1 vs block 1, early blocks; Figure 3-2B, red dashed line), indicating that they maintained their strategy of continuing to attend to the tone. As they received error feedback (no reward for incorrect trials), the rats modified their strategy, switching their attention to odors, which consequently led to fewer incongruent errors (t(6)=4.5, p<0.01; block 1 vs block 6, early blocks; Figure 3-2B, red lines) and increased task accuracy (t(6)=-4.88, p=0.0028, block

1 vs block 6, early blocks; Figure 3-2A, bold line). Rats displayed an average of 89.7% correct responses for ‘odor attention’ (±1.63 SEM, inter-animal range: 85.42-96.67%).

Second, we observed that odor-directed selective attention is subject to plasticity with experience. Across sessions, rats improved their ability to shift their attention to odors

(Figures 3-1C & 3-2A, compare dashed vs. bold lines), with high levels of performance reached sooner in late sessions versus early sessions (t(6)=-2.74, p=0.034, block 6 (early) vs block 6 (late); Figure 3-2A). Incongruent errors decreased more rapidly across the shift in later sessions, indicating that rats shifted their attention following fewer incongruent error trials (t(6)=2.59, p=0.041, block 6 (early) vs block 6 (late), Figure 3-2B, red lines).

The number of congruent error trials remained relatively stable across the shift (t(6)=0, p=1.0, block -1 vs block 1, early blocks; t(6)=2.25, p=0.066, block 1 vs block 6, early blocks) and across sessions (t(6)=1.55, p=0.172, block 6 (early) vs block 6 (late)); Figure

3-2B, blue lines), suggesting that these trials were less informative in the attentional shift from tones to odors.

The rats took fewer blocks to reach criterion level (≥80% correct responses/block for ≥6 blocks) in late sessions, averaging 9.07 ±0.55 blocks (including the 6 blocks performed at ≥80%) in comparison to 11.71 ±1.06 blocks in early sessions (t(6)=3.34, p=0.016; Figure 3-2C). They reached their first high performance block (≥80% correct) within 3.36 ±0.51 blocks relative to the attentional shift (Figure 3-2D), demonstrating that they are capable of shifting their attention from tones to odors often following less than 30 informative incongruent trials, and that this shifting is enhanced with experience. We also tested a subset of rats for their abilities to direct selective attention to odors when perceptual demands were increased, given that there is interplay between attention, performance

accuracy, and perceptual difficulty in other sensory systems (e.g., (Spitzer, Desimone and

Moran, 1988a)). Rats required more blocks to shift their attention to odors of lower intensities (Figure S3-3). Together, these results demonstrate that rats can selectively attend to odors and that odor-directed attention improves with experience.

In agreement with the known influence of attention on dictating subtle, yet critical aspects of behavior (Kowler et al., 1995; Li, Barbot and Carrasco, 2016), we also uncovered that trial congruency and multisensory input impact CAT performance (Figure

S3-4). We hypothesized that the higher attentional load of the intermodal CAT would require more time to be invested sampling odors than the single-modality 2AC odor task.

We further hypothesized that attending to one cue in the presence of an incongruent competing cue would impinge on the rat’s rapid decision-making, and thus that rats would invest more time directed at stimulus sampling for incongruent versus congruent trials. To test these hypotheses, we analyzed two different behavioral decision epochs: sampling durations (length of odor sampling) and reward latencies (center port withdrawal to reward retrieval). The trial outcomes were ‘correct’ (correct reward port choice), ‘incorrect’

(incorrect reward port choice), and ‘omission’ (no reward port choice), made within 4s of the trial start. To prevent biasing of the data (sampling durations from incorrect trials or omissions could skew latencies), the behavioral analyses were held to strict criterion. Only correct trials were analyzed from ≥80% performance blocks and from sessions in which they successfully switched, reaching criterion (≥80% for ≥6 blocks) on all tasks. These measures were grouped and analyzed across the different task types (‘odor only,’ ‘tone attention,’ and ‘odor attention’), according to congruency, and were further divided among trial type (odor A + tone, odor A + no tone, etc.).

Across the three task types, the sampling and reward latency durations were not significantly different (Figure S3-4B&C), suggesting that, in the context of the CAT, task demand does not influence decision deliberations overall. Several aspects of odor-guided behavior beyond solely discrimination accuracy, were, however, influenced. First, while errors contributed to a small number of trials overall (0.19±0.07 congruent errors/block,

2.42±0.22 incongruent errors/block) once the rats reached criterion, they committed more incongruent error trials (t(6)=-8.00, p<0.001; Figure S3-4D). Proportionally, 91.76±3.18% of the errors were made on incongruent trials, significantly greater than the 8.24±3.18% of errors made on congruent trials (t(6)=-13.11, p<0.0001; S3-4E). The low error rate reflects that for the majority of trials, rats are indeed able to ignore the competing irrelevant modality, and perform within the range of that reported by other groups in odor only 2AC tasks (e.g., (Uchida and Mainen, 2003; Frederick et al., 2011, 2016)), but indicates that an incongruent stimulus may be more likely to capture the rat’s attention than a congruent stimulus, leading to more frequent lapses in attention to the opposing modality. Second, as predicted, among correct decision trials, rats invested more time sampling the stimulus if that trial was incongruent (33±8ms difference; t(6)=-4.20, p=0.0057; S3-4F); that is, they spent more time to make their decision when conflicting cues were present. This difference was small, however, in the context of the mean sampling duration which was ~500ms.

Despite these differences, there was no impact of trial congruency upon the latency to retrieve the reward, suggesting that animals did not deliberate upon their decision as they approached the reward port, nor were they less motivated to retrieve a reward (t(6)=-1.45, p=0.197; S3-4G).

Given that there was an effect of congruency on odor sampling durations, we further separated the data into the four trial types to determine if one was more greatly influenced by different combinations of sensory input. As multisensory input facilitates rapid decision-making (Miller, 1982; Sakata, Yamamori and Sakurai, 2004; Hirokawa et al., 2011), we hypothesized that rats would need less time to sample the stimulus when both cues were present (tone on + odor) and congruent. In accordance with this, we also hypothesized that rats would take longer to sample odors when one of the cues was absent

(tone off + odor) and the cues were incongruent. While sampling and reward latency durations were highly similar within an attentional task (i.e., comparing the four trial types to one another within either tone or odor attention), and across task types (i.e., comparing the ‘odor A + tone’ trial type between tone and odor attention), we did find two significant differences (Figure S3-4H). During odor attention, affirming that multisensory cues in the

CAT can facilitate rapid decision-making, rats sampled shorter for congruent trials in which the tone was on, as compared to incongruent trials in which the tone was off

(t(6)=6.05, p=0.0009, Bonferonni critical p=0.0083; S3-4H). Additionally, when the tone was off and the trial was incongruent, rats invested more time sampling the odor when they were attending to it versus when they were attending to the tone (51±15ms longer; t(6)=3.352, p=0.015; S3-4H). Importantly, the stimuli in these trials were identical (odor + tone off), but attending to the tone provided a sampling duration advantage, evidence that the rats were indeed attending to the correct modality. Altogether, while odor-directed selective attention controls performance accuracy and rats can learn to switch their attention to the relevant modality across sessions, there are additional influences of

enhanced cognitive demand, trial congruency, multisensory input, and attention, on these subtle, yet critical, aspects of sensory driven behaviors.

Attention controls the neural representation of odors.

The finding that selective attention is a prerequisite for accurate olfactory decisions indicates that attention may also sculpt the neural representation of an odor in a manner that facilitates perception. Does the brain represent an odor, of equal intensity and valence, differently dependent upon whether it is attended? To address this question, rats were unilaterally implanted with drivable tetrodes (Voigts et al., 2013) into their OT. We successfully performed OT single-unit recordings from 4 rats (Figure 3-3-3A, rats 1-4 in

Figure 3-1). Across multiple recording sessions/rat (range: 6-10), we lowered the tetrodes, and identified 232 cell-odor pairs (116 total single-units x 2 odors) (Table S3-3).

To directly test for attentional modulation of odor coding, we only analyzed ‘tone off’ trials (50% of trials) (Figure S3-1H, blue box). We identified four epochs relative to stimulus onset, to assess behaviorally-relevant changes in neuron firing: (1) background (-

1400 to -800ms), (2) stimulus port approach (-800 to -600ms), (3) preparatory hold (-600 to 0ms), and (4) odor stimulus duration (0 to 400ms). Across the entire population, during odor attention, 36 neurons were modulated by odor (32.03% of 116). We identified 55 odor-modulated cell-odor pairs out of the 232 possibilities (23.71%, 116 x 2 odors); n=27 odor-excited, n=28 odor-inhibited, n=177 unmodulated by odors, with some cells modulated by both odors (see STAR Methods).

We found that odor-directed selective attention bi-directionally sculpts the coding of odors in the OT by increasing the FRs of odor-excited cell-odor pairs (Figure 3-3B, 3-

4A), while further decreasing the FRs of odor-inhibited cell-odor pairs (Figure 3-3C, 3-

4B) during the preparatory hold and odor epochs. The normalized average FRs display enhanced contrast for odor-excited (S3-5A) and odor inhibited (S3-5B) cell-odor pairs, such that the average FR change with attention (ΔHzattention=FRattended-FRunattended) is significantly greater during the hold and odor epochs for both odor-excited (hold: t(26)=-

3.386, p=0.002; odor: t(26)=-3.459, p=0.002; Figure S3-5D) and odor-inhibited (hold: t(27)= 3.175, p=0.004; odor: t(27)=3.535, p=0.001; Figure S3-5E) cell-odor pairs.

Changes in FRs were not statistically different for unmodulated neurons (Figure S3-

5C&F).

To statistically represent significant FR changes for the cell-odor pairs, we classified the data using auROC analyses (Cohen et al., 2012; Gadziola et al., 2015) (see

STAR Methods), which represents changes in FR within sliding windows of time relative to a shuffled background distribution. Greater significance emerges during odor attention for both populations during the hold and odor epochs (Figure 3-4C&D). During odor attention, for odor-excited cell-odor pairs, a large proportion of the population was significantly and rapidly excited during the hold and odor epochs (Figure 3-4E). The duration of this excitement was significantly longer during both the preparatory hold and odor epochs when rats attended to odors versus when they attended to tones (hold: t(26)=-

3.20, p=0.0036; odor: t(26)=-3.51, p=0.0016), while 2AC odor only discrimination was not significantly different (hold: t(26)=-2.15, p=0.041; odor: t(26)=-2.37, p=0.026)

(Bonferonni critical p=0.0167; Figure 3-4F). Similarly, for odor-inhibited cell-odor pairs, a large proportion of the population was significantly and rapidly inhibited during the hold and odor epochs (Figure 3-4G). The duration that odor-inhibited cell-odor pairs were

significantly suppressed relative to background during both the preparatory hold and odor epochs was significantly increased during odor attention as compared to tone attention

(hold: t(27)=-3.79, p<0.001; odor: t(27)=-4.09, p<0.001) and odor only discrimination

(hold: t(27)=-3.87, p<0.0001; odor: t(27)=-4.67, p<0.0001) (Bonferonni critical p=0.0167;

Figure 3-4H).

The above results indicate that selective attention to odors bi-directionally controls both OT ensemble activity and the representation of odors. To define how individual neurons incorporate attentional demands into their representation of odors and the distribution of their changes, we used the cell-odor pairs classified above and calculated their individual changes in FR with attention (ΔHzattention=FRattended-FRunattended) to yield a simple index for the direction of change in firing. Neurons were classified, for each epoch, as shifted negatively or positively if their FR either increased or decreased ≥1Hz. Among those odor-excited cell-odor pairs whose FRs shifted (n=10/27 during background, n=17/27 during hold, n=20/27 during odor), we found that the majority decreased their background FRs (70%, 7/10), while increasing their FRs during the hold (70.6%, 12/17) and odor (60.0%, 12/20) epochs with odor-directed attention (Figure 3-5A). The proportion of odor-excited cell-odor pairs with decreased background FRs was greater than the proportion with increased background FRs (One sample proportion z=2.8, p<0.01), while the proportion of cell-odor pairs with increased FRs during the preparatory hold was greater than the proportion with decreased firings rates (z=3.7, p<0.001). The proportion of cell-odor pairs with increased FRs during odor did not reach significance (z=1.8, p=0.0679). Furthermore, of the 16 odor-excited neurons whose FRs were shifted positively during the hold or odor epochs, 4/16 (25%) were shifted positively during only the odor

epoch, 4/16 (25%) during hold only, and 8/25 (50%) were modulated during both the hold and odor epochs. Therefore, while the attentional effects on these odor-excited neurons were sometimes specific to the odor epoch, increases in FRs also frequently occurred in tandem and in a similar direction during the preparatory hold.

An opposite direction of change was observed among the odor-inhibited cell-odor pairs, where among those whose FR changed (n=13/28 during background, n=16/28 during hold, n=16/28 during odor), the majority decreased their firing during the hold (75.0%,

12/16) and odor (68.8%, 11/16) epochs while the rats were attending to odors (Figure 3-

5B). A greater proportion of odor-inhibited cell-odor pairs decreased their FRs during the preparatory hold (z=4.6, p<0.0001) and odor epochs (z=3.2, p=0.0012) with attention.

Furthermore, of the 17 odor-inhibited neurons whose FRs were shifted negatively during the hold or odor epochs, 5/17 (29.41%) were shifted negatively during only the odor epoch,

6/17 (35.29%) during hold only, and 6/17 (35.29%) during both the hold and odor epochs.

Similar to the odor-excited neurons, while the effects on the odor-inhibited neurons were sometimes specific to the odor epoch, decreases in FRs also frequently occurred in parallel with decreases during the preparatory hold epoch.

Notably, we determined that these effects were selective to odor-modulated cell- odor pairs, as the majority of FRs for those which were unmodulated were unchanged during background (87.01%, 154/177), hold (88.70%, 157/177), and odor epochs (87.57%,

155/177; Figure S3-5G). Among those unmodulated cell-odor pairs that were shifted

(11.30%, 20/177), we observed that a greater proportion displayed decreased firing during the preparatory hold (z=3.9, p< 0.0001). Overall, with odor-directed attention, odor- modulated cell-odor pairs display shifts in firing rates that occur within these task-critical

moments of preparation and odor stimulus sampling. For odor-excited cell-odor pairs, the

FR relative to background is shifted positively in preparation for the upcoming stimulus, while odor-inhibited cell-odor pair FRs are shifted negatively relative to background both in preparation for and during the odor stimulus.

It is possible that odor-directed attention controls individual neural FRs by broadly influencing the direction and magnitude of FRs across the background, hold, and odor epochs, indicative of a general ramping up or down of overall activity, and thus little enhancement of the odor signal relative to background. However, Figures 3-5A and B suggest that odor attention may be controlling the odor signal-to-noise ratios such that an odor-excited neuron’s FR is increased during the preparatory hold and odor epochs while background activity remains either unchanged or is decreased. In contrast, an odor- inhibited neuron’s FR during the preparatory hold and odor epochs may be further suppressed, while background activity remains either unchanged or is increased.

To address the above question and determine how these FRs are changing relative to these critical behavioral epochs for each neuron, in a final series of analyses, we compared the change in FR with attention (ΔHzattention) of the background to either the hold or odor epochs for both odor-excited and odor-inhibited cell-odor pairs (Figure 3-5C-F).

The unity line (dashed line) illustrates where changes in FR with attention would fall if they were similar in direction and magnitude across the epochs, which would support a general increase/decrease in neural activity within a trial, irrespective of epoch-specific influences. We found, however, for odor-excited cell-odor pairs, that the change in FR during both the preparatory hold and odor epochs was increased relative to the change in

FR of the background (hold: t(26)=-2.32, p=0.028, odor: t(26)=-2.54, p=0.017) Figure 3-

5C&D). In many cases, the background FR decreased, while the FR during the hold and odor epochs increased. 37.04% (10/27) of neurons had decreased background FRs, with increased FRs during the hold, while 33.33% (9/27) of neurons had decreased background

FRs with increased FRs during the odor. As also predicted, for odor-inhibited cell-odor pairs, the change in FR during the preparatory hold period was more greatly decreased relative to the change in background FR (hold: t(27)=3.227, p=0.003, odor: t(27)=1.93, p=0.064; Figure 3-5E&F).

Notably, these effects were specific to odor-modulated neurons. During odor attention, unmodulated neurons displayed FR changes that were similar in both their direction and magnitude (hold: t(176)=1.38, p=0.169, odor: t(176)=1.01, p=0.313; Figure

S3-5H). Odor-directed attention recruited more cell-odor pairs to encode the acts of the preparatory hold (odor attention: 21.12%, 49/232 vs tone attention: 13.36%, 31/232;

Figure 3-5G) and odor sampling (odor attention: 21.552%, 50/232 vs tone attention:

23.707%, 55/232; Figure 3-5H). Therefore, these results indicate that selective attention sculpts odor coding and the preparatory activity that occurs pre-stimulus arrival within these task-critical moments by enhancing the contrast of the signal-to-noise.

Discussion

Olfactory perception and processing are shaped by behavioral state in robust manners. For instance, sleep-like states and behavioral context, which may influence behavioral state, modulate activity among neurons in the olfactory system (Karpov, 1980;

Kay and Laurent, 1999; Murakami et al., 2005; Wilson, 2010). Here, we expanded upon these reports, and human brain imaging results (Zelano et al., 2005), which described the

modulation of olfactory cortex activity by selective attention, in order to define the cellular strategies underlying attention-dependent odor perception.

We demonstrated that rats are capable of selectively attending to odors in the presence of conflicting stimuli. We predict this executive capacity affords rodents the ability to engage in ecologically critical behaviors (e.g., foraging, predator avoidance, mate selection), which are highly multisensory contexts requiring animals to focus at times upon a single modality at the expense of others. Not only does our work show that selective attention enhances odor discrimination capacity, but also that this ability improves with experience. This result highlights an important interplay between attention, olfactory processing, and learning, and indicates that rodents develop a strategy to selectively attend to odors.

Equally important is our finding that selective attention contributes to olfactory processing by enhancing the contrast of odor representation in the OT through amplification of odor signal-to-noise ratios. We observed the sculpting of relevant odor responses in both directions – increased as well as further suppressed responses. Limiting the neurophysiological analyses to odor trials in the absence of tones reflects the influence of selective attention alone, independent of multisensory processes known to occur in the olfactory cortex (Wesson and Wilson, 2010; Maier, Wachowiak and Katz, 2012). While in other sensory systems selective attention may suppress stimulus-evoked activity in an engaged task condition (Otazu et al., 2009) as well as non-optimal stimuli (Reynolds,

Chelazzi and Desimone, 1999; Treue and Martinez Trujillo, 1999), our findings add to standing literature that it often enhances sensory responses among neurons (e.g., (Hubel et al., 1959; Hillyard et al., 1973; Spitzer, Desimone and Moran, 1988a; Desimone and

Duncan, 1995; Kastner and Ungerleider, 2000; Maunsell and Cook, 2002)). It is also more likely that odor attention enhanced the odor responses (vs suppression by tone attention), given that odor-evoked responses during the odor only task were similar to those of tone attention. These results add to a growing body of literature on state-dependent cellular coding in chemosensory systems (e.g., (Kay and Laurent, 1999; Fontanini and Katz, 2005;

Samuelsen, Gardner and Fontanini, 2012)) and highlight that even a primitive sensory system found in a rodent incorporates executive functions to facilitate behavior. How this occurs in a sensory system lacking the canonical thalamic relay through which peripheral sensory information passes before reaching neocortical areas (Carmichael, Clugnet and

Price, 1994; Kay and Sherman, 2006; Gottfried, 2010) is yet to be determined. In no other instances have olfactory cortex neurons been reported to be modulated by attention.

Given that the OT is strongly interconnected with motivational brain systems, it is possible that an enhancement of these responses within this structure plays a particular role in guiding behavioral decisions. This, together with possible attentional modulation in other olfactory structures, is likely responsible for the effect of selective attention on facilitating accurate odor discriminations. Selective attention may thus ‘filter’ available odor information into the entirety of down-stream structures important for emotion, motivation, and memory.

The CAT is not an all-encompassing behavioral task. First, it does not allow for the investigation of intra-modal attention in which subjects attend to stimulus attributes (i.e. spatial location or target in a mixture), like many visual studies (e.g. (Engel et al., 2016)), and one olfactory study (see (Rokni et al., 2014)). Second, while an odor on/off design would have matched the tone on/off structure, it would have resulted in half the number of

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trials and cell-odor pairs to analyze. Despite this asymmetry, similar behavioral performance and comparisons of the same trial types should still show any effects on neural activity that are directly related to the attentional state of the rat, and we predict that an odor on/off structure would produce similar effects. Third, we investigated FR differences with attention for high performance blocks, but there are low performance blocks as the rat shifts its attention across a session. These data support the notion that the FRs of neurons may be sculpted across an attentional shift, but the low FRs of OT neurons made a quantitatively significant analysis of these changes difficult. Furthermore, it is unclear if the firing rate changes we observed are causal in odor-directed attentional behavior or for the shift to occur. It is possible that they are not necessary for the shift, but instead may reflect an enhancement of sensory representation that occurs after a shift has been made.

While an animal is engaged in a dynamic sensory task one cannot rule out influences of sensorimotor demands upon sensory processing. That said, our findings that rats sampled odors similarly whether they were attending to odor or not, and the influence of selective attention enhancing odor representation in the attention task going beyond those responses in the odor only task, indicate that these effects surpass those of sensorimotor influences alone (e.g., go left or right). Finally, the CAT was designed with a preparatory hold epoch. Initially, this was to be allocated for pre-stimulus ‘background’ comparisons, but many of the odor-modulated neurons were also modulated during this time. It is known that neurons encode anticipation of port entry and task engagement

(Samuelsen, Gardner and Fontanini, 2012, 2013; Nieh et al., 2015), but beyond this, we found influences of selective attention, suggesting that the impact of selective attention is not restricted to the overt stimulus period. The influence of behavioral state on the encoding

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of sensory information does not mandate itself to occur within solely the moment the stimulus is engaged by the brain, but may occur even while one prepares for stimulus arrival. Indeed, increased activity with odor-directed attention has also been observed in the OTs of humans during the epoch preceding stimulus delivery, when subjects were given verbal instructions to attend to an odor (Zelano et al., 2005). This may be a preparatory phenomenon related to attentional direction (‘I’m anticipating an odor’) that aides in guiding olfactory goal-directed behaviors.

Taken together, a rodent, just like a human (Spence et al., 2001; Zelano et al.,

2005), can employ selective attention to aid in olfactory perceptual goals and this attention enhances the representation of odor information within part of a brain system that is integral for evaluating sensory information in the context of evolving motivational demands. Our results put forward a model whereby an attention-dependent signal-to-noise coding strategy facilitates odor perception.

Methods

Animals. Adult, male Long-Evans rats (n = 7, 2-3 months of age, 250-350g) were obtained from Charles River Laboratories (Wilmington, MA) and maintained within the Case

Western Reserve University School of Medicine vivarium. Rats were housed on a 12:12- hour (light:dark) cycle with food and water available ad libitum, until behavioral shaping or electrophysiology experiments. Experiments were performed during the light cycle and conducted in accordance with the guidelines of the National Institutes of Health and approved by both the Case Western Reserve University and the University of Florida

Institutional Animal Care and Use Committee.

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Olfactory and auditory stimuli. The odor pairs used were: 1) isopentyl acetate vs limonene

(-) and 2) 2-butanone vs 1,7- octadiene. Molecularly diverse, monomolecular hydrocarbons were selected to generate coarse (perceptually easy) odor pairs to discriminate. Unless otherwise noted, odors were diluted in mineral oil to equal vapor pressure (0.5 Torr, 66.67

Pa; Sigma Aldrich, St. Louis, MO) and presented through a custom air-dilution olfactometer (2.0 L/min) with independent stimulus lines up to point of entry into a custom

3D-printed [polylactic acid (PLA)] nose-poke port. The photoionization detector (miniPID,

Aurora Scientific) trace in Figure S3-4A confirms the precision and stability of the odor presentation and clearance temporal dynamics in our apparatus. The auditory stimulus, positioned immediately above the behavioral apparatus, 60cm above the rat, consisted of a

2.8kHz tone (76dB, piezo speaker, RadioShack). Stimuli were presented pseudorandomly

(random, but organized in such a manner that an equivalent number of the four trial types were given across a block) and counterbalanced, such that all four trial types (Figure 3-

1B) were equally possible in each block (20 trials/block).

CAT behavioral shaping. Rats were mildly water-restricted for several days before shaping.

Over the course of training (1-2 months), though water-restricted, the rats did not display any signs of illness or distress, sustained task performance motivation, and even gained weight (Table S3-4), without exceeding a ≥ 15% drop body weight. Each shaping phase of the CAT will be detailed herein and the corresponding number of blocks and sessions to reach criterion can be found in Supplemental Tables S3-1, S3-2. In a dimly lit, well- ventilated room (20-22oC), rats were placed into an open-top chamber (ABS plastic,

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acrylonitrile butadiene, 24cm2, 48cm high), with ports/spouts positioned along one wall: left reward spout, center stimulus port, right reward port. Ports/spouts were embedded with

880nm infrared photobeams, the status of which were acquired to detect entry (380Hz).

Rats were first shaped on the single-modality 2AC tasks (odor only and tone only) in blocks of 20 trials (Figures S3-1A-E, S3-2, Table S3-1). 5/7 rats were trained on the tone only 2AC first, and 2/7 on the odor only 2AC first, so that we could inspect for gross differences in CAT learning based upon initial shaping modality, of which none were observed. For these early phases, the rats were shaped to nosepoke in the center port and retrieve rewards to the right or left; they were required to reach two consecutive blocks of

≥85% correct before moving onto each subsequent phase. Rats were initially shaped with a ≥85% correct criterion on the single-modality tasks, which was later decreased to ≥80% in the final intermodal CAT. In phase 1 (Figures S3-1A, S3-2A), the left reward port was covered and rats learned to nose-poke into the center port for 200ms and retrieve a reward

(25µL of 5mM saccharin) from the right reward spout. Initially, the reward was auto- triggered to the right spout if the rat remained in the center port for ≥200ms to aid in behavioral shaping. Rats eventually associated the center nose-poke with a right reward and within 4s post-stimulus presentation would retrieve it. After two consecutive blocks at criterion (breaking the IR beam within 4s post-stimulus delivery), the auto-triggered reward was removed and the rat was required to break the reward IR beam for retrieval on its own. Many rats displayed a brief dip in performance when this auto-triggered reward was removed (Figure S3-2A).

In phase 2, (Figures S3-1B, S3-2B), with the left reward port still blocked, rats

(5/7; tone only 2AC) held for an additional 300ms (‘tone off’), the initial minimum

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stimulus duration requirement. After two consecutive criterion blocks, rats were advanced to phase 3, (S3-1C, S3-2C), wherein the right reward port was covered, while the left was made available. The rats nose-poked into the center port, waited the required 200ms hold period, and then received a ‘tone on’ stimulus during the 300ms stimulus duration, and were shaped to retrieve a left reward. Finally, in phase 4a (S3-1D, S3-2D), termed the ‘tone only’ task, both reward ports were made available and rats were shaped to report detection of the tone (‘tone on’ vs ‘tone off’). After reaching criterion (≥6 blocks of ≥85%), the rats were advanced to phase 4b, the ‘odor only’ task, where they were given odor stimuli instead of tone stimuli and shaped to discriminate between the two odors (‘odor A’ vs ‘odor

B’) (S3-1E, S3-2E). The remaining 2/7 rats learned the ‘odor only’ task first and the ‘tone only’ task second. Regardless of which task they were shaped on first, rats took significantly longer to reach criterion for the ‘tone only’ task than for the ‘odor only’ task

(101±6.3 vs 11±2.1 blocks, t(6)=-16.98, p<0.0001; 5.86±0.26 vs 1.29±0.18 sessions, t(6)=-

15.37, P<0.0001), but despite these learning differences, all rats were trained to the same level of criterion on each task.

We then shaped the rats to switch between modalities, on ‘tone only’ and ‘odor only’ blocks (Figure S3-2F, Table S3-2). The block task type was changed after they reached ≥85% criterion performance across three blocks for a single modality. In the following session, rats needed to complete three blocks of the tone only task at ≥85%, at which point we then presented both tone and odor simultaneously, but rewarded only the choices made towards the odors, in what we termed ‘odor attention’ blocks (Figure S3-

2G). Rats were shaped to complete 6 blocks of ≥80% correct on this odor attention task.

After successful completion at this criterion, we began the following sessions with the

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alternate ‘tone attention’ task, in which cues from both modalities were again presented simultaneously, but only choices made towards the tones were rewarded (Figure S3-2H, left). That is, while the same (now irrelevant) odors from the odor attention task were presented simultaneously, the rats learned that they no longer predicted reward availability.

Once they achieved 6 blocks of ≥80% correct, the rats were switched back to the ‘odor attention’ blocks, in which the odors were then rewarded, while the tones were now irrelevant (Figure S3-2H, right).

At this point, the rats had learned to switch from ‘tone attention’ to ‘odor attention’ blocks across the course of a session. At this point, the 200ms hold and 300ms stimulus duration times were gradually increased to 600ms and 400ms, respectively, in 100ms increments. For each increment, the rats were required to perform two consecutive blocks at ≥80%. Then, over the course of a session, a 1s ITI was imposed, followed by a second session in which a nose-poke within that 1s ITI period would reset the ITI and trial (viz., a

‘time-out’). We selected this mandatory ITI and time-out to provide a background epoch between trials for analysis of the physiology data (see later). The rats established robust behavioral performance over numerous successive sessions in this final CAT phase

(hereafter simply called the ‘CAT’; Figures 3-1C & 3-2A, S3-2H).

Classifying selective attention. Attentional shifts from tones to odors were determined by an initial decrease in performance at the attentional switch point, which gradually increased over the course of the session as the rat shifted its attention (Figure 3-1C & 3-2A). As expected, we found performance to fluctuate as rats determine which modality to attend to, so we focused our behavioral and physiological analyses on only correct trials from those

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blocks in which the rat performed at ≥80% correct (high performance before the switch

[tone attention]; high performance after the switch [odor attention]), well above chance. It is possible that during these blocks, there is an occasional lapse in attention to the opposite modality (e.g., as observed in other systems (Lakatos et al., 2016)) resulting in either 1) an incorrect choice (on incongruent trials) or 2) a correct choice for the wrong reason (on congruent trials). However, this would be infrequent and unlikely to bias our data given our strict requirement of ≥80% correct and discrete choice of analyzing only correct trials.

Behavioral data analysis. Two major epochs relative to odor onset were chosen for behavioral data analyses: Sampling duration: time from stimulus-onset to withdrawal from center port; latency to reward: withdrawal from center port to reward spout.

Task type was separated into three categories: ‘odor only,’ ‘tone attention,’ and

‘odor attention.’ Within these tasks, congruent and incongruent trials were separated and compared. Further, trials were separated based upon type (four different possible combinations, Figure S3-1H) and whether or not the rat was attending to the odors. This allowed for both the behavioral and neural data to be analyzed for the specific trial types that corresponded to the two possible odors in the absence of tone stimuli. For behavioral and electrophysiological data analyses, we required a minimum of: 1) 3 blocks (60 trials) of 2AC odor only at criterion (≥80%), 2) 6 blocks (120 trials, 60 trials unattended and with no tone) of tone attention at criterion (≥80%), and 6 blocks (120 trials, 60 trials attended with no tone) of odor attention at criterion (≥80%) within a single session. All behavioral data was taken after CAT learning; individual rats contributed a single mean value per behavioral measure, averaged over three sessions each. For final percent correct averages

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during tone and odor attention as reported in the main text, averages of the six criterion blocks pre and post switch were taken from the last 2 sessions/rat. All error bars are standard error means (SEMs).

Micro-drive and tetrode construction. Following Voigts et. al (Voigts et al., 2013), we constructed flexDrives. These ultralight Microdrive implants with independently moveable tetrodes were adapted slightly to fit our needs by utilizing only 8 of the independently moveable tetrodes (instead of 16) and increasing the length of the static guide tube to reach just dorsal to the OT. Tetrodes were constructed following Nguyen et al. (Nguyen et al.,

2009). We twisted 12.5µm XTC-bonded ni-chrome wires with a tetrode twister

(https://open-ephys.atlassian.net/wiki/display/OEW/Twister), and electroplated tetrodes to

200-250 kOhm in a neuralynx gold:PEG solution (Neuralynx non-cyanide gold plating solution in PEG [polyethylene glycol, Sigma-Aldrich, 8000MW, 1g/L in ddH2O]), following Ferguson et. al (Ferguson, Boldt and Redish, 2009). We also constructed a 3D- printed cone and cap [polylactic acid (PLA)] to cover the drive body, lined the inside with aluminum foil, and secured the cap top into place with plastic screws.

Surgical procedures. Rats were provided ad libitum access to both food and water for at least 48 hrs prior to surgery. We then unilaterally implanted all rats (n=7) with the constructed flexDrives just dorsal of the right OT. Only 4 were later utilized for electrophysiology data analyses due to electrode placement errors, poor signals, or inability to perform/sustain motivation during the course of the cognitively demanding CAT following surgery. Surgery was performed as described previously (Carlson, Dillione and

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Wesson, 2014), with the following modifications. After anesthesia (isoflurane, 3.5-1% in

1.5L/min O2) and preparation of craniotomies, three 0-80 s/s screws were implanted into the skull for anchoring the cement. The skull was cleaned with 0.9% physiological saline and 0.3% hydrogen peroxide, allowed to dry, and coated with a thin layer of Vetbond. The flexDrive array was lowered (centered at 1.2mm anterior to bregma, 1.75mm lateral to midline, 7.7mm ventral). Wax was applied to seal the craniotomy and a layer of cement was placed around the PI tubing to keep the flexDrive implant initially stable. One of the stripped ground wires (s/s) was wrapped around the ipsilateral skull screw and silver paint was applied to ensure conductivity. The stripped reference electrode was lowered into the contralateral hemisphere, sealed with wax, and cemented in place. Finally, additional layers of cement were used to secure the flexDrive base more rigidly to the skull and the ground skull screw. Before fully fleshing out the cement, the 3D-printed plastic cone was attached, surrounding the flexDrive. More cement was used to hold the drive and cone into place.

The second ground wire was threaded through the cone and attached to a screw on the outside of the cone, coated with silver paint, and epoxied into place. The cover of the cone was secured in place with plastic bolts, which allowed for its removal to access the connector during recordings. Rats were then injected with 2.0 mL physiological saline

(0.9%, s.c.) to aid in rehydration. After surgeries, rats were returned to their individual fresh home cages and they were given Carprofen daily for 5 days post-surgery (Rimadyl,

Pfizer Animal Care, 5mg/kg s.c.).

Data acquisition. We recorded full-band neural activity with Intan hardware (Intan

Technologies, Los Angeles, CA), amplified and digitized at the headstage, and stored at

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25kHz through the Intan software GUI. Behavior and odor presentation events were recorded simultaneously using OpenEx (Tucker Davis Technologies ,TDT) and an RZ5

BioAmp Processor with a sampling rate of 25kHz. We used an Arduino to ensure synchrony of behavioral and neural data.

Electrophysiology recordings. After surgery, the rats were allowed to recover for at least three days before being placed on a gradual water-restriction schedule, at which point they were handled and re-acclimated to the chamber. Their body weight was monitored and maintained daily by means of supplemental water, given at the end of each session (Table

S3-4). By day five post-surgery, rats began CAT re-shaping with their newly implanted hardware, but without the headstage/tether attached. Once they began switching, we plugged them into the headstage. Each session began with several blocks of the odor only

2AC, followed by tone attention blocks, and then odor attention blocks. Some tetrodes were independently advanced each day, such that they traversed the OT. If they had units, but the rat didn’t switch, for example, we did not move them and tried recording again the following day. If tetrodes were advanced, they were advanced at least 60 µm to ensure we were capturing novel neurons. Not all implanted rats contributed physiology data due to electrode placement errors, poor signals, or their inability to perform the cognitively demanding CAT following surgery.

Perceptually demanding CAT. After collecting the standard CAT data, we tested n=2 rats on the perceptually demanding reduced odor intensities. The CAT was identical, except we utilized a descending staircase design of decreased odor intensity over consecutive days

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(0.5, 0.05, 0.005, 0.0005 Torr). Performance on the single modality 2AC odor discrimination task when odor intensities were reduced: (0.5 Torr: 93±1.7%; 0.05 Torr:

91±0.8%; 0.005 Torr: 95±0%; 0.0005 Torr: 85±4.2%).

Electrode placement verification. Following recordings, the rats were lethally overdosed with a urethane injection (i.p.). Current was delivered into the tetrode array (Cygnus stimulus isolator; 50 uA, 15s) to aid in post-mortem localization of tetrode tips. The rats were then transcardially perfused with 4oC 0.9% NaCl, followed by 10% formalin (Fisher

Scientific, Waltham, MA). After perfusion, brains were removed and stored in 30% sucrose formalin at 4oC. We sectioned rat brains coronally (40µm), mounting on gelatin-subbed slides, and labeled with 0.1% cresyl violet. Micro-drive tip distances were reconstructed by finding the most ventral tip from the most ventral section, referencing the Paxinos and

Watson (1997) (Paxinos and Watson, 1997) rat brain atlas. OT recording sites were found mostly in the anterior OT, spanning medial-lateral and dorsal-ventral about 1mm2 (Figure

3-3A). Any recordings dorsal to the OT were not analyzed.

Electrophysiology with behavior data analysis. Neural data was converted from Intan to

Spike2 (Cambridge Electronic Design) and merged with TDT behavioral data. The full- band neural data was filtered (second-order low-pass Butterworth, 0.2-3000Hz). Units were sorted following tetrode-sorting methods in Spike2, where we used a combination of template matching and cluster cutting based on principle component analysis. Single neurons were defined as having <2% of spikes occurring within a 2ms refractory period.

Spike times associated with each trial were then extracted, exported, and analyzed with

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custom MATLAB (MathWorks) scripts, as we have described previously (Gadziola and

Wesson, 2016).

For analysis of electrophysiology data we categorized behavioral epochs relative to odor onset including: (1) background (-1400 to -800ms): time prior to approaching the center stimulus port during the ITI, (2) approach (-800 to -600ms): time period as rat approaches center port, (3) preparatory hold (-600 to 0ms): center port nose-poke until stimulus onset, and (4) odor (0 to 400ms): stimulus onset until the minimal 400ms required stimulus duration. Of particular interest is the odor epoch, during which information may be mapped onto the neurons and used to guide behavioral choice. Also of interest is the preparatory hold epoch, in which the rat is holding its nose in place in preparation for the arrival of the upcoming stimulus. Since it is not uncommon for neurons to encode anticipation of port entry and/or task engagement (Samuelsen, Gardner and Fontanini,

2012, 2013; Nieh et al., 2015), the 200ms approach epoch was categorized to clearly separate out background from the hold and odor epochs. The stimulus duration epoch is referred to as ‘odor’ because we only analyzed ‘tone off’ trials. Importantly, the required

400ms minimum stimulus sampling allowed for an unbiased epoch of time to be consistently compared.

Modulations of firing rate (FR) within a single trial were examined similar to

(Gadziola and Wesson, 2016). Mean FRs across trials were measured in 50ms bins. The mean background FR for each neuron was averaged across the background epoch. Neurons were categorized as significantly modulated (excited, inhibited, or unmodulated) by comparing the mean FR during the odor to the background FRs during odor attention utilizing a t-test. Neurons FRs during the odor epochs were classified as odor-excited

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(significantly greater), odor-inhibited (significantly less), or unmodulated (not significantly different). Across 116 neurons (Table S3-3), we performed this significance test for each odor pair. Many of the odor-excited and odor-inhibited cells were also modulated during the preparatory hold period. To test for this modulation, we similarly compared the hold

FR to background FRs and tested for significance. For the 2D histograms of FR in Figure

3-4A&B, we normalized the data to the minimums and maximums [FRnormalized = (FRx- min)/(max-min)], where FRx = FR of one 50ms bin, within each neuron across each task type. A single neuron’s lowest FR was then 0, while its highest FR was 1, so that differences in FR could be observed across attentional demand and states.

ROC analysis. We additionally performed an area under the receiver operating characteristic analysis (auROC), a nonparametric measure of the discriminability of two distributions (Green and Swets, 1966). This normalized activity across neurons and allowed us to significantly quantify stimulus-related changes in FR relative to the background activity, on a scale from 0-1, following Gadziola et al. (Gadziola and Wesson,

2016) (more details see (Cohen et al., 2012)). A value of 0.5 indicates overlapping distributions, while 0 or 1 indicate perfect discriminability. The auROC was calculated at each 50ms time bin over the 3.6s period (-1800ms to 1800ms), centered on odor onset for each neuron. Values >0.5 indicated the probability that FRs were increased relative to background, while values <0.5 indicated the probability that FRs were decreased relative to background. A null distribution of auROC values of ~0.5 was created by utilizing a permutation test, where the “response” and “background” FR labels were randomly reassigned and calculated 1000 times. Significant auROC bins (50ms), as reported, were

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determined by testing whether or not the actual auROC value was outside the 95% confidence interval of the null distribution (Veit and Nieder, 2013).

Statistical information. All statistical analyses were performed in Microsoft Excel or

MATLAB (Mathworks, Waltham, MA), and all data are reported as means +/- SEM unless noted otherwise. All t-tests are paired and two-tailed, unless otherwise stated.

Acknowledgments

This work was supported by NIH NIDCD grants R01DC014443 and

R01DC016519 to D.W. and F31DC014615 to K.C. We thank Dr. Ben Strowbridge for helpful discussions throughout this study. An earlier version of this manuscript entitled,

“Selective attention controls olfaction in rodents,” was published on the pre-print server bioRxiv (Carlson et al., 2017). At the time of this writing, an updated manuscript entitled,

“Selective attention controls olfactory decisions and the neural encoding of odors,” has been accepted and is In Press at Current Biology.

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Figure 3-1

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Figure 3-1. Odor-directed attention dictates discrimination accuracy. (A) CAT trial outline. Example trials show correct 2AC choices during odor-directed attention on ‘tone off’ trials. Dashed line indicates mandatory preparatory hold time. (B) Four possible trials during the final phase of the CAT. Top arrows for each trial indicate reward direction for tone cues; bottom arrows indicate reward direction for odors. Faded icons indicate cues that are present, but should be ignored when attending to the correct modality. (C) Example

2D histograms displaying performance of 7 rats over the course of six sessions of switching their attention during the CAT (20 trials/bin). Solid overlaid lines indicate performance (% correct); dashed horizontal lines indicate criterion performance (80%). Vertical dashed line with arrowheads indicates the uncued experimenter-controlled switch from tone to odor attention. See STAR Methods for additional details. For each rat, the top three rows were from early sessions, the bottom three rows from late sessions, except rat 1 which has 5 sessions. Rats 1-4 were used for the neural recordings. See also Figures S3-1 to S3-3 and

Tables S3-1&2.

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Figure 3-2

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Figure 3-2. Rats make fewer incongruent errors as they shift their attention, which leads to increased performance accuracy. (A) Average performance of all 7 rats relative to the attentional shift, on their first two (early) and last two sessions (late). Performance dropped to chance levels immediately after the task switch and returned to criterion as the rat shifted its attention to odors. Note that performance improved more quickly during late sessions. #p<0.01 (block -1 vs. 1, late), †p<0.01 (block 1 vs. 6, late), ‡p<0.05 (block 6 early vs. block 6 late). (B) Corresponding number of incongruent (red) and congruent (blue) errors per block relative to the attentional shift for early (dashed) and late (bold) sessions.

Incongruent errors increase significantly at the task switch and decrease across blocks and over sessions. ¥p<0.01 (block -1 vs. 1, late), ¢p<0.01 (block 1 vs. 6, late), ¤p<0.05 (block 6 early vs. block 6 late). (C) The average number of blocks for each rat to reach criterion after the attentional shift (minimum of 6 blocks ≥80%) for early and late sessions, *p<0.05.

(D) Average number of blocks for the rats to reach the first full criterion block of ≥80% performance. See also Figure S3-4.

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Figure 3-3

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Figure 3-3. OT units are bidirectionally modulated by odor-directed attention. (A)

Histological confirmation of recording locations in the OT. The dorsal-ventral and medial- lateral spans of the micro-drivable arrays are shown (red outline) within the grey outlined

OT (n = 4 rats). Front panel: +2.20mm; back panel: +1.7mm relative to bregma. (B) A representative odor-excited single unit (blue), which displayed an increase in firing rate

(FR) during the preparatory (hold) and stimulus presentation (odor). This FR was enhanced with odor-directed attention. (C) A representative odor-inhibited unit (red), which displayed a decrease in FR during the preparatory (hold) and stimulus presentation (odor).

This decrease in FR was further suppressed during odor-directed attention. Single-unit activity (SUA) was sorted from the multi-unit activity (MUA); the tetrode waveforms for each of the sorted units are in the upper right hand corner of their respective trace (scale:

100µV, 0.5ms). Peri-stimulus time histograms (PSTHs) are shown below the traces as averages across the tone (top) and odor-directed attention trials (bottom). These averages include only correct trials, from a single trial type (Odor B + tone off), taken from criterion performance blocks (≥80%) in either tone (top) or odor (bottom) attention. Raster plots include the first 30 of these correct trials. See also Table S3-3.

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Figure 3-4

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Figure 3-4. Odor-directed attention controls odor coding. 2D histograms (50ms bins) displaying normalized firing rates (FRs) of odor excited (A) and odor-inhibited (B) cell- odor pairs across the task states. Neurons are arranged from highest to lowest FRs, averaged over the first five bins post-stimulus onset during odor attention. See STAR

Methods for normalization details. As indicated by auROC significant bins, odor attention increases the FRs for odor-excited cell-odor pairs (C), and further decreases the FRs for odor-inhibited cell-odor pairs (D) during the preparatory hold and odor epochs. Each row represents the corresponding neuron from the 2D histograms in A and B. Odor-directed attention increases the percentage of odor-excited cell-odor pairs that have significantly excited activity (E) and the percentage of odor-inhibited cell-odor pairs that have significantly inhibited activity (G) relative to background, earlier and for a longer duration.

Odor attention thus significantly increases the duration of excitement (F) and inhibition

(H) during both hold and odor epochs. *p<0.05, **p<0.01, two-tailed, paired t-test. Data from four rats (same as in Figure 3-1), 2-6 sessions/rat. See also Figure S3-5.

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Figure 3-5

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Figure 3-5. Attention yields enhanced signal-to-noise among odor coding neurons.

Changes in FR with odor-directed attention, ΔHzattention=FRattended-FRunattended, for odor- excited (A) and odor-inhibited (B) cell-odor pairs for the three behavioral epochs. Pie chart:

The proportion of increased or decreased FRs among neurons that shifted either negatively or positively (≥1Hz). ns above pie charts indicate the number of cell-odor pairs shifted out of the total number of excited or inhibited cell-odor pairs. ΔHzattention during background plotted against ΔHzattention during either the hold or odor epochs for excited (C&D) or inhibited (E&F) neuron populations. Excited cell-odor pairs falling above the dotted unity line indicate a greater change in FR relative to background. Inhibited cell-odor pairs falling below the dotted unity line indicate a greater decrease in FR relative to background. Blue

(excited) and red (inhibited) arrowheads are shown for illustrative purposes to denote these shifts. Odor-modulated neurons that were not significantly modulated during the hold (only odor modulated) are colored in white. R2: [odor-excited, df=25] (background vs hold:

0.493, p<0.05; background vs odor: 0.641, p<0.05; [odor-inhibited, df=26]: (background vs hold: 0.243, n.s.; background vs odor odor: 0.061, n.s.). (G&H) The percentage of cell- odor pairs classified as either significantly excited or inhibited relative to background during the specified epochs are increased with attention to odor, particularly during the preparatory hold. Data from rats and sessions as in Figure 3-4. p-values in (H), as denoted, are two-tailed, paired t-tests. **p <0.01, ***p <0.001, one-proportion z-test. See also Figure

S3-5.

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Figure S3-1

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Figure S3-1. Detailed structure of CAT shaping phases. Related to Figure 3-1. (A-H)

Panels illustrate the CAT phases. Bold arrows indicate the direction of a correct choice.

(A) In phase 1 of the tone 2AC, the left port is restricted with a barrier, and rats learn to nose-poke into the center port (200ms), retrieving a reward to the right. (B) In phase 2, rats learn to hold (an additional 300ms, ‘tone off’ stimulus) and retrieve a reward to the right.

(C) In phase 3, the right port restricted and after a center nose-poke hold (200ms), a tone is played for 300ms, and rats retrieve a reward to the left. (D) In phase 4, with all ports accessible, rats receive either the ‘tone on’ or ‘tone off’ cue (300ms) after holding in the center port (200ms) and make the 2AC to the left or right in the tone only task. (E)

Following the phase 4 tone task, rats receive one of two odor stimuli, and make their decision to go left or right based on these cues in the odor only task. (F) Rats switch between modalities with 2AC tone only and 2AC odor only blocks over a session. Switches are indicated by the arrowhead and dashed line in the middle of the panels. (G) Rats begin the session with tone only blocks, and then switch to odor attention, where tone and odor cues are presented simultaneously, but they must attend to the odors. Faded icons and arrows indicate tone cues that rats must ignore. Note trial congruencies. (H) In the final

CAT, rats begin by performing tone attention blocks, switching to odor attention blocks in the latter half. Note which cues are faded at which times. The blue box highlights the data used for single-neuron analyses, wherein the trial types are exactly the same and tone is always off.

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Figure S3-2

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Figure S3-2. Behavioral performance during task shaping. Related to Figure 3-1. (A-

E) 2D histogram learning curves for the acquisition of phases 1-4 of the single-modality

2AC tone and odor tasks, as indicated. Data are organized by rat number (1-7) and are binned into blocks of 20 trials wherein each bin indicates the % correct. Multiple sessions may be shown and aligned for each rat in each phase. Solid lines are individual learning curves (% correct), while horizontal dashed lines indicate criterion performance.

Performance is only plotted up until the rat reached criterion for each phase, though in some cases the rats performed several blocks beyond criterion before moving on to the following phase. Note that 2/7 rats were trained on the odor 2AC first, while 5/7 were trained on the tone 2AC first.

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Figure S3-3

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Figure S3-3. Shifting to odor-directed attention is delayed when perceptual demand is enhanced. Related to Figure 3-1. (A) 2D histogram of performance for n = 2 rats over the course of an attentional shift, as the odor intensity is decreased across successive sessions (one session = one row). (B) The number of blocks it took rats to reach criterion (6 blocks at ≥80% correct) for tone attention (left) and odor attention (right) plotted against decreasing odor intensities. Rats took more blocks to switch as the stimulus intensity decreased. (C) The overall percent correct for the session as odor intensities are decreased for tone attention (left) and odor attention (right).

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Figure S3-4

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Figure S3-4. Selective attention influences subtle, yet critical aspects of olfactory behavior.

Related to Figure 3-2. (A) Photoionization detector (PID) trace (20Hz low-pass Butterworth filtered) displaying the voltage change relative to odor onset which illustrates the rapid onset and largely stable kinetics of the odor. T10 and T90 are the times to reach 10% and 90% of the maximum odor. n = 15 trials of 0.5 Torr (66.67 Pa) isopentyl acetate, presented for 2s. Sampling durations

(B) and latency to retrieve the reward (C) are similar across odor only, tone attention, and odor attention task types. Rats commit relatively few errors once they have reached criterion performance (D), and of those errors they commit, a greater proportion are on incongruent versus congruent trials (E). They invest more time sampling for incongruent trials (F), while their latency to retrieve the reward remains similar (G). Distribution of sampling durations (H) and latency to reward (I) across the four possible trial types and attentional states. Our bin sizes and epochs for later single-unit analyses, not to mention the mandatory stimulus sampling time of 400ms, overcome any influence of this behavioral difference on our neural data analyses. All sampling durations, latency to reward times, and error percentages were taken from all blocks in a session wherein the performance was at criterion (≥80%) and the trial outcome was correct. Sampling duration = odor onset to withdrawal from port; latency to reward = withdrawal from port to reward retrieval. Data from n = 7 rats, same rats as from Figure 3-1. **p<0.01, ***p<0.0001, two-tailed, paired t-test.

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Figure S3-5

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Figure S3-5. The majority of odor-unmodulated units have firing rate and signal-to- noise ratios that remain unchanged by attention. Related to Figures 3-4, 3-5.

Normalized FRs for (A) odor-excited, (B) odor-inhibited, and (C) unmodulated cell-odor pairs with and without odor-directed selective attention. (D-F) Normalized changes in FR with odor-directed attention for each epoch. (G) ΔHz plots for those units unmodulated by odors (n = 177). Changes in firing rate (FR) are displayed for each of the background, hold, and odor epochs. Inlay shows the percentage of units that were shifted negatively or positively among those units whose FR was shifted >1Hz. Note that the majority of the

177 units do not have changes in their FR beyond more than 1Hz, but for those that do during the hold epoch (20/177), the majority of their FRs shift negatively. (H) The change in background FR plotted against the change in the FR for either the hold (left) or odor

(right) epochs. Points largely fall along the dotted line, indicating minimal shifts in FR relative to changes in background FR. ns above the pie charts indicate the number of cell- odor pairs shifted out of the total number of excited or inhibited cell-odor pairs.

***p<0.0001, one-proportion z-test.

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Blocks Sessions rat P1 P2 P3 P4 P4 P4 P4 P1 P2 P3 P4 P4 P4 P4 odor1 odor2 tone1 tone2 odor1 odor2 tone tone 1 2 1 5 2 3 10 N/A N/A 105 1 1 1 1 N/A N/A 6 2 8 2 4 9 N/A N/A 92 2 1 1 1 N/A N/A 5 3 5 2 4 N/A 11 103 N/A 1 1 1 N/A 2 6 N/A 4 8 2 5 N/A 23 123 N/A 3 1 1 N/A 2 6 N/A 5 8 2 5 N/A 6 92 N/A 4 1 1 N/A 1 6 N/A 6 8 2 4 N/A 9 74 N/A 2 1 2 N/A 1 5 N/A 7 8 2 7 N/A 9 118 N/A 2 1 2 N/A 1 7 N/A avg 7.6 2 4.6 9.5 11.6 102 98.5 2.1 1 1.3 1.0 1.4 6.0 5.5 sem 0.6 0.0 0.5 0.5 3.0 8.9 6.5 0.4 0 0.2 0.0 0.2 0.3 0.5

Table S3-1. Total number of blocks and sessions to reach criterion across shaping phases 1-

4. Related to Figure 3-1 and Methods. Criterion for Phases 1-4 was ≥85% correct responses for

≥ 2 consecutive blocks. odor1 = trained on odor 2AC first (2/7 rats); odor2 = trained on tone 2AC second (5/7 rats).

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Blocks Sessions rat tone tone only tone final total # tone tone tone final total # only vs. attention task blocks to only only attention task sessions vs. odor vs. first vs. vs. vs. to first odor attention odor criterion odor odor odor criterion only attention switch only attention attention switch 1 10 9 256 162 562 1 1 11 7 30 2 12 7 232 168 534 1 1 9 7 28 3 32 7 88 103 355 4 1 4 6 26 4 19 10 116 98 404 2 1 4 5 25 5 16 8 41 58 236 1 1 2 3 20 6 15 12 34 155 313 2 1 2 6 22 7 22 7 88 83 344 2 1 4 3 23 avg 18.0 8.6 122.1 118.1 392.6 1.9 1.0 5.1 5.3 24.9 sem 2.8 0.7 33.3 16.4 44.6 0.4 0.0 1.3 0.6 1.3

Table S3-2. Total number of blocks and sessions to reach criterion across multimodal and attention phases. Related to Figure 3-1 and Methods. Criterion for odor and tone only: ≥85% correct on 3 blocks. Criterion for tone and odor attention: ≥80% correct on 6 blocks.

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Rat # sessions # neurons 1 6 29 2 6 13 3 9 42 4 10 32 total 31 116 avg 7.8 29.0 sem 1.0 6.0

Table S3-3. Descriptive summary of single-neuron data used for analyses. Related to Figure

3 and Methods. # neurons refers to the number of single neurons acquired from each rat that contributed to analyses.

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rat bwt at start of bwt at end of bwt% bwt at start of bwt at end of bwt% shaping (g) shaping (g) recordings (g) recordings (g) 1 266 316 118.8 430 358 83.3 2 288 321 111.5 420 420 100 3 300 364 121.3 415 394 94.9 4 327 384 117.4 440 418 95.0 5 287 338 117.8 420 394 93.8 6 279 336 120.4 N/A N/A N/A 7 275 360 130.9 406 374 92.1 avg 288.9 345.6 119.7 421.8 393 93.2 sem 7.6 9.3 2.2 4.8 9.9 2.3

Table S3-4. Body weights (bwts) during shaping and performance. Related to Methods. End bwt is increased compared to bwt at start of shaping, allowing rats to grow normally. All measures related to recordings were acquired from subjects post-op of micro-drive implantation.

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Chapter 4: Discussion

4.1 Major conclusions

The purpose of this dissertation was to understand the impact of state (sleep, anesthesia, and attention) on olfactory system function, given that sensory systems are not merely bottom-up hierarchical processing centers, but heavily influenced by state and top- down modulation (Lavin, Alcocer-Cuaron and Hernández ‐ Peón, 1959; Hopfinger,

Buonocore and Mangun, 2000; Neville and Haberly, 2003; Wilson and Yan, 2010; Baluch and Itti, 2011). The first goal of this thesis was to define how the network activity of the

OT compares to that of the OB, how it is sculpted in relation to respiration, and how the dynamics of network activity change with odor presentation. Additionally, we aimed to determine how state (sleep and anesthesia) influence spontaneous and odor-evoked activity

(Chapter 2). Simultaneously recording LFPs across olfactory areas along with sniffing allows us to better understand how the OT functions within the context of active sampling and in relation to the upstream OB. These results add to standing literature on state- dependent influences within the olfactory system, and additionally define the OT's role in shaping and relaying odor information (Wesson and Wilson, 2011).

The second goal of this thesis was to determine how selective attention to olfaction controls odor-guided behaviors and the neural dynamics that underlie these behaviors

(Chapter 3). To this end, we developed the novel two-alternative choice CAT, and provide the first evidence for behavioral and single-neuron influences within the olfactory cortex of intermodal selective attention to odors in rodents. The development of the CAT provides a critical advancement for the olfactory community to study odor-guided attention, given its numerous benefits (see Chapter 3) and its use in organically manipulating directed

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attention. Previously, no such task was available. Furthermore, this work significantly advances our understanding of how odor processing is shaped with regards to attentional state by putting forth a model whereby odor information is controlled via a signal-to-noise ratio, similar to that found in the visual system. This provides support for the notion that these changes in processing can take place even when odor information bypasses the thalamus. Prior to these experiments, no studies had investigated if and how selective attention sculpts single-neuron activity within the olfactory system. Given that it does, we now have a starting point to begin investigating precisely how selective attention functions in olfaction. Future work will need to determine where these attentional effects originate, the neuromodulators involved in contributing to the signal-to-noise changes, and the specific types of neurons that are influenced. Altogether, these data further define the OT's role in odor information processing and with the establishment of this task, will consequently allow for future research, including the optical and pharmacological dissection of the neural circuits that underlie selective attention to odors, to be performed in a highly tractable animal model.

4.2 Influences of selective attention on odor coding

An organism's sensory experiences are shaped based on their past experiences, predictive cues, and current behavioral goals. Our results add to the variety of studies in chemosensory systems (mainly taste) which have focused on the cognitive modulation of sensory processing (Fontanini and Katz, 2006; Nitschke et al., 2006; Samuelsen, Gardner and Fontanini, 2012, 2013; Gardner and Fontanini, 2014). We found that selective attention to odors recruits the modulation (further excited or further inhibited) of neurons more

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rapidly (Figure 3-4), similar to the finding that expectation speeds up taste coding in the gustatory cortex (Samuelsen, Gardner and Fontanini, 2012), which illustrates the importance of how such cognitive ‘biases’ can shape incoming sensory information at early stages. Furthermore, our results shed light on the underlying neural dynamics that may give rise to the changes observed with olfactory attention in OERP (Geisler and Murphy, 2000;

Morgan and Murphy, 2010; Andersson et al., 2018) and fMRI studies (Zelano et al., 2005;

Plailly et al., 2008; Veldhuizen and Small, 2011).

The enhanced signal-to-noise-ratios with odor-directed attention are nicely juxtaposed to those attentional influences observed in the visual system. Many visual studies have explored how neural responses change relative to attention, and different tasks have revealed unique aspects of the neural dynamics. The visual cortex displays filtering of unattended stimuli within the same receptive field of attended stimuli (Moran and

Desimone, 1985). Selective attention also frequently enhances sensory responses (Hubel et al., 1959; Hillyard et al., 1973; Spitzer, Desimone and Moran, 1988b; Desimone and

Duncan, 1995; Kastner and Ungerleider, 2000; Maunsell and Cook, 2002), in agreement with our findings (Figures 3-4&5). Enhanced cognitive demand also augments neural responses (Spitzer, Desimone and Moran, 1988b), similar to our results in Figure 3-4 (2AC discrimination compared to the intermodal CAT), demonstrating that the amount of attention or cognitive effort devoted to the stimulus effects how it is coded. It is important to note that within our experiments, selective attention to odor enhanced its representation beyond that of the fundamental 2AC discrimination (Figure 1-2G), instead of alternatively reducing its representation during tone attention (Figure 1-2F).

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While we observed an overall enhancement of odor representation, we more specifically uncovered that odor-excited neurons were further excited, while odor-inhibited neurons were further suppressed. That is, attention sculpted these two populations of neurons in opposing directions. When hypothesizing how selective attention may shape the neural dynamics (Figure 1-2F to 1-2I), we had considered only changes to those neurons which were odor-excited and neglected to consider neurons which were odor-inhibited.

The observed changes for odor-inhibited neurons were unanticipated but exemplify why it is important to consider how the incoming stimulus first may influence neurons differently independent of attention. Simply calculating how FRs change across neurons would have provided mixed effects, overshadowing the opposing effects. Furthermore, selective attention has been shown to suppress stimulus-evoked activity in an engaged task condition

(Otazu et al., 2009) as well as non-optimal or distracting stimuli irrelevant to the task at hand (Reynolds, Chelazzi and Desimone, 1999; Treue and Martinez Trujillo, 1999). In support of these findings in other sensory systems, we found that attention did not influence the neurons unmodulated by odors, and if anything, had background FRs that increased with attention (Figure 3-5C). Odor-directed attention therefore seems to be enhancing the signal that is relevant for the task at hand.

These two populations of neurons (odor-excited and odor-inhibited) raise the question of their functional significance. Neurons in the olfactory system, including those of the olfactory cortex, encode stimulus intensity, with odor-evoked excited responses increasing their FRs and odor intensity is increased (Meredith, 1986; Duchamp-Viret,

Duchamp and Chaput, 2000; Haberly, 2001; Onoda, Sugai and Yoshimura, 2005; Sugai et al., 2005; Kadohisa and Wilson, 2006; Stettler and Axel, 2009; Xia, Adjei and Wesson,

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2015). It is possible then that the enhanced FRs of odor-excited neurons within the olfactory cortex (likely in tandem with other olfactory structures) effectively reflect an internally enhanced odor intensity, allowing animals to better perceive attended odors. This may begin at the level of the OB, wherein information is first filtered via top-down modulation and then sent to downstream structures. Inhibitory neurons in the olfactory cortex act to maintain low spontaneous FRs across neurons (Sturgill and Isaacson, 2015), increasing the signal-to-noise ratio of brain activity. This could lead to an increase in the salience of encoded odors, allowing for odor responses to be more readily discriminated from background activity. It is possible that attention is recruiting more of these inhibitory neurons, which function to further suppress those neurons which are already odor- inhibited.

4.3 Caveats

4.3.1 CAT design

The establishment of this novel intermodal two-alternative choice task to distill the effects of selective olfactory attention in rodents (the CAT) is a huge step forward in understanding how selective attention influences odor-guided behavior and physiology.

We foresee it being used well into the future with the possibility for a variety of modifications (See Section 4.4). The CAT, however, cannot address all components of odor-directed attention, and it is not without limitations (see also Discussion in Chapter

3, which is expanded upon here). First, regarding odor-guided behavior, we did not find differences in sampling durations between the less cognitively demanding 2AC odor discrimination and the more challenging intermodal CAT (Figure S3-4B), which we had

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initially predicted (Figure 1-2E). This is likely because the rats were required to sample stimuli for 400ms. Had they been able to self-regulate their sampling, we predict that they would have taken longer to sample on the CAT than the 2AC odor only task. We found subtle influences in sampling durations for incongruent versus congruent trials (Fig S3-4F) and with attention (Fig S3-4G). If rodents had self-regulated their sampling durations, we predict that differences on incongruent versus congruent trials would be more significant, and further, that rodents would be able to more quickly make decisions when they were attending to and performed the tone detection task. Reporting whether or not a stimulus is presented (tone on versus tone off), as opposed to a discrimination of two odors, may be a faster decision. Self-regulated sampling duration would allow us to better understand how odor-directed selective attention influences perception and the ability to make rapid decisions.

Second, our definition of selective attention to the odors relied upon performance accuracy and the ability of the animal to make the correct choice. It is possible that the rat was attending to the incorrect modality (tones) during odor attention, but still made the correct choice (this would mostly be on a congruent trial), detracting from the observed

‘attentional influences.' To eliminate this bias, we analyzed only neural data from correct trials in which rats achieved high performance (³80%). Including these trials would dampen the observed effects, which means that the physiological effects are likely more significant than we report.

Third, during the CAT, rats switch from attending to tones to attending to odors over the course of a session. Although not included in the data presented herein, it is possible to shape rats to shift from odor attention to tone attention and vice versa. It is also

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possible to shape them to switch between attending to either modality multiple times within a session, though they have more blocks with low performance. The initial goal of the task was to have counterbalanced attentional states across the course of a session, such as (Otazu et al., 2009). However, this became unfeasible once rodents were shaped, implanted with microdrives, and connected via a tether to our recording system. Obtaining enough trials with enough high-performance blocks was challenging as rats were occasionally distracted by attachment to the tether. To have enough data with statistical power, we chose to switch the rats once, from tones to odors, allowing us to collect a minimum of 6 blocks of ³80% in either condition and time to also test the 2AC odor only task within the 1-2 hour session.

We would expect, however, that the signal-to-noise ratios of the odor-modulated neurons would decrease if we switched the rats back to tone attention, correlating with the attentional state of the rat. If this is the case, this would provide evidence that the neurons are able to flexibly and reversibly encode sensory information filtered through selective attention on a moment-to-moment basis. Indeed, in some preliminary experiments, at the end of the session after the switch, we repeated 2AC odor only and tone only tasks. The odor attention-enhanced FRs do indeed begin to fall back down to their lower FRs as seen at the beginning of the session with the 2AC odor only blocks.

Fourth, several types of errors can occur within the CAT, including perseverative errors, as the animal continues to follow the previously relevant cue before it has shifted its attention; probe errors, errors the animal makes when it begins to shift its attention and is unsure of which modality to attend; and maintenance errors, errors made after the animal has shifted its attention, that may occur during an attentional lapse to the opposing modality. Given the low FRs of OT neurons, we were unable to investigate how the signal-

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to-noise ratios relate to the outcome of the trial or how they change over the course of a session. Enhanced signal-to-noise ratios that correlate with performance would suggest that these ratios are meaningful to the rat’s behavior (i.e. is a high signal-to-noise ratio necessary for a correct choice?). To determine causal roles, during performance, signal-to- noise ratios could be manipulated optogenetically (enhanced or decreased) upon nose-poke into the center port. Questions such as, when the rat transitions from tone to odor attention, at what point does the signal-to-noise ratio change? Is it tangential to increases in performance? How high does the signal-noise ratio need to be in order to predict the animal's behavior of a correct choice? Does the decoding of odors become more accurate as the animal switches to odor attention? Relatedly, in a task in which rodents were allowed to self-regulate sampling durations, we could ask, are signal-to-noise ratios correlated with faster decision making or greater certainty? If other olfactory regions are similarly influenced by selective attention, higher FRs in those areas may better serve to investigate these questions.

Finally, the cues in this task were equally weighted. Though not always ‘relevant' for reward, the rats knew that at some point during the session the cue would predict reward delivery. We do not know how irrelevant cues would differ in processing. Task-irrelevant

‘distractor' cues were presented in (Otazu et al., 2009). Similar to Otazu et al., we predict that distractor odor cues, irrelevant to the task at hand, would be suppressed. If these cues gained salience and the rats then directed their attention toward them, we would expect an enhancement of FR. Presenting a precisely-timed olfactory distractor cue would, however, be much more difficult than presenting a distractor auditory cue. Despite these limitations of the current CAT and those described in the discussion of Chapter 3, the CAT is highly

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versatile. Small modifications of it can be used to suit the needs of the experimenter, and we expect that many more questions with regards to selective odor-guided attention could be explored with it in the future.

4.3.2 Attention and reward

Notably, units in the PCX (Calu et al., 2007; Roesch, Stalnaker and Schoenbaum,

2007), and the OT (Gadziola et al., 2015), code for odor valence. All cues within the CAT are at some point relevant to the task and across the switch. However, during tone attention, the odor temporarily does not predict reward. As attention shifts from tones to odors, the odor then temporarily gains value. Animals have learned this association over many sessions, but how can we separate a reward-related response from attention? Though attention and reward are distinct concepts on the surface, they are hard to disentangle

(Maunsell, 2004), particularly when we base the definition of selective attention on performance accuracy relative to the stimulus.

Importantly, during recordings, rats performed the 2AC odor only task, followed by the tone attention task, and were then ultimately switched to the odor attention task.

Two of our findings support the notion that the effects we find of selective attention go above and beyond those that are merely reward-related. First, odor attention responses were more greatly enhanced relative to odor-only responses. In both of these tasks, rats were attending to the equally rewarded odors, but the odor attention task required a greater attentional demand as the rats also had to ignore the simultaneous tones. Each odor was rewarded, and attended to, but the neural response was greater in odor attention. Second, responses during odor attention were enhanced relative to tone attention. Thus, although

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there was enhanced attentional demand with both multimodal tasks (tone and odor attention), there was an even greater enhancement of odor-evoked activity in the odor attention task beyond that of the tone attention task. While reward and attention may be difficult to disentangle, when attentional demand was varied, but the reward remained constant, we can be quite confident that these effects appear to be explicitly associated with attention.

4.3.3 Limitations of the extracellular techniques

There are some limitations regarding the extracellular techniques utilized herein.

First, we only extracted single-unit activity during the CAT. It would be interesting to further investigate, similarly to Chapter 2, how selective attention modulates the spectral components of local field potentials within the olfactory system. Given evidence that attention can acutely affect the spectral content of LFPs (Engel, Fries and Singer, 2001;

Fan et al., 2007; Sundberg et al., 2012), we predict that the LFP spectral power would be enhanced in the gamma band during odor-directed attention (relative to either 2AC odor only or tone attention) due to its known modulation by task demands (Beshel, Kopell and

Kay, 2007).

Second, we recorded SUA from one structure within the olfactory cortex, but it is possible to record local network activity across many structures simultaneously. How selective attention influences other regions, such as the PCX or the upstream OB, remains to be determined. We expect the spectral components of odor processing to be affected similarly between these regions (Zelano et al., 2005) and it is likely that they cooperate to inform stimulus perception, supporting the hypothesis that attention mediates olfaction via

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distributed parallel pathways (Payton, Wilson and Wesson, 2012). Evidence for which structures are most greatly influenced, at what step this information is filtered, and how it is transferred inter-regionally would be highly informative of olfactory system function in the context of cognitive modulation. Beyond the olfactory system, LFPs could be recorded simultaneously from multiple areas across different sensory systems (auditory and olfactory, for example) would allow for comparisons between oscillatory dynamics of the auditory and olfactory cortices. We hypothesize that increased gamma activity would occur with odor-directed attention in the olfactory cortex, that would be independent of changes within the auditory cortex and vice versa. Such global dynamics would likely be reflective of the "selective attention" changes observed across sensory systems (e.g., Hernández-Peón et al., 1961). Third, recording simultaneous single unit activity and LFPs would allow for spike-field comparisons, permitting us to better understand how single-neurons contribute to the network dynamics underlying selective attention. For example, during visual attention, single units have increased spike coherence (spike-field coherence) with theta

(Desimone and Duncan, 1995; Yu et al., 2018) and gamma oscillations (Fries et al., 2001).

Fourth, the extracellular technique we used limits our knowledge of the types of neurons that we are recording. Given that attention does not modulate all neurons, it is likely that it modulates specific subnetworks of neurons. Are the odor-excited neurons of a specific class? There is evidence for such cell-type specific dynamics of neurons and network activity in V4 of the awake monkey (Vinck et al., 2013). Furthermore, in the case of odor-excited neurons, do they receive input from excitatory cholinergic neurons?

Finally, how different neuromodulators may be involved during this task to enhance odorant representation is unknown. Given that acetylcholine levels elevate with increased

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attentional demand (Himmelheber, Sarter and Bruno, 2001), in the future, it will be informative to determine whether the increases in acetylcholine release relate to attentional state in the CAT. Recording from units while simultaneously measuring acetylcholine release via amperometry or voltammetry, for example, would provide insights into why FR changes may occur. Understanding how these neuromodulators shape the underlying activity would be correlative but knowing the exact timing of such changes would be informative for future experiments with specifically-timed optogenetic manipulations to determine causality (see Section 4.4.4).

Importantly, the robustly-controlled nature of this task will allow for future investigations to be made to tease apart the neural mechanisms of attention in greater detail, which is particularly important given the vast array of genetic tools available for rodents and the high relevance of the olfactory system in guiding rodent behavior.

4.4 Future directions

4.4.1 Influences of increased attentional demand

We highlighted the top-down influences of selective attention in our task, but this leads us to ask how bottom-up processing relative to the intensity of the stimulus influences neural activity. In the visual system, neuronal responses become larger and more selective in difficult tasks (Spitzer, Desimone and Moran, 1988b). That is, when increasing the amount of attention directed towards a stimulus, the responsiveness and selectivity of the neurons that process it are also enhanced. We provided data in Chapter 3 demonstrating that increased perceptual demand (decreased odor intensity), results in a delay in attentional shifting. To perform these experiments, we utilized different odor intensities on different

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sessions. However, this design did not allow us to follow how a single neuron's FR changed relative to odor intensity and attention.

In the spirit of (Spitzer, Desimone and Moran, 1988b), we propose an alternative odor-guided 2AC task that would answer this question. Rats could be trained on an easy task to discriminate odors (A versus B), and alternatively on a difficult task to discriminate binary odor mixtures, (e.g., A:B, 20:80, 40:60, 80:20, etc.) (Figure 4-1). To assay effects of attention on acuity, in one session mice would perform the "easy" enantiomer discrimination task, which would include occasional trials of the binary mixtures from the

“difficult” task. In a separate session, mice would perform the “difficult” binary mixture task, which would include occasional trials of the pure odorants from the “easy” enantiomer discrimination task. This allows for comparisons in performance on the easy trials when the mouse is performing the easy task (requires a smaller attentional load) versus easy trials when the mouse is performing the difficult task (requires greater attentional load) and vice versa. Given that the difficult task requires greater attentional effort, we hypothesize that performance during the difficult and easy trials will be better during the difficult task session (and vice versa during the easy session), paralleling results from the visual attention task (Spitzer, Desimone and Moran, 1988b) (Figure 4-1). We also predict that, similar to the neural effects found in this study, an enhancement of the signal-to-noise ratio that is even greater for those odors of lower intensity. This would suggest that to perform well on the task, the odor representation within the olfactory cortex should be even further enhanced (increasing the perception of its intensity), and would provide further evidence in the olfactory system for enhanced bottom-up processing via top-down attentional modulation.

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4.4.2 Multimodal influences

While the primary goal of this thesis was to investigate attentional modulation of odor processing, the CAT also affords opportunities to investigate multimodal processing.

Limiting the neurophysiological analyses to 'tone off' trials, we investigated the influence of selective attention alone, independent of multi-sensory-driven processes known to occur in the olfactory cortex (Wesson and Wilson, 2010; Maier, Wachowiak and Katz, 2012).

We did not investigate how 'tone on' trials during odor attention influence odor processing, nor how simultaneously presented congruent cues modulate sensory processing regardless of attentional state. Because attentional resources are limited, we predict that attention to tones when the tone is present would more greatly suppress odor-evoked activity, such that it would be even lower than that of 2AC odor only activity. Another aspect of interest could be the attentional modulation of those units within the olfactory cortex that are modulated by tones (Varga and Wesson, 2013). Apart from these explorations, one could also add more sensory modalities to increase the number of 'sensory channel' inputs and the cognitive demand of the task. We hypothesize that the more extramodal cues there are (e.g., more channels of sensory input via the addition of somatosensory or gustatory cues). This could be done by shaping the rats on a third modality, such as discriminating between two different tastes. It will be presumably more difficult for the rat to attend to odors, but we predict that the signal-to-noise ratio would be even more enhanced in comparison.

4.4.3 Active sampling

While we measured differences in sampling durations across the task and trial types, we did not collect simultaneous sniffing activity. It is likely that with odor-directed

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attention, rodents modulate their sniffing, which ultimately results in the sculpting of the neural FRs. It has even been described as a behavioral readout of directed attention

(Kepecs, Uchida and Mainen, 2007; Wesson et al., 2008; Wachowiak, 2011).

Though Gottfried controlled for changes in respiration in human subjects and found attentional effects beyond it (Plailly et al., 2008), we did not obtain measures of respiration from our rats. However, it is possible to use theta as a surrogate measure of respiration, given the findings in Chapter 2 which show that OT theta couples with respiration, albeit with a slight delay. To investigate how theta (sniffing) changes relative to task type, we filtered our recorded data (same data as used for unit recordings) between (2–12 Hz) (Leslie

M Kay, 2003). We then peak detected each oscillation and determined the average theta frequency relative to odor-onset. In these preliminary analyses, we found dynamic increases in theta peak frequency (presumed to be sniffing frequency) during the approach, hold, and odor-delivery across all task types (Figure 4-2). Surprisingly, we did not find differences with odor attention, however, which suggests that at least within the context of the CAT, sniffing frequency is unaltered with odor-directed attention. It may be that the sniffing frequency increases with general attention, but is not specific to selective attention.

4.4.4 Neuromodulatory influences

Where is the top-down attentional control coming from in the olfactory system?

While it may be difficult to identify the exact mechanism by which attention is derived

(and certainly it could arise from numerous structures including, for example, the prefrontal cortex), it is possible that cholinergic neurotransmission may contribute to selective attention by influencing the systems that control attention or by modulating local sensory

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input (Hasselmo, 1995; Sarter et al., 2005). Enhanced signal-to-noise ratios with odor- directed attention may occur via neuromodulation of the OB signal-to-noise ratios before it is then received by secondary olfactory structures (Cleland and Linster, 2003; Mandairon and Linster, 2009). Cholinergic modulation of stimulus processing is hypothesized to provide a mechanism whereby attention enhances stimulus representation (Hasselmo and

Barkai, 1995; Everitt and Robbins, 1997; Sarter et al., 2005). The olfactory system receives its primary cholinergic input from the basal forebrain (Wenk, Meyer and Bigl, 1977;

Shipley and Ennis, 1996). The direct input of cholinergic fibers into the OT via the horizontal diagonal band (HDB) (Price and Powell, 1970; Luskin and Price, 1982;

Zaborszky et al., 1986) provides an anatomical framework whereby attention-dependent cholinergic transmission may modulate odor coding in these structures. We hypothesize that descending cholinergic modulation from the HDB acts to regulate the encoding of odors within the OT and is instrumental for odor coding and the subsequent behavioral outcomes during odor-directed attention.

Cholinergic influences are known to critically mediate olfactory system function, throughout a variety of olfactory structures (Van Der Pers and Den Otter, 1978; Hasselmo and Bower, 1992; Hasselmo and Barkai, 1995; Linster, Wyble and Hasselmo, 1999;

Wilson, 2001; Castriota-Scanderbeg et al., 2005; Rothermel et al., 2014; San-Galli and

Bouret, 2014). Lesions of the HDB impair odor habituation investigation latency and duration (Paolini and McKenzie, 1993), while electrical stimulation influences EPSP in the

PCX (Linster, Wyble and Hasselmo, 1999), implicating a possible role in olfactory- mediated attention through descending cholinergic input. To understand these contributions and determine causality of attention-dependent effects, one could use

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optogenetics to either excite or inhibit the descending cholinergic input to test the hypothesis that cholinergic input from the HDB is required for attention-dependent odor coding in the OT and enhancements in odor acuity. We hypothesize that enhanced cholinergic input would enhance the rat’s ability to shift its attention towards odors, while inhibited input would decrease the rat’s ability to shift its attention towards odors.

Preliminary experiments in mice show robust retrograde transduction of HDB neurons from the OT with the AAV8-CamK2a-mCherry (Figure 4-3). This data indicates extensive and specific HDB labeling after bilateral OT injection. A comparable vector containing the inhibitory opsin ArchT (archaerhodopsin) could be injected in the future.

Following retrograde transduction and labeling of HDB cell bodies (~3 weeks), while recording from neurons in the OT during performance of the CAT, HDB neurons could be inhibited (bilaterally) by means of 532nm light delivered via an implanted fiber optic ferrule on either 1) half of the trials to the attended and unattended (30/60 trials each) odors or 2) on each trial after the point of the attentional shift. Continuous light would last for the duration of nose poke (hold and odor).

To determine the role of HDB input on odor processing, we would compare odor- evoked breadths of tuning and signal-to-noise ratios to those when HDB projection neurons are inhibited or excited. We hypothesize that a lack of cholinergic input will affect the signal-to-noise ratio, causing either impairments in performance as the rats begin to shift their attention to odors or delays in shifting. Further, we hypothesize that an increased cholinergic input would enhance the signal-to-noise ratio, allowing for enhanced odor discrimination or the ability to more quickly shift attention towards odors.

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To determine whether or not a lack of descending HDB input parallels attentional modulation of odor processing, comparing responses to attended and unattended stimuli with and without the inhibition of HDB output neurons could be related to behavioral performance. We predict that when HDB input is inhibited, signal-to-noise ratios during attended olfactory cues will be similar to unattended odors. We also expect that greater attentional performance will be correlated with enhanced signal-to-noise ratios, and dependent upon HDB input. Control experiments would include performance on the basic

2AC task, which, if still performing at high levels, would indicate a change in processing that was related to the attentional demand of the task instead of the inability to discriminate odors.

4.4.5 Goal cells

In some of the recordings during CAT performance, we noticed units that strongly fired as animals decided to go leftward or rightward to retrieve a reward. Place cells within the hippocampus encode information related to the rat's location within an environment

(O’Keefe and Dostrovsky, 1971) and some also increase their FR when the rat is at a goal location where a reward is distributed (Hok et al., 2007) or when the animal moves towards a goal. Neurons responsible for left versus right escape decisions have even been described in zebrafish (Koyama et al., 2016). To investigate how choices to the left or right may be encoded during the CAT, we separated left and right trials. Given the four distinct trial types (Figure 3-1B), across the different attentional states, half of the trials signal a reward to the left, while the other half signal a reward to the right. We could then look at how each neuron’s FR changed dependent on the resulting left or right correct decision.

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Responses were classified as excited if the FR during the ‘decision epoch' (400-

600ms relative to stimulus onset, the time during which the rat left the center port and moved to the left or right to retrieve a reward) was significantly increased relative to the

‘background' FR (-1400 to -800ms, relative to stimulus onset). If the increased FR was due only to leftward movement, the cell would respond when the animal moved left, regardless of the other stimuli presented or the attentional state, and vice versa for the right. We found preliminary evidence to support this laterality in goal-directed decision-making, wherein several units (n = 19/116, 16.38%) encoded a leftward goal-directed movement to the reward port (Figures 4-4, 4-5). We also found units that encoded a rightward goal-directed movement (not shown), though these were less prominent. This may suggest that there is a left or right hemispheric bias, as all of the units we recorded were from the right hemisphere.

For example, when rats attended to tones (Tone OFF = right decision; Tone ON = left decision), during presentation of the same odor, a neuron's FR upon leaving the port depended upon which way the animal moved (Figure 4-4). When the rat moved right, correctly following the Tone OFF cue (Figure 4-4A), the neuron’s FR was not increased.

However, when the rat moved left, correctly following the Tone ON cue (Figure 4-4B), the neuron's FR was increased. This increase in FR comes as the rat leaves the center port

(red ‘+'s indicate withdrawal from center port), but before reward retrieval (blue ‘*'s indicate entry into reward port) (Figure 4-4), suggesting that its response is related to the animal actively deciding to go to the left or right.

Given the example neuron in Figure 4-4, it is possible that the neuron is encoding the end of the ‘tone on’ stimulus. To ensure that this leftward goal-directed movement was

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independent of the stimulus presented, we separated n = 19 neurons classified as having a significantly increased FR during a correct left trial across any of the task conditions (odor only, tone attention, odor attention, and tone only). The normalized average FRs across these neurons were enhanced during the decision epoch when rats moved leftward (Figure

4-5, left), but not rightward (Figure 4-5, right), though some neurons encoded both leftward and rightward movement. This indicates that many neurons preferentially fire for unilateral goal-directed movement independent of the task type, attentional state, and the stimulus delivered. Further experiments should involve determining whether or not these neurons selective for goal-direction still increase their FR when the rat A) makes an incorrect decision by moving to the right instead of the left, or B) makes an incorrect decision on a rightward trial, by moving to the left. Analyzing these trials separately would allow one to parse apart if these neuron's FR changes were relative to the animal's decision or to its movement. Either outcome would provide compelling insight into how the OT may function to guide sensorimotor outcomes related to the integration of multiple modalities to make decisions.

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Figure 4-1

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Figure 4-1. Future experiment: Task structure for elucidating neural effects of increased attentional demand. Rats will be trained on both easy (odor A vs B) and difficult (binary mixtures of A and B) tasks. The easy task session (left) will consist of mostly easy trials (odor A v B), with presentations of difficult trials occasionally probed.

The difficult task will consist of mostly difficult trials (binary mixtures) with presentations of easy trials occasionally probed. We predict that performance on difficult trials will be worse when attentional demand is low (easy task) vs when attentional demand is high

(difficult task). Furthermore, we predict that performance on easy trials will be better during the difficult task than during the easy task. We expect that these behavioral correlated will be reflected in the OT signal-to-noise ratios.

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Figure 4-2

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Figure 4-2. The frequency of theta cycles increases during anticipatory hold and odor across task types. Used as a surrogate marking for sniffing, theta (2-12Hz) was filtered from full-band data of n = 4 rats performing the CAT. Averages were taken from filtered, peak-detected data, across trials from blocks within the different task types in 0.1sec bins from at least one session/rat.

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Figure 4-3

PCX HDB HDB OT

midline A B dorsal lateral

BCC D

left right

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Figure 4-3. Proposed experiment: Inhibiting cholinergic input from the HDB to the

OT during performance of the CAT. (A) An AAV encoding the inhibitory opsin ArchT is injected bilaterally into the OT. Retrogradely transduced neurons in the HDB will be inhibited bilaterally via light stimulation while recording from neurons in the OT. (B)

Retrogradely labeled bilateral HDB. (C) Left and (D) right hemispheres, showing layer ii

OT labeling with high specificity across the medial-lateral span. Scale bar: 100µm.

Neurons within the HDB would be inhibited, while recording from neurons within the OT during performance of the CAT.

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Figure 4-4

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Figure 4-4. Example of a goal-directed unit that increases its FR with leftward movement toward the reward port. In both (A) and (B), odors are unattended. With tone attention, ‘tone off’ indicates a rightward reward (A), while ‘tone on’ indicates a leftward reward (B). The goal-directed leftward units fire directly after the odor stimulus ends, as the rat leaves the port (red ‘+’s), well before the rat retrieves a reward around 1.0 sec (blue

‘*’s). Note that both odors are the same, such that these effects are independent of the odor identity. Also note the differing y-axes, the unit in (A) does not have a higher background

FR. Furthermore, this increase in FR with leftward movement occurs regardless of if the tone is on or off (see Figure 5-5). Epochs denoted by the following colors: light grey = background, dark grey = approach, orange = hold, light green = unattended odor, which occurred simultaneously with the presentation of tone.

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Figure 4-5

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Figure 4-5. Goal-directed units within the OT encode leftward movement. Across all task types (A) odor only, (B) tone attention, (C) odor attention, and (D) tone only, units within the OT encode for leftward movement upon port withdrawal. The majority of these leftward encoding units do not encode rightward movement, though some do occasionally encode both. These units increase their FR just after the 400ms of the required stimulus sampling, independent of task type, stimulus type, or attentional demand. The increase in

FR occurs as the animal removes its snout from the port and begins to move to the reward goal port, during the decision epoch (400 to 600ms post-stimulus onset, ‘dec’), as denoted in blue. The same neurons are arranged from 1-19 within each 2D histogram. FRs are normalized within each neuron. Trial type icons follow those presented in Figure 3-1B.

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Bibliography

Adrian, E. D. (1928) ‘The Basis of Sensation’, British Medical Journal, 1(4857), p. 287. doi: 10.1136/bmj.1.4857.287.

Adrian, E. D. (1942) ‘Olfactory reactions in the brain of the hedgehog’, Journal of Physiology, 100, pp. 459–473.

Adrian, E. D. (1950) ‘The electrical activity of the mammalian olfactory bulb’, Electroencephalogr Clin Neurophysiol, 2(4), pp. 377–388. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=14793507.

Adrian, E. D. (1954) ‘The basis of sensation: some recent studies of olfaction’, British Medical Journal, (Feb 6), pp. 287–290.

Ahrens, S. et al. (2015) ‘ErbB4 regulation of a thalamic reticular nucleus circuit for sensory selection’, Nature Neuroscience, 18(1), pp. 104–111. doi: 10.1038/nn.3897.

Alheid, G. F. and Heimer, L. (1988) ‘New perspectives in basal forebrain organization of special relevance for neuropsychiatric disorders: the striatopallidal, amygdaloid, and corticopetal components of substantia innominata.’, Neuroscience, 27(1), pp. 1–39.

Andersson, L. et al. (2018) ‘Effects of task demands on olfactory, auditory, and visual event-related potentials suggest similar top-down modulation across senses’, Chemical Senses, 43(2), pp. 129–134. doi: 10.1093/chemse/bjx082.

Aoki, S. et al. (2015) ‘Role of Striatal Cholinergic Interneurons in Set-Shifting in the Rat’, Journal of Neuroscience, 35(25), pp. 9424–9431. doi: 10.1523/JNEUROSCI.0490- 15.2015.

Arakawa, H. et al. (2008) ‘Scent marking behavior as an odorant communication in mice’, Neuroscience & Biobehavioral Reviews, 32(7), pp. 1236–1248. doi: http://dx.doi.org/10.1016/j.neubiorev.2008.05.012.

Assad, J. A. (2003) ‘Neural coding of behavioral relevance in parietal cortex’, Current Opinion in Neurobiology, pp. 194–197. doi: 10.1016/S0959-4388(03)00045-X.

Atiani, S. et al. (2014) ‘Emergent selectivity for task-relevant stimuli in higher-order auditory cortex’, Neuron, 82(2), pp. 486–499. doi: 10.1016/j.neuron.2014.02.029.

Baluch, F. and Itti, L. (2011) ‘Mechanisms of top-down attention’, Trends in Neurosciences, pp. 210–224. doi: 10.1016/j.tins.2011.02.003.

Barnes, D. C. et al. (2011) ‘Odor Fear Conditioning Modifies Piriform Cortex Local Field Potentials Both during Conditioning and during Post-Conditioning Sleep’, PLoS

169

ONE. Public Library of Science, 6(3), p. e18130. doi: 10.1371/journal.pone.0018130.

Beauchamp, G. K. and Yamazaki, K. (2003) ‘Chemical signalling in mice’, Biochem Soc Trans, 31(Pt 1), pp. 147–151. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12546673.

Berridge, K. C. and Aldridge, J. W. (2008) ‘Decision utility, the brain, and pursuit of hedonic goals’, Soc. Cogn., 26(5), pp. 621–646.

Beshel, J., Kopell, N. and Kay, L. M. (2007) ‘Olfactory bulb gamma oscillations are enhanced with task demands’, J Neurosci. 2007/08/03, 27(31), pp. 8358–8365. doi: 27/31/8358 [pii]10.1523/JNEUROSCI.1199-07.2007.

Birrell, J. M. and Brown, V. J. (2000) ‘Medial frontal cortex mediates perceptual attentional set shifting in the rat.’, J Neurosci, 20(11), pp. 4320–4324.

Bishop, G. H. (1932) ‘Cyclic changes in excitability of the optic pathway of the rabbit’, American Journal of Physiology--Legacy Content, 103(1), pp. 213–224.

De Bivort, B. L. and Van Swinderen, B. (2016) ‘Evidence for selective attention in the insect brain’, Current Opinion in Insect Science, 15, pp. 9–15. doi: 10.1016/j.cois.2016.02.007.

Blass, E. M. and Teicher, M. H. (1980) ‘Suckling’, Science, 210(4465), pp. 15–22. Available at: http://sfx.med.nyu.edu/sfxlcl3?sid=Entrez:PubMed&id=pmid:6997992.

Boring, E. G. (1961) ‘Fechner: Inadvertent founder of psychophysics’, Psychometrika, 26(1), pp. 3–8. doi: 10.1007/BF02289680.

Bouret, S. and Sara, S. J. (2002) ‘Locus coeruleus activation modulates firing rate and temporal organization of odour-induced single-cell responses in rat piriform cortex’, Eur J Neurosci, 16(12), pp. 2371–2382. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12492432.

Brennan, P., Kaba, H. and Keverne, E. B. (1990) ‘Olfactory recognition: a simple memory system’, Science, 250(4985), pp. 1223–1226. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=2147078.

Bressler, S. L. and Freeman, W. J. (1980) ‘Frequency analysis of olfactory system EEG in cat, rabbit, and rat’, Electroencephalogr Clin Neurophysiol, 50(1–2), pp. 19–24. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=6159187.

170

Broadbent, D. E. (1958) ‘Perception and communication’, Education + Training, 8(6), pp. 264–269. doi: 10.1108/eb015727.

Brown, R. E. et al. (2012) ‘Control of Sleep and Wakefulness’, Physiological Reviews, 92(3), pp. 1087–1187. doi: 10.1152/physrev.00032.2011.

Buck, L. and Axel, R. (1991) ‘A novel multigene family may encode odorant receptors: a molecular basis for odor recognition’, Cell, 65, p. 175.

Buck, L. B. (1996) ‘Information coding in the vertebrate olfactory system’, Annu Rev Neurosci, 19, pp. 517–544.

Buonviso, N. et al. (2003) ‘Rhythm sequence through the olfactory bulb layers during the time window of a respiratory cycle’, Eur J Neurosci, 17(9), pp. 1811–1819. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12752780.

Buonviso, N., Amat, C. and Litaudon, P. (2006) ‘Respiratory modulation of olfactory neurons in the rodent brain’, Chem Senses, 31(2), pp. 145–154. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=16339270.

Bushdid, C. et al. (2014) ‘Humans Can Discriminate More than 1 Trillion Olfactory Stimuli’, Science, 343(6177), pp. 1370–1372. doi: 10.1126/science.1249168.

Buzsaki, G. (2006) Rhythms of the Brain. Oxford Press.

Calleja, C. (1893) La region olfactoria del cerebro. Madrid: Nicolas Moya.

Calu, D. J. et al. (2007) ‘Associative Encoding in Posterior Piriform Cortex during Odor Discrimination and Reversal Learning’, Cereb Cortex, 17(6), pp. 1342–1349. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=16882682.

Cameron, E. L., Tai, J. C. and Carrasco, M. (2002) ‘Covert attention affects the psychometric function of contrast sensitivity’, Vision Research, 42(8), pp. 949–967. doi: 10.1016/S0042-6989(02)00039-1.

Carlson, K. S. et al. (2017) ‘Selective attention controls olfaction in rodents’, bioRxiv. Available at: http://biorxiv.org/content/early/2017/12/18/236331.abstract.

Carlson, K. S., Dillione, M. and Wesson, D. W. (2014) ‘Odor- and state-dependent olfactory tubercle local field potential dynamics in awake rats’, Journal of neurophysiology, 111(10), pp. 2109–2123. doi: 10.1152/jn.00829.2013.

171

Carlson, K. S., Xia, C. Z. and Wesson, D. W. (2013) ‘Encoding and representation of intranasal CO2 in the mouse olfactory cortex’, Journal of Neuroscience, 33(34), pp. 13873–13881.

Carmichael, S. T., Clugnet, M. C. and Price, J. L. (1994) ‘Central olfactory connections in the macaque monkey’, J Comp Neurol, 346(3), pp. 403–434.

Carr, W. J., Krames, L. and Costanzo, D. J. (1970) ‘Previous sexual experience and olfactory preference for novel versus original sex partners in rats’, Journal of Comparative Physiological Psychology, 71(2), pp. 216–222.

Carrasco, M. (2006) ‘Chapter 3 Covert attention increases contrast sensitivity: psychophysical, neurophysiological and neuroimaging studies’, Progress in Brain Research, pp. 33–70. doi: 10.1016/S0079-6123(06)54003-8.

Carriero, G. et al. (2009) ‘Distribution of the Olfactory Fiber Input Into the Olfactory Tubercle of the In Vitro Isolated Guinea Pig Brain’, J Neurophysiol, 101(3), pp. 1613– 1619. doi: 10.1152/jn.90792.2008.

Castriota-Scanderbeg, A. et al. (2005) ‘The appreciation of wine by sommeliers: a functional magnetic resonance study of sensory integration’, NeuroImage, 25(2), pp. 570–578. doi: http://dx.doi.org/10.1016/j.neuroimage.2004.11.045.

Cenier, T. et al. (2008) ‘Odor vapor pressure and quality modulate local field potential oscillatory patterns in the olfactory bulb of the anesthetized rat’, Eur J Neurosci, 27(6), pp. 1432–1440. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=18364022.

Cenier, T. et al. (2009) ‘Respiration-gated formation of gamma and beta neural assemblies in the mammalian olfactory bulb’, European journal of neuroscience, 29(5), pp. 921–930. Available at: http://sfx.med.nyu.edu/sfxlcl3?sid=Entrez:PubMed;id=pmid:19291223.

Chabaud, P. et al. (1999) ‘Functional coupling in rat central olfactory pathways: a coherence analysis’, Neurosci Lett, 276(1), pp. 17–20. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=10586964.

Chapuis, J. et al. (2007) ‘Importance of retronasal and orthonasal olfaction for odor aversion memory in rats’, Behav Neurosci, 121(6), pp. 1382–1392.

Chapuis, J. et al. (2009) ‘The Way an Odor Is Experienced during Aversive Conditioning Determines the Extent of the Network Recruited during Retrieval: A Multisite Electrophysiological Study in Rats’, J. Neurosci., 29(33), pp. 10287–10298. doi:

172

10.1523/jneurosci.0505-09.2009.

Chapuis, J. et al. (2013) ‘Lateral Entorhinal Modulation of Piriform Cortical Activity and Fine Odor Discrimination’, The Journal of neuroscience, 33(33), pp. 13449–13459. doi: 10.1523/jneurosci.1387-13.2013.

Chen, K. et al. (2013) ‘Olfaction spontaneously highlights visual saliency map’, Proceedings of the Royal Society B Biological Sciences, 280(1768), p. 20131729. doi: 10.1098/rspb.2013.1729.

Cherry, E. C. (1953) ‘Some experiments on the recognition of speech, with one and with 2 ears’, Journal of the Acoustical Society of America, 25(5), pp. 975–979. doi: 10.1121/1.1907229.

Chiang, E. and Strowbridge, B. W. (2007) ‘Diversity of neural signals mediated by multiple, burst-firing mechanisms in rat olfactory tubercle neurons’, J Neurophysiol, 98(5), pp. 2716–2728.

Cleland, T. A. and Linster, C. (2003) ‘Central olfactory structures’, in Doty, R. L. (ed.) Handbook of olfaction and gustation. 2nd edn. New York: Marcel Dekker, pp. 165–180.

Cohen, J. Y. et al. (2012) ‘Neuron-type-specific signals for reward and punishment in the ventral tegmental area’, Nature. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved., 482(7383), pp. 85–88. Available at: http://dx.doi.org/10.1038/nature10754.

Cohen, M. R. and Maunsell, J. H. R. (2009) ‘Attention improves performance primarily by reducing interneuronal correlations’, Nat Neurosci. Nature Publishing Group, 12(12), pp. 1594–1600. Available at: http://dx.doi.org/10.1038/nn.2439.

Cohen, M. R. and Newsome, W. T. (2009) ‘Estimates of the Contribution of Single Neurons to Perception Depend on Timescale and Noise Correlation’, J. Neurosci., 29(20), pp. 6635–6648. doi: 10.1523/jneurosci.5179-08.2009.

Colzato, L. S. et al. (2014) ‘Attentional control in the attentional blink is modulated by odor’, Attention, Perception, and Psychophysics, 76(6), pp. 1510–1515. doi: 10.3758/s13414-014-0733-0.

Courtiol, E. et al. (2011) ‘Individual and synergistic effects of sniffing frequency and flow rate on olfactory bulb activity’, Journal of neurophysiology, 106(6), pp. 2813–2824. doi: 10.1152/jn.00672.2011.

Courtiol, E. and Wilson, D. A. (2016) ‘Neural Representation of Odor-Guided Behavior in the Rat Olfactory Thalamus’, The Journal of Neuroscience , 36(22), pp. 5946–5960. doi: 10.1523/JNEUROSCI.0533-16.2016. Crick, F. (1984) ‘Function of the thalamic reticular complex: the searchlight hypothesis’,

173

Proc Natl Acad Sci U S A, 81(14), pp. 4586–4590. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=6589612.

Darling, F. M. and Slotnick, B. (1994) ‘Odor-cued taste avoidance: a simple and efficient method for assessing olfactory detection, discrimination and memory in the rat.’, Behav Neurosci, 55(5), pp. 817–822.

Desimone, R. and Duncan, J. (1995) ‘Neural Mechanisms of Selective Visual’, Annual Review of Neuroscience, 18(1), pp. 193–222. doi: 10.1146/annurev.ne.18.030195.001205.

Deutsch, J. A. and Deutsch, D. (1963) ‘Attention: Some theoretical considerations’, Psychological Review, 70(1), pp. 51–60. doi: 10.1037/h0042712.

Duchamp-Viret, P., Duchamp, A. and Chaput, M. A. (2000) ‘Peripheral odor coding in the rat and frog: quality and intensity specification’, J Neurosci, 20(6), pp. 2383–2390. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=10704512.

Duffy, E. (1957) ‘The psychological significance of the concept of “arousal” or “activation”’, Psychological Review, 64(5), pp. 265–275. doi: 10.1037/h0048837.

Engel, A. K., Fries, P. and Singer, W. (2001) ‘Dynamic predictions: oscillations and synchrony in top-down processing’, Nat Rev Neurosci, 2(10), pp. 704–716. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=11584308.

Engel, T. A. et al. (2016) ‘Selective modulation of cortical state during spatial attention’, Science, 354(6316), pp. 1140–1144. doi: 10.1126/science.aag1420.

Engen, T. (1983) ‘The human uses of olfaction’, American Journal of Otolaryngology-- Head and Neck Medicine and Surgery, 4(4), pp. 250–251. doi: 10.1016/S0196- 0709(83)80067-2.

Everitt, B. J. and Robbins, T. W. (1997) ‘Central cholinergic systems and cognition’, Annu Rev Psychol, 48, pp. 649–684. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=9046571.

Fallon, J. H. (1983) ‘The islands of Calleja complex of rat basal forebrain, II. Connections of medium and large sized cells.’, Brain Res Bull, 10, pp. 179–199.

Fan, J. et al. (2002) ‘Testing the Efficiency and Independence of Attentional Networks’, Journal of Cognitive Neuroscience, 14(3), pp. 340–347. doi:

174

10.1162/089892902317361886.

Fan, J. et al. (2007) ‘The relation of brain oscillations to attentional networks.’, The Journal of neuroscience : the official journal of the Society for Neuroscience, 27(23), pp. 6197–6206. doi: 10.1523/JNEUROSCI.1833-07.2007.

Ferguson, J. E., Boldt, C. and Redish, A. D. (2009) ‘Creating low-impedance tetrodes by electroplating with additives’, Sensors and Actuators, A: Physical, 156(2), pp. 388–393. doi: 10.1016/j.sna.2009.10.001.

Ferrero, D. M. et al. (2011) ‘Detection and avoidance of a carnivore odor by prey’, Proceedings of the National Academy of Sciences, 108(27), pp. 11235–11240. doi: 10.1073/pnas.1103317108.

Firestein, S. (1991) ‘A noseful of odor receptors’, Trends in Neurosciences, 14(7), pp. 270–272. doi: 10.1016/0166-2236(91)90135-H.

Firestein, S. and Werblin, F. (1989) ‘Odor-induced membrane currents in vertebrate- olfactory receptor neurons’, Science, 244(4900), pp. 79–82. doi: 10.1126/science.2704991.

Fletcher, M. L. et al. (2005) ‘High-frequency oscillations are not necessary for simple olfactory discriminations in young rats.’, Journal of Neuroscience, 25(4), pp. 792–798.

Fontanini, A. and Bower, J. M. (2005) ‘Variable coupling between olfactory system activity and respiration in ketamine/xylazine anesthetized rats’, J Neurophysiol. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=15689385.

Fontanini, A. and Bower, J. M. (2006) ‘Slow-waves in the olfactory system: an olfactory perspective on cortical rhythms’, Trends in neurosciences, 29(8), pp. 429–437.

Fontanini, A. and Katz, D. B. (2005) ‘7 to 12 Hz activity in rat gustatory cortex reflects disengagement from a fluid self-administration task’, J Neurophysiol. 2004/12/03, 93(5), pp. 2832–2840. doi: 01035.2004 [pii]10.1152/jn.01035.2004.

Fontanini, A. and Katz, D. B. (2006) ‘State-dependent modulation of time-varying gustatory responses’, J Neurophysiol. 2006/08/25, 96(6), pp. 3183–3193. doi: 00804.2006 [pii]10.1152/jn.00804.2006.

Fontanini, A. and Katz, D. B. (2008) ‘Behavioral states, network states, and sensory response variability’, J Neurophysiol. 2008/07/11, 100(3), pp. 1160–1168. doi: 90592.2008 [pii]10.1152/jn.90592.2008.

Fontanini, A., Spano, P. and Bower, J. M. (2003) ‘Ketamine-xylazine-induced slow (<

175

1.5 Hz) oscillations in the rat piriform (olfactory) cortex are functionally correlated with respiration’, J Neurosci, 23(22), pp. 7993–8001. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12954860.

Frederick, D. E. et al. (2011) ‘Rat behavior in go/no-go and two-alternative choice odor discrimination: Differences and similarities’, Behavioral Neuroscience. US: American Psychological Association, 125(4), pp. 588–603. doi: 10.1037/a0024371.

Frederick, D. E. et al. (2016) ‘Gamma and Beta Oscillations Define a Sequence of Neurocognitive Modes Present in Odor Processing’, The Journal of Neuroscience , 36(29), pp. 7750–7767. doi: 10.1523/JNEUROSCI.0569-16.2016.

Freeman, W. J. (1975) Mass action in the nervous system: Examination of the Neurophysiological Basis of Adaptive Behavior through the EE. New York: Academic Press.

Freeman, W. J. and Baird, B. (1987) ‘Relation of olfactory EEG to behavior: spatial analysis’, Behav Neurosci, 101(3), pp. 393–408. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=3606812.

Freeman, W. J. and Viana Di Prisco, G. (1986) ‘Relation of olfactory EEG to behavior: time series analysis’, Behav Neurosci, 100(5), pp. 753–763. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=3778638.

Friedrich, R. W. (2006) ‘Mechanisms of odor discrimination: neurophysiological and behavioral approaches’, Trends Neurosci. 2005/11/18, 29(1), pp. 40–47. doi: S0166- 2236(05)00282-1 [pii]10.1016/j.tins.2005.10.004.

Friedrich, R. W. and Korsching, S. I. (1998) ‘Chemotopic, combinatorial, and noncombinatorial odorant representations in the olfactory bulb revealed using a voltage- sensitive axon tracer’, J Neurosci, 18(23), pp. 9977–9988. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=9822753.

Fries, P. et al. (2001) ‘Modulation of oscillatory neuronal synchronization by selective visual attention’, Science, 291(5508), pp. 1560–1563. doi: 10.1126/science.1055465.

Fritz, J. et al. (2003) ‘Rapid task-related plasticity of spectrotemporal receptive fields in primary auditory cortex’, Nature Neuroscience, 6(11), pp. 1216–1223. doi: 10.1038/nn1141.

Gadziola, M. A. et al. (2015) ‘The Olfactory Tubercle Encodes Odor Valence in Behaving Mice’, The Journal of Neuroscience , 35(11), pp. 4515–4527. doi:

176

10.1523/JNEUROSCI.4750-14.2015.

Gadziola, M. A. and Wesson, D. W. (2016) ‘The Neural Representation of Goal-Directed Actions and Outcomes in the Ventral Striatum’s Olfactory Tubercle’, The Journal of Neuroscience , 36(2), pp. 548–560. doi: 10.1523/JNEUROSCI.3328-15.2016.

Galef, B. G. (1985) ‘Direct and Indirect Behavioral Pathways to the Social Transmission of Food Avoidance’, Annals of the New York Academy of Sciences. Blackwell Publishing Ltd, 443(1), pp. 203–215. doi: 10.1111/j.1749-6632.1985.tb27074.x.

Gardner, M. P. H. and Fontanini, A. (2014) ‘Encoding and Tracking of Outcome-Specific Expectancy in the Gustatory Cortex of Alert Rats’, The Journal of neuroscience, 34(39), pp. 13000–13017. doi: 10.1523/jneurosci.1820-14.2014.

Geisler, M. W. and Murphy, C. (2000) ‘Event-related brain potentials to attended and ignored olfactory and trigeminal stimuli’, International Journal of Psychophysiology, 37(3), pp. 309–315. doi: 10.1016/S0167-8760(00)00111-2.

Geisler and Murphy (2000) ‘Event-related brain potentials to attended and ignored olfactory and trigeminal stimuli’, Int J Psychophysiol, 37(3), pp. 309–315. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=10858576.

Gelperin, A. and Tank, D. W. (1990) ‘Odour-modulated collective network oscillations of olfactory interneurons in a terrestrial mollusc’, Nature, 345(6274), pp. 437–440. Available at: http://dx.doi.org/10.1038/345437a0.

Gerfen, C. R. et al. (1990) ‘D1 and D2 dopamine receptor-regulated gene expression of striatonigral and striatopallidal neurons’, Science, 250(4986), pp. 1429–1432. Available at: http://science.sciencemag.org/content/250/4986/1429.abstract.

Gervais, G. (1979) ‘Unilateral lesions of the olfactory tubercle modifying general arousal effects in the rat olfactory bulb’, Electroencephalogr Clin Neurophysiol, 46(6), pp. 665– 674.

Gervais, R. et al. (2007) ‘What do electrophysiological studies tell us about processing at the olfactory bulb level?’, J Physiol Paris, 101(1–3), pp. 40–45. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=18054211.

Gottfried, J. A. (2010) ‘Central mechanisms of odour object perception’, Nat Rev Neurosci. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved., 11(9), pp. 628–641. doi: http://www.nature.com/nrn/journal/v11/n9/suppinfo/nrn2883_S1.html.

Gottlieb, J. (2012) ‘Attention, learning and the value of information’, Neuron, 76(2), pp.

177

281–295. doi: 10.1016/j.neuron.2012.09.034.

Grant, D. and Berg, E. (1948) ‘A behavioral analysis of degree of reinforcement and ease of shifting to new responses in a Weigl-type card-sorting problem’, J Exp Psychology, 38(4), pp. 404–11.

Gray, C. M. and Skinner, J. E. (1988) ‘Field potential response changes in the rabbit olfactory bulb accompany behavioral habituation during the repeated presentation of unreinforced odors’, Exp Brain Res, 73(1), pp. 189–197. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=3208856.

Green, D. M. and Swets, J. A. (1966) Signal detection theory and psychophysics. New York: Wiley.

Griffith, L. C. and Ejima, A. (2009) ‘Multimodal sensory integration of courtship stimulating cues in drosophila melanogaster: Contextual effects on chemosensory cues’, in Annals of the New York Academy of Sciences, pp. 394–398. doi: 10.1111/j.1749- 6632.2009.04367.x.

Grossberg, S., Hwang, S. and Mingolla, E. (2002) ‘Thalamocortical dynamics of the McCollough effect: boundary-surface alignment through perceptual learning’, Vision Res, 42(10), pp. 1259–1286. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12044758.

Guillery, R. W. et al. (1998) ‘Paying attention to the thalamic reticular nucleus’, Trends in Neurosciences, 21(1), pp. 28–32. doi: 10.1016/S0166-2236(97)01157-0.

Haber, S. N. (2011) Neuroanatomy of Reward: A View from the Ventral Striatum, Neurobiology of Sensation and Reward. Edited by J. A. Gottfried. Boca Raton (FL): CRC Press.

Haberly, L. B. (2001) ‘Parallel-distributed processing in olfactory cortex: new insights from morphological and physiological analysis of neuronal circuitry’, Chem Senses, 26(5), pp. 551–576. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=11418502.

Haberly, L. B. and Price, J. L. (1977) ‘The axonal projection patterns of the mitral and tufted cells of the olfactory bulb in the rat’, Brain Res, 129(1), pp. 152–157. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=68803.

Hadley, J. K. and Halliwell, J. V (2010) ‘Serotonin modulates glutamatergic transmission in the rat olfactory tubercle’, European journal of neuroscience, 31(4), pp. 659–672.

178

Available at: http://dx.doi.org/10.1111/j.1460-9568.2010.07084.x.

Hasselmo, M. E. (1995) ‘Neuromodulation and cortical function: modeling the physiological basis of behavior’, Behav Brain Res, 67(1), pp. 1–27. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=7748496.

Hasselmo, M. E. and Barkai, E. (1995) ‘Cholinergic modulation of activity-dependent synaptic plasticity in the piriform cortex and associative memory function in a network biophysical simulation’, J Neurosci, 15(10), pp. 6592–6604. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=7472421.

Hasselmo, M. E. and Bower, J. M. (1992) ‘Cholinergic suppression specific to intrinsic not afferent fiber synapses in rat piriform (olfactory) cortex’, J Neurophysiol, 67(5), pp. 1222–1229. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=1597708.

Heimer, L. and Wilson, R. D. (1975) The subcortical projections of the allocortex: similarities in the neural associations of the hippocampus, the piriform cortex, and the neocortex, Golgi Centennial Symposium, Proceedings. Edited by M. Santini. New York: Raven.

Hernández-Peón, R. et al. (1961) ‘THE EFFERENT CONTROL OF AFFERENT SIGNALS ENTERING THE CENTRAL NERVOUS SYSTEM’, Annals of the New York Academy of Sciences, 89(5), pp. 866–882. doi: 10.1111/j.1749-6632.1961.tb20183.x.

Hillyard, S. A. et al. (1973) ‘Electrical Signs of Selective Attention in the Human Brain’, Science, 182(4108), p. 177 LP-180. Available at: http://science.sciencemag.org/content/182/4108/177.abstract.

Himmelheber, A. M., Sarter, M. and Bruno, J. P. (2001) ‘The effects of manipulations of attentional demand on cortical acetylcholine release’, Cognitive Brain Research, 12(3), pp. 353–370. doi: 10.1016/S0926-6410(01)00064-7.

Hirokawa, J. et al. (2011) ‘Multisensory information facilitates reaction speed by enlarging activity difference between superior colliculus hemispheres in rats’, PLoS ONE, 6(9). doi: 10.1371/journal.pone.0025283.

Hocherman, S. et al. (1976) ‘Evoked unit activity in auditory cortex of monkeys performing a selective attention task’, Brain Res, 117(1), pp. 51–68. doi: http://dx.doi.org/10.1016/0006-8993(76)90555-2.

Hok, V. et al. (2007) ‘Goal-Related Activity in Hippocampal Place Cells’, The Journal of Neuroscience, 27(3), p. 472 LP-482. Available at:

179

http://www.jneurosci.org/content/27/3/472.abstract.

Hopfinger, J. B., Buonocore, M. H. and Mangun, G. R. (2000) ‘The neural mechanisms of top-down attentional control’, Nature Neuroscience, 3(3), pp. 284–291. doi: 10.1038/72999.

Hubel, D. H. et al. (1959) ‘“Attention” Units in the Auditory Cortex’, Science, pp. 1279– 1280. doi: 10.1126/science.129.3358.1279.

Hughes, N. K., Price, C. J. and Banks, P. B. (2010) ‘Predators are attracted to the olfactory signals of prey’, PLoS ONE, 5(9), pp. 1–4. doi: 10.1371/journal.pone.0013114.

Humphreys, M. S. and Revelle, W. (1984) ‘Personality, motivation, and performance: a theory of the relationship between individual differences and information processing’, Psychological review, 91(2), pp. 153–84. doi: 10.1037/h0021465.

Hurst, J. L. and Beynon, R. J. (2004) ‘Scent wars: the chemobiology of competitive signalling in mice’, BioEssays. Wiley Subscription Services, Inc., A Wiley Company, 26(12), pp. 1288–1298. doi: 10.1002/bies.20147.

Ikemoto, S. (2007) ‘Dopamine reward circuitry: Two projection systems from the ventral midbrain to the nucleus accumbens-olfactory tubercle complex’, Brain Res Rev, 56(1), pp. 27–78.

Illig, K. R. and Haberly, L. B. (2003) ‘Odor-evoked activity is spatially distributed in piriform cortex’, J Comp Neurol, 457(4), pp. 361–373. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12561076.

Inokuchi, A. et al. (1988) ‘Effects of locus ceruleus and olfactory bulb stimulation on rat olfactory tubercle neuron activity.’, Otolaryngol Head Neck Surg, 98(2), pp. 116–120.

Ishikawa, T. et al. (2007) ‘Odor-Driven Activity in the Olfactory Cortex of an In Vitro Isolated Guinea Pig Whole Brain With Olfactory Epithelium’, J Neurophysiol, 97(1), pp. 670–679. doi: 10.1152/jn.01366.2005.

Isles, A. R. et al. (2001) ‘Urinary odour preferences in mice’, Nature, 409, pp. 783–784. James, W. (1890) The principles of psychology. New York: Holt.

Johnson, B. A. and Leon, M. (2007) ‘Chemotopic odorant coding in a mammalian olfactory system’, J Comp Neurol. 2007/05/08, 503(1), pp. 1–34. doi: 10.1002/cne.21396.

Johnson, D. M. et al. (2000) ‘New features of connectivity in piriform cortex visualized by intracellular injection of pyramidal cells suggest that “primary” olfactory cortex functions like “association” cortex in other sensory systems’, J Neurosci, 20(18), pp.

180

6974–6982. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=10995842.

Kadohisa, M. and Wilson, D. A. (2006) ‘Olfactory cortical adaptation facilitates detection of odors against background’, J Neurophysiol, 95(3), pp. 1888–1896. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=16251260.

Karpov, A. P. (1980) ‘Analysis of Neuron Activity in the Rabbit’s Olfactory Bulb during Food-Acquisition Behavior’, in Thompson, R. F., Hicks, L. H., and Shvyrkov, V. B. (eds) Neural Mechanisms of Goal-Directed Behavior and Learning. New York: Academic Press, pp. 273–282.

Kastner, S. and Ungerleider, L. G. (2000) ‘Mechanisms of visual attention in the human cortex’, Annu Rev Neurosci, 23, pp. 315–341. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=10845067.

Kay, L. M. (2003) ‘A challenge to chaotic itinerancy from brain dynamics’, Chaos: An Interdisciplinary Journal of Nonlinear Science, 13(3), pp. 1057–1066. doi: doi:http://dx.doi.org/10.1063/1.1596071.

Kay, L. M. (2003) ‘Two species of gamma oscillations in the olfactory bulb: dependence on behavioral state and synaptic interactions.’, J Integr Neurosci, 2(1), pp. 31–44.

Kay, L. M. (2005) ‘Theta oscillations and sensorimotor performance’, Proc Natl Acad Sci U S A, 102(10), pp. 3863–3868. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=15738424.

Kay, L. M. et al. (2009) ‘Olfactory oscillations: the what, how and what for’, Trends in neurosciences, 32(4), pp. 207–214. Available at: http://www.sciencedirect.com/science/article/B6T0V-4VP583K- 1/2/81074b757b21e048942f3b263ef83986.

Kay, L. M. and Beshel, J. (2010) ‘A beta oscillation network in the rat olfactory system during a 2-alternative choice odor discrimination task’, Journal of neurophysiology, 104(2), pp. 829–839. Available at: http://sfx.med.nyu.edu/sfxlcl3?sid=Entrez:PubMed;id=pmid:20538778.

Kay, L. M. and Laurent, G. (1999) ‘Odor- and context-dependent modulation of mitral cell activity in behaving rats’, Nat Neurosci, 2(11), pp. 1003–1009. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=10526340. Kay, L. M. and Sherman, S. M. (2006) ‘An argument for an olfactory thalamus’, Trends

181

Neurosci. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=17161473.

Kay, L. M. and Stopfer, M. (2006) ‘Information processing in the olfactory systems of insects and vertebrates’, Seminars in Cell & Developmental Biology, 17(4), pp. 433–442. doi: http://dx.doi.org/10.1016/j.semcdb.2006.04.012.

Kay, R. B. et al. (2011) ‘Spatial distribution of neural activity in the anterior olfactory nucleus evoked by odor and electrical stimulation’, The Journal of Comparative Neurology. Wiley Subscription Services, Inc., A Wiley Company, 519(2), pp. 277–289. doi: 10.1002/cne.22519.

Kepecs, A., Uchida, N. and Mainen, Z. F. (2007) ‘Rapid and precise control of sniffing during olfactory discrimination in rats’, J Neurophysiol, 98(1), pp. 205–213. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=17460109.

Kim, D., Lokey, S. and Ling, S. (2017) ‘Elevated arousal levels enhance contrast perception’, Journal of Vision, 17(2), p. 14. doi: 10.1167/17.2.14.

Komisaruk, B. R. (1970) ‘Synchrony between limbic system theta activity and rhythmical behavior in rats’, J Comp Physiol Psychol, 70(3), pp. 482–492. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=5418472.

Kopell, N. et al. (2000) ‘Gamma rhythms and beta rhythms have different synchronization properties’, Proceedings of the National Academy of Sciences of the United States of America, 97(4), pp. 1867–1872. Available at: http://sfx.med.nyu.edu/sfxlcl3?sid=Entrez:PubMed;id=pmid:10677548.

Kowler, E. et al. (1995) ‘The role of attention in the programming of saccades’, Vision Research, 35(13), pp. 1897–1916. doi: 10.1016/0042-6989(94)00279-U.

Koyama, M. et al. (2016) ‘A circuit motif in the zebrafish hindbrain for a two alternative behavioral choice to turn left or right’, eLife, 5(AUGUST). doi: 10.7554/eLife.16808.001.

Krauel, K. et al. (1998) ‘Attentional modulation of central odor processing’, Chemical Senses, 23(4), pp. 423–432. doi: 10.1093/chemse/23.4.423.

Laing & Glemarec D G, A. (1992) ‘Selective attention and the perceptual analysis of odor mixtures’, Physiology & Behavior, 52, pp. 1047–1053.

Lakatos, P. et al. (2016) ‘Global dynamics of selective attention and its lapses in primary auditory cortex’, Nat Neurosci. Nature Publishing Group, a division of Macmillan

182

Land, M. F. (1999) ‘Motion and vision: Why animals move their eyes’, Journal of Comparative Physiology - A Sensory, Neural, and Behavioral Physiology, pp. 341–352. doi: 10.1007/s003590050393.

Laurent, G. (2002) ‘Olfactory network dynamics and the coding of multidimensional signals’, Nat Rev Neurosci, 3(11), pp. 884–895. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12415296.

Laurent, G. and Davidowitz, H. (1994) ‘Encoding of Olfactory Information with Oscillating Neural Assemblies’, Science, 265(5180), pp. 1872–1875.

Lavin, A., Alcocer-Cuaron, C. and Hernandez-Peon, R. (1958) ‘Centrifugal arousal in the olfactory bulb’, Science, 129, pp. 332–333.

Lavin, A., Alcocer-Cuaron, C. and Hernández-Peón, R. (1959) ‘Centrifugal arousal in the olfactory bulb’, Science, 129(3345), pp. 1–2. doi: 10.1126/science.129.3345.332.

Lepousez, G. and Lledo, P.-M. (2013) ‘Odor Discrimination Requires Proper Olfactory Fast Oscillations in Awake Mice’, Neuron. Cell Press, 80(4), pp. 1010–1024. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0896627313006491.

Li, A. et al. (2012) ‘Effects of Different Anesthetics on Oscillations in the Rat Olfactory Bulb’, Journal of the American Association for Laboratory Animal Science, 51(4), pp. 458–463. Available at: http://www.ingentaconnect.com/content/aalas/jaalas/2012/00000051/00000004/art00012.

Li, H. H., Barbot, A. and Carrasco, M. (2016) ‘Saccade Preparation Reshapes Sensory Tuning’, Current Biology, 26(12), pp. 1564–1570. doi: 10.1016/j.cub.2016.04.028.

Li, Q. and Liberles, S. D. (2015) ‘Aversion and attraction through olfaction’, Current biology : CB, 25(3), pp. R120–R129. doi: 10.1016/j.cub.2014.11.044.

Linster, C. and Cleland, T. A. (2002) ‘Cholinergic modulation of sensory representations in the olfactory bulb’, Neural Netw, 15(4–6), pp. 709–717. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12371521.

Linster, C., Wyble, B. P. and Hasselmo, M. E. (1999) ‘Electrical stimulation of the horizontal limb of the diagonal band of broca modulates population EPSPs in piriform cortex’, J Neurophysiol, 81(6), pp. 2737–2742. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=10368393.

183

Litaudon, P., Garcia, S. and Buonviso, N. (2008) ‘Strong coupling between pyramidal cell activity and network oscillations in the olfactory cortex’, Neuroscience. 2008/09/16, 156(3), pp. 781–787. doi: S0306-4522(08)01115-9 [pii]10.1016/j.neuroscience.2008.07.077.

Logan, D. W. et al. (2012) ‘Learned Recognition of Maternal Signature Odors Mediates the First Suckling Episode in Mice’, Current biology : CB. Cell Press, 22(21), pp. 1998– 2007. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0960982212010068.

Lowry, C. A. and Kay, L. M. (2007) ‘Chemical factors determine olfactory system beta oscillations in waking rats’, J Neurophysiol. 2007/04/20, 98(1), pp. 394–404. doi: 00124.2007 [pii]10.1152/jn.00124.2007.

Luskin, M. B. and Price, J. L. (1982) ‘The distribution of axon collaterals from the olfactory bulb and the nucleus of the horizontal limb of the diagonal band to the olfactory cortex, demonstrated by double retrograde labeling techniques’, J Comp Neurol. 1982/08/10, 209(3), pp. 249–263. doi: 10.1002/cne.902090304.

Luskin, M. B. and Price, J. L. (1983) ‘The topographic organization of associational fibers of the olfactory system in the rat, including centrifugal fibers to the olfactory bulb’, The Journal of Comparative Neurology, 216, pp. 264–291.

Macrides, F., Eichenbaum, H. B. and Forbes, W. B. (1982) ‘Temporal relationship between sniffing and the limbic theta rhythm during odor discrimination reversal learning’, J Neurosci, 2, pp. 1705–1711.

Maier, J. X., Wachowiak, M. and Katz, D. B. (2012) ‘Chemosensory Convergence on Primary Olfactory Cortex’, The Journal of neuroscience, 32(48), pp. 17037–17047. doi: 10.1523/jneurosci.3540-12.2012.

Mainen, Z. F. (2006) ‘Behavioral analysis of olfactory coding and computation in rodents’, Current Opinion in Neurobiology, 16(4), pp. 429–434. Available at: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VS3-4KBDWDY- 2&_user=489277&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C0000226 79&_version=1&_urlVersion=0&_userid=489277&md5=db1b4a4530963e60dc9bdc870 8e36b8a.

Manabe, H. and Mori, K. (2013) ‘Sniff rhythm-paced fast and slow gamma-oscillations in the olfactory bulb: relation to tufted and mitral cells and behavioral states’, Journal of neurophysiology, 110(7), pp. 1593–1599. doi: 10.1152/jn.00379.2013.

Mandairon, N. and Linster, C. (2009) ‘Odor perception and olfactory bulb plasticity in adult mammals.’, Journal of neurophysiology, 101(5), pp. 2204–2209. doi: 10.1152/jn.00076.2009. Martin, C. et al. (2004) ‘Learning-induced modulation of oscillatory activities in the

184

mammalian olfactory system: the role of the centrifugal fibres’, J Physiol Paris. 2005/11/09, 98(4–6), pp. 467–478. doi: S0928-4257(05)00019-7 [pii]10.1016/j.jphysparis.2005.09.003.

Martin, C. et al. (2006) ‘Learning-induced oscillatory activities correlated to odour recognition: a network activity’, Eur J Neurosci. 2006/04/21, 23(7), pp. 1801–1810. doi: EJN4711 [pii]10.1111/j.1460-9568.2006.04711.x.

Martin, C., Beshel, J. and Kay, L. M. (2007) ‘An olfacto-hippocampal network is dynamically involved in odor-discrimination learning’, J Neurophysiol, 98(4), pp. 2196– 2205. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=17699692.

Maunsell, J. H. (2004) ‘Neuronal representations of cognitive state: reward or attention’, Trends Cogn. Sci., 8, p. 261.

Maunsell, J. H. and Cook, E. P. (2002) ‘The role of attention in visual processing’, Philos Trans R Soc Lond B Biol Sci, 357(1424), pp. 1063–1072. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12217174.

Maunsell, J. H. R. and Treue, S. (2006) ‘Feature-based attention in visual cortex’, Trends in Neurosciences, pp. 317–322. doi: 10.1016/j.tins.2006.04.001.

McAdams, C. J. and Maunsell, J. H. (1999) ‘Effects of attention on the reliability of individual neurons in monkey visual cortex.’, Neuron, 23(4), pp. 765–773. doi: S0896- 6273(01)80034-9 [pii].

Mcadams, C. J. and Maunsell, J. H. R. (2000) ‘Attention to Both Space and Feature Modulates Neuronal Responses in Macaque Area V4’, Journal of Neurophysiology, 83, pp. 1751–1755. doi: 10.1152/jn.2000.83.3.1751.

McCormick, D. A. and Bal, T. (1997) ‘SLEEP AND AROUSAL: Thalamocortical Mechanisms’, Annual Review of Neuroscience, 20(1), pp. 185–215. doi: 10.1146/annurev.neuro.20.1.185.

McCormick, D. A., McGinley, M. J. and Salkoff, D. B. (2015) ‘Brain state dependent activity in the cortex and thalamus’, Current Opinion in Neurobiology, 31, pp. 133–140. doi: 10.1016/j.conb.2014.10.003.

McDonald, D. G., Johnson, L. C. and Hord, D. J. (1964) ‘Habituation of the Orienting Response in Alert and Drowsy Subjects’, Psychophysiology. 1964/10/01, 57, pp. 163– 173. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=14226790.

185

McNamara, A. M. et al. (2008) ‘Distinct neural mechanisms mediate olfactory memory formation at different timescales’, Learn Mem. 2008/02/27, 15(3), pp. 117–125. doi: 15/3/117 [pii] 10.1101/lm.785608.

McNamara, A. M., Cleland, T. A. and Linster, C. (2004) ‘Characterization of the synaptic properties of olfactory bulb projections’, Chem Senses, 29(3), pp. 225–233. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=15047597.

Meredith, G. E. (1999) ‘The synaptic framework for chemical signaling in nucleus accumbens’, Annals of the New York Academy of Sciences, 877(1 ADVANCING FRO), pp. 140–156. doi: 10.1111/j.1749-6632.1999.tb09266.x.

Meredith, M. (1986) ‘Patterned response to odor in mammalian olfactory bulb: the influence of intensity’, J Neurophysiol, 56(3), pp. 572–597. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=3537224.

Miller, J. (1982) ‘Divided attention: Evidence for coactivation with redundant signals’, Cognitive Psychology, 14(2), pp. 247–279. doi: 10.1016/0010-0285(82)90010-X.

Miller, J. M. et al. (1980) ‘Electrophysiologic studies of the auditory cortex in the awake monkey’, American Journal of Otolaryngology--Head and Neck Medicine and Surgery, 1(2), pp. 119–130. doi: 10.1016/S0196-0709(80)80004-4.

Millhouse, O. E. (1987) ‘Granule cells of the olfactory tubercle and the question of the islands of calleja’, J Comp Neurol, 265(1), pp. 1–24.

Millhouse, O. E. and Heimer, L. (1984) ‘Cell configurations in the olfactory tubercle of the rat.’, J Comp Neurol, 228(4), pp. 571–597.

Milner, A. D. and Goodale, M. A. (1995) The visual brain in action. Oxford: Oxford University Press.

Mitchell, J. F., Sundberg, K. A. and Reynolds, J. H. (2007) ‘Differential Attention- Dependent Response Modulation across Cell Classes in Macaque Visual Area V4’, Neuron, 55(1), pp. 131–141. doi: 10.1016/j.neuron.2007.06.018.

Moran, J. and Desimone, R. (1985) ‘Selective attention gates visual processing in the extrastriate cortex’, Science, 229(4715), pp. 782–784. doi: 10.1126/science.4023713.

Morgan, C. D. and Murphy, C. (2010) ‘Differential effects of active attention and age on event-related potentials to visual and olfactory stimuli.’, International journal of psychophysiology : official journal of the International Organization of Psychophysiology, pp. 190–9. doi: 10.1016/j.ijpsycho.2010.07.008.

186

Mori, K. et al. (2013) ‘Olfactory consciousness and gamma oscillation couplings across the olfactory bulb, olfactory cortex and orbitofrontal cortex’, Frontiers in Psychology, 4. doi: 10.3389/fpsyg.2013.00743.

Mori, K., Nagao, H. and Yoshihara, Y. (1999) ‘The olfactory bulb: coding and processing of odor molecule information’, Science, 286(5440), pp. 711–715. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=10531048.

Murakami, M. et al. (2005) ‘State-dependent sensory gating in olfactory cortex’, Neuron, 46, pp. 285–296.

Nagayama, S. et al. (2010) ‘Differential Axonal Projection of Mitral and Tufted Cells in the Mouse Main Olfactory System’, Frontiers in Neural Circuits, 4, pp. 1–8. doi: 10.3389/fncir.2010.00120.

Narikiyo, K., Manabe, H. and Mori, K. (2014) ‘Sharp wave-associated synchronized inputs from the piriform cortex activate olfactory tubercle neurons during slow-wave sleep’, Journal of Neurophysiology, 111(1), pp. 72–81. Available at: http://jn.physiology.org/content/111/1/72.abstract.

Neville, K. R. and Haberly, L. (2004) ‘Olfactory cortex’, in Shepherd, G. M. (ed.) The synaptic organization of the brain. 5th edn. New York: Oxford University Press, pp. 415–454.

Neville, K. R. and Haberly, L. B. (2003) ‘Beta and gamma oscillations in the olfactory system of the urethane-anesthetized rat’, J Neurophysiol, 90(6), pp. 3921–3930. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12917385.

Nguyen, D. P. et al. (2009) ‘Micro-drive array for chronic in vivo recording: tetrode assembly’, Journal of Visualized Experiments, (26). doi: 10.3791/1098.

Nieh, E. H. et al. (2015) ‘Decoding neural circuits that control compulsive sucrose seeking’, Cell, 160(3), pp. 528–541. doi: 10.1016/j.cell.2015.01.003.

Nitschke, J. B. et al. (2006) ‘Altering expectancy dampens neural response to aversive taste in primary taste cortex’, Nature Neuroscience, 9(3), pp. 435–442. doi: 10.1038/nn1645.

Norman, D. A. (1968) ‘Toward a theory of memory and attention’, Psychological Review, 75(6), pp. 522–536. doi: 10.1037/h0026699.

O’Keefe, J. and Dostrovsky, J. (1971) ‘The hippocampus as a spatial map. Preliminary

187

evidence from unit activity in the freely-moving rat’, Brain Research, 34(1), pp. 171– 175. Available at: http://www.sciencedirect.com/science/article/B6SYR-484B26V- 2N9/2/297f6b9c5cbd0e5f981aa273421a09fb.

Oatman, L. C. (1971) ‘Role of visual attention on auditory evoked potentials in unanesthetized cats’, Experimental Neurology, 32(3), pp. 341–356. doi: 10.1016/0014- 4886(71)90003-3.

Ogawa, M. et al. (2013) ‘Risk-Responsive Orbitofrontal Neurons Track Acquired Salience’, Neuron, 77(2), pp. 251–258. doi: http://dx.doi.org/10.1016/j.neuron.2012.11.006.

Ongur, D. and Price, J. L. (2000) ‘The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans’, Cereb Cortex, 10(3), pp. 206– 219. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=10731217.

Onoda, N., Sugai, T. and Yoshimura, H. (2005) ‘Odor-intensity coding in the anterior piriform cortex’, Chem Senses. 2005/03/02, 30 Suppl 1, pp. i162-3. doi: 30/suppl_1/i162 [pii]10.1093/chemse/bjh164.

Otazu, G. H. et al. (2009) ‘Engaging in an auditory task suppresses responses in auditory cortex’, Nat Neurosci. Nature Publishing Group, 12(5), pp. 646–654. Available at: http://dx.doi.org/10.1038/nn.2306.

Pankevich, D. E., Baum, M. J. and Cherry, J. A. (2004) ‘Olfactory Sex Discrimination Persists, Whereas the Preference for Urinary Odorants from Estrous Females Disappears in Male Mice after Vomeronasal Organ Removal’, J. Neurosci., 24(42), pp. 9451–9457. doi: 10.1523/jneurosci.2376-04.2004.

Paolini, A. G. and McKenzie, J. S. (1993) ‘Effects of lesions in the horizontal diagonal band nucleus on olfactory habituation in the rat’, Neuroscience, 57(3), pp. 717–724. doi: 10.1016/0306-4522(93)90017-A.

Parker, A. J. and Newsome, W. T. (1998) ‘Sense and the single neuron: probing the physiology of perception’, Annu Rev Neurosci, 21, pp. 227–277. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=9530497.

Paxinos, G. and Watson, C. (1997) The Rat Brain in Stereotaxic Coordinates. 3rd edn. Academic Press.

Payton, C. A., Wilson, D. A. and Wesson, D. W. (2012) ‘Parallel Odor Processing by Two Anatomically Distinct Olfactory Bulb Target Structures’, PLoS ONE. Public Library of Science, 7(4), p. e34926. doi: 10.1371/journal.pone.0034926.

188

Van Der Pers, J. N. C. and Den Otter, C. J. (1978) ‘Single cell responses from olfactory receptors of small ermine moths to sex-attractants’, Journal of Insect Physiology, 24(4), pp. 337–343. doi: http://dx.doi.org/10.1016/0022-1910(78)90033-1.

Pestilli, F. and Carrasco, M. (2005) ‘Attention enhances contrast sensitivity at cued and impairs it at uncued locations’, Vision Research, 45(14), pp. 1867–1875. doi: 10.1016/j.visres.2005.01.019.

Petrulis, A. (2013) ‘Chemosignals, hormones and mammalian reproduction’, Hormones and Behavior, 63(5), pp. 723–741. doi: http://dx.doi.org/10.1016/j.yhbeh.2013.03.011.

Pfaffmann, C. (1957) ‘The afferent code for sensory quality’, The American Psychologist, 14(5), pp. 226–232.

Pinault, D. (2004) ‘The thalamic reticular nucleus: Structure, function and concept’, Brain Research Reviews, pp. 1–31. doi: 10.1016/j.brainresrev.2004.04.008.

Plailly, J. et al. (2008) ‘Attention to odor modulates thalamocortical connectivity in the human brain’, J Neurosci. 2008/05/16, 28(20), pp. 5257–5267. doi: 28/20/5257 [pii]10.1523/JNEUROSCI.5607-07.2008.

Poo, C. and Isaacson, J. S. (2009) ‘Odor Representations in Olfactory Cortex: Sparse Coding, Global Inhibition, and Oscillations’, 62(6), pp. 850–861. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0896627309003973.

Price, J. L. and Powell, T. P. (1970) ‘An experimental study of the origin and the course of the centrifugal fibres to the olfactory bulb in the rat.’, J Anat., 107(Pt 2), pp. 215–237.

Rampin, O., Bellier, C. and Maurin, Y. (2012) ‘Electrophysiological responses of rat olfactory tubercle neurons to biologically relevant odours’, European journal of neuroscience. Blackwell Publishing Ltd, 35(1), pp. 97–105. doi: 10.1111/j.1460- 9568.2011.07940.x.

Ravel, N. et al. (2003) ‘Olfactory learning modifies the expression of odour-induced oscillatory responses in the gamma (60-90 Hz) and beta (15-40 Hz) bands in the rat olfactory bulb’, Eur J Neurosci, 17(2), pp. 350–358. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=12542672.

Raz, A. and Buhle, J. (2006) ‘Typologies of attentional networks’, Nature Reviews Neuroscience, pp. 367–379. doi: 10.1038/nrn1903.

Regenbogen, C. et al. (2017) ‘Behavioral and neural correlates to multisensory detection of sick humans’, Proceedings of the National Academy of Sciences, 114(24), pp. 6400– 6405. doi: 10.1073/pnas.1617357114.

189

Rennaker, R. L. et al. (2007) ‘Spatial and Temporal Distribution of Odorant-Evoked Activity in the Piriform Cortex’, J. Neurosci., 27(7), pp. 1534–1542. doi: 10.1523/jneurosci.4072-06.2007.

Ressler, K. J., Sullivan, S. L. and Buck, L. B. (1993) ‘A zonal organization of odorant receptor gene expression in the olfactory epithelium’, Cell, 73(3), pp. 597–609. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=7683976.

Reynolds, J. H. and Chelazzi, L. (2004) ‘Attentional modulation of visual processing’, Annual Reviews in Neuroscience, 27, pp. 611–647.

Reynolds, J. H., Chelazzi, L. and Desimone, R. (1999) ‘Competitive mechanisms subserve attention in macaque areas V2 and V4.’, Journal of Neuroscience, 19(5), pp. 1736–1753. doi: 10.1523/jneurosci.4499-07.2008.

Reynolds, J., Pasternak, T. and Desimone, R. (2000) ‘Attention increases sensitivity of V4 Neurons’, Neuron, 26, pp. 703–714.

Rinberg, D., Koulakov, A. and Gelperin, A. (2006) ‘Sparse odor coding in awake behaving mice’, J Neurosci, 26(34), pp. 8857–8865. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=16928875.

Robbins, T. W. (2002) ‘The 5-choice serial reaction time task: Behavioural pharmacology and functional neurochemistry’, Psychopharmacology, pp. 362–380. doi: 10.1007/s00213-002-1154-7.

Roesch, M. R., Stalnaker, T. A. and Schoenbaum, G. (2007) ‘Associative encoding in anterior piriform cortex versus orbitofrontal cortex during odor discrimination and reversal learning’, Cereb Cortex. 2006/05/16, 17(3), pp. 643–652. doi: bhk009 [pii]10.1093/cercor/bhk009.

Rojas-Líbano, D. and Kay, L. (2008) ‘Olfactory system gamma oscillations: the physiological dissection of a cognitive neural system’, Cognitive Neurodynamics. Springer Netherlands, 2(3), pp. 179–194. doi: 10.1007/s11571-008-9053-1.

Rokni, D. et al. (2014) ‘An olfactory cocktail party: Figure-ground segregation of odorants in rodents’, Nature Neuroscience, 17(9), pp. 1225–1232. doi: 10.1038/nn.3775.

Rothermel, M. et al. (2014) ‘Cholinergic Inputs from Basal Forebrain Add an Excitatory Bias to Odor Coding in the Olfactory Bulb’, The Journal of neuroscience, 34(13), pp. 4654–4664. doi: 10.1523/jneurosci.5026-13.2014. Roux, S. G. et al. (2007) ‘A wavelet-based method for local phase extraction from a

190

multi-frequency oscillatory signal’, J Neurosci Methods, 160(1), pp. 135–143. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=17049617.

Ruff, D. A. and Cohen, M. R. (2016) ‘Stimulus Dependence of Correlated Variability across Cortical Areas’, Journal of Neuroscience, 36(28), pp. 7546–7556. doi: 10.1523/JNEUROSCI.0504-16.2016.

Ruxton, G. D., Sherratt, T. N. and Speed, M. P. (2004) ‘Avoiding Attack: The Evolutionary Ecology of Crypsis, Warning Signals and Mimicry’, Oxford biology, 17, p. 249. doi: 10.1093/acprof.

Sakata, S., Yamamori, T. and Sakurai, Y. (2004) ‘Behavioral studies of auditory-visual spatial recognition and integration in rats’, Experimental Brain Research, 159(4), pp. 409–417. doi: 10.1007/s00221-004-1962-6.

Salinas, E. and Sejnowski, T. J. (2001) ‘Correlated neuronal activity and the flow of neural information’, Nat Rev Neurosci, 2(8), pp. 539–550. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=11483997.

Samuelsen, C. L., Gardner, M. P. H. and Fontanini, A. (2012) ‘Effects of Cue-Triggered Expectation on Cortical Processing of Taste’, Neuron, 74(2), pp. 410–422. doi: 10.1016/j.neuron.2012.02.031.

Samuelsen, C. L., Gardner, M. P. H. and Fontanini, A. (2013) ‘Thalamic Contribution to Cortical Processing of Taste and Expectation’, The Journal of neuroscience, 33(5), pp. 1815–1827. doi: 10.1523/jneurosci.4026-12.2013.

San-Galli, A. and Bouret, S. (2014) ‘Assessing value representation in animals’, Journal of Physiology-Paris, (0). doi: http://dx.doi.org/10.1016/j.jphysparis.2014.07.003.

Sanbonmatsu, D. and Kardes, F. (1988) ‘The effects of physiological arousal on information processing and persuasion’, Journal of Consumer Research, 15(3), pp. 379– 385. doi: 10.1086/209175.

Sarter, M. et al. (2005) ‘Unraveling the attentional functions of cortical cholinergic inputs: interactions between signal-driven and cognitive modulation of signal detection’, Brain Res Brain Res Rev, 48(1), pp. 98–111. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=15708630.

Schwob, J. E. and Price, J. L. (1978) ‘The cortical projection of the olfactory bulb: development in fetal and neonatal rats correlated with quantitative variations in adult rats’, Brain Research, 151, pp. 369–374.

191

Schwob, J. E. and Price, J. L. (1984a) ‘The development of axonal connections in the central olfactory system of rats’, J Comp Neurol, 223(2), pp. 177–202. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=6200518.

Schwob, J. E. and Price, J. L. (1984b) ‘The development of lamination of afferent fibers to the olfactory cortex in rats, with additional observations in the adult’, J Comp Neurol. 1984/02/20, 223(2), pp. 203–222. doi: 10.1002/cne.902230205.

Scott, J. W., McBride, R. L. and Schneider, S. P. (1980) ‘The organization of projections from the olfactory bulb to the piriform cortex and olfactory tubercle in the rat’, J Comp Neurol, 194(3), pp. 519–534. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=7451680.

Shepherd, G. M. (1972) ‘Synaptic organization of the mammalian olfactory bulb’, Physiol Rev, 52(4), pp. 864–917. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=4343762.

Shepherd, G. M. (2005) ‘Perception without a thalamus: How does olfaction do it?’, Neuron, pp. 166–168. doi: 10.1016/j.neuron.2005.03.012.

Shipley, M. T. and Ennis, M. (1996) ‘Functional organization of olfactory system’, J Neurobiol, 30(1), pp. 123–176. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=8727988.

Solano-Flores, L. P., Aguilar-Baturoni, H. U. and Guevara-Aguilar, R. (1980) ‘Locus coeruleus influences upon the olfactory tubercle’, Brain Res Bull, 5(4), pp. 383–389.

Sorwell, K. G., Wesson, D. W. and Baum, M. J. (2008) ‘Sexually dimorphic enhancement by estradiol of male urinary odor detection thresholds in mice’, Behav Neurosci. 2008/08/30, 122(4), pp. 788–793. doi: 2008-09788-007 [pii]10.1037/0735- 7044.122.4.788.

Spence, C. et al. (2000) ‘Selective attention to the chemosensory modality’, Perception and Psychophysics, 62(6), pp. 1265–1271. doi: 10.3758/BF03212128.

Spence, C. et al. (2001) ‘Attention to olfaction. A psychophysical investigation’, Experimental Brain Research, 138(4), pp. 432–437. doi: 10.1007/s002210100713.

Spence, C. and Gallace, A. (2007) ‘Recent developments in the study of tactile attention’, Canadian Journal of Experimental Psychology, 61(3), pp. 196–207. doi: 10.1037/cjep2007021.

192

Spitzer, H., Desimone, R. and Moran, J. (1988a) ‘Increased attention enhances both behavioral and neuronal performance’, Science, 240(4850), pp. 338–340. doi: 10.1126/science.3353728.

Spitzer, H., Desimone, R. and Moran, J. (1988b) ‘Increased attention enhances both behavioral and neuronal performance’, Science, 240(4850), pp. 338–340. doi: 10.1126/science.3353728.

Spors, H. and Grinvald, A. (2002) ‘Spatio-temporal dynamics of odor representations in the mammalian olfactory bulb’, Neuron, 34(2), pp. 301–315. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=11970871.

Staubli, U., Le, T. T. and Lynch, G. (1995) ‘Variants of olfactory memory and their dependencies on the hippocampal formation’, J Neurosci, 15(2), pp. 1162–1171. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=7869092.

Stettler, D. D. and Axel, R. (2009) ‘Representations of Odor in the Piriform Cortex’, Neuron, 63(6), pp. 854–864. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0896627309006849.

Stevenson, R. J. (2009) ‘An initial evaluation of the functions of human olfaction’, Chemical Senses, pp. 3–20. doi: 10.1093/chemse/bjp083.

Stroop, J. R. (1935) ‘Studies of interference in serial verbal reactions’, Journal of Experimental Psychology, 18(6), pp. 643–662. doi: 10.1037/h0054651.

Sturgill, J. F. and Isaacson, J. S. (2015) ‘Somatostatin cells regulate sensory response fidelity via subtractive inhibition in olfactory cortex’, Nature Neuroscience, 18(4), pp. 531–535. doi: 10.1038/nn.3971.

Sugai, T. et al. (2005) ‘Odor-concentration coding in the guinea-pig piriform cortex’, Neuroscience, 130(3), pp. 769–781. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=15590159.

Sundberg, H. et al. (1982) ‘A method for studying responses and habituation to odors in rats.’, Behavioral and neural biology, 34(1), pp. 113–119.

Sundberg, K. A. et al. (2012) ‘Attention Influences Single Unit and Local Field Potential Response Latencies in Visual Cortical Area V4’, Journal of Neuroscience, 32(45), pp. 16040–16050. doi: 10.1523/JNEUROSCI.0489-12.2012. Takiguchi, N. et al. (2008) ‘Performance of mice in discrimination of liquor odors:

193

Behavioral evidence for olfactory attention’, Chemical Senses, 33(3), pp. 283–290. doi: 10.1093/chemse/bjm086.

Tan, Z. et al. (2018) ‘Dynamic ErbB4 Activity in Hippocampal-Prefrontal Synchrony and Top-Down Attention in Rodents’, Neuron, 98(2), pp. 380–393.

Tham, W. W. P., Stevenson, R. J. and Miller, L. A. (2009) ‘The functional role of the medio dorsal thalamic nucleus in olfaction’, Brain research reviews, 62(1), pp. 109–126. Available at: http://www.sciencedirect.com/science/article/B6SYS-4XBX70R- 1/2/19cc9615f8dcc6dd450edcf42ae320fb.

Tolhurst, D. J., Movshon, J. A. and Dean, A. F. (1983) ‘The statistical reliability of signals in single neurons in cat and monkey visual cortex’, Vision Research, 23(8), pp. 775–785. doi: 10.1016/0042-6989(83)90200-6.

Treisman, A. (1964) ‘Monitoring and storage of irrelevant messages in selective attention’, Journal of Verbal Learning and Verbal Behavior, 3(6), pp. 449–459. doi: 10.1016/S0022-5371(64)80015-3.

Treisman, A. M. (1969) ‘Strategies and models of selective attention.’, Psychological Review. US: American Psychological Association, pp. 282–299. doi: 10.1037/h0027242.

Treue, S. and Martinez Trujillo, J. C. (1999) ‘Feature-based attention influences motion processing gain in macaque visual cortex’, Nature, 399(6736), pp. 575–579. doi: 10.1038/21176.

Uchida, N. and Mainen, Z. F. (2003) ‘Speed and accuracy of olfactory discrimination in the rat’, Nat Neurosci, 6(11), pp. 1224–1229. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=14566341.

Vanderwolf, C. H. (1992) ‘Hippocampal activity, olfaction, and sniffing: an olfactory input to the dentate gyrus’, Brain Research, 593(2), pp. 197–208. Available at: http://www.sciencedirect.com/science/article/B6SYR-4834JH9- R3/2/ce8d38a95ac8e0efe74e7038307b5adf.

Vanderwolf, C. H. and Zibrowski, E. M. (2001) ‘Pyriform cortex beta-waves: odor- specific sensitization following repeated olfactory stimulation’, Brain Res, 892(2), pp. 301–308. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=11172777.

Varga, A. G. and Wesson, D. W. (2013) ‘Distributed auditory sensory input within the mouse olfactory cortex’, European journal of neuroscience, 37, pp. 564–571. doi: 10.1111/ejn.12063. Veit, L. and Nieder, A. (2013) ‘Abstract rule neurons in the endbrain support intelligent

194

Veldhuizen, M. G. and Small, D. M. (2011) ‘Modality-specific neural effects of selective attention to taste and odor’, Chemical Senses, 36(8), pp. 747–760. doi: 10.1093/chemse/bjr043.

Victor, J. D. and Purpura, K. P. (1997a) ‘Metric-space analysis of spike trains: theory, algorithms and application’, Network, 8, pp. 127–164.

Victor, J. D. and Purpura, K. P. (1997b) ‘Sensory coding in cortical neurons: Recent results and speculations’, in Annals of the New York Academy of Sciences, pp. 330–352. doi: 10.1111/j.1749-6632.1997.tb48640.x.

Vinck, M. et al. (2013) ‘Attentional Modulation of Cell-Class-Specific Gamma-Band Synchronization in Awake Monkey Area V4’, Neuron, 80(4), pp. 1077–1089. doi: 10.1016/j.neuron.2013.08.019.

Voigts, J. et al. (2013) ‘The flexDrive: an ultra-light implant for optical control and highly parallel chronic recording of neuronal ensembles in freely moving mice ’, Frontiers in Systems Neuroscience , p. 8. Available at: http://journal.frontiersin.org/article/10.3389/fnsys.2013.00008.

Wachowiak, M. (2011) ‘All in a Sniff: Olfaction as a Model for Active Sensing’, Neuron. Cell Press, 71(6), pp. 962–973. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0896627311007859.

Wachowiak, M. et al. (2013) ‘Optical Dissection of Odor Information Processing In Vivo Using GCaMPs Expressed in Specified Cell Types of the Olfactory Bulb’, The Journal of neuroscience, 33(12), pp. 5285–5300. doi: 10.1523/jneurosci.4824-12.2013.

Wachowiak, M. and Shipley, M. T. (2006) ‘Coding and synaptic processing of sensory information in the glomerular layer of the olfactory bulb’, Semin Cell Dev Biol, 17(4), pp. 411–423. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=16765614.

Wenk, H., Meyer, U. and Bigl, V. (1977) ‘Centrifugal cholinergic connections in the olfactory system of rats’, Neuroscience, 2(5), pp. 797–800. doi: 10.1016/0306- 4522(77)90033-1.

Wesson, D. W. et al. (2008) ‘Rapid encoding and perception of novel odors in the rat’, PLoS Biol. 2008/04/11, 6(4), p. e82. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=18399719.

195

Wesson, D. W. et al. (2011) ‘Sensory Network Dysfunction, Behavioral Impairments, and Their Reversibility in an Alzheimer’s beta-Amyloidosis Mouse Model’, The Journal of neuroscience, 31(44), pp. 15962–15971. doi: 10.1523/jneurosci.2085-11.2011.

Wesson, D. W. (2013) ‘Sniffing Behavior Communicates Social Hierarchy’, Current Biology. Cell Press, 23(7), pp. 575–580. Available at: http://linkinghub.elsevier.com/retrieve/pii/S096098221300153X.

Wesson, D. W. and Wilson, D. A. (2010) ‘Smelling Sounds: Olfactory-Auditory Sensory Convergence in the Olfactory Tubercle’, J Neurosci, 30(8), pp. 3013–3021.

Wesson, D. W. and Wilson, D. A. (2011) ‘Sniffing out the contributions of the olfactory tubercle to the sense of smell: hedonics, sensory integration, and more?’, Neurosci Biobehav Rev, 35, pp. 655–668.

White, L. E. (1965) ‘Olfactory bulb projections of the rat’, Anat Rec, 152, pp. 465–479.

Wiederman, S. D. and O’Carroll, D. C. (2013) ‘Selective attention in an insect visual neuron’, Current Biology, 23(2), pp. 156–161. doi: 10.1016/j.cub.2012.11.048.

Wilson, D. A. (2001) ‘Scopolamine enhances generalization between odor representations in rat olfactory cortex’, Learn Mem, 8(5), pp. 279–285. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=11584075.

Wilson, D. A. (2010) ‘Single-Unit Activity in Piriform Cortex during Slow-Wave State Is Shaped by Recent Odor Experience’, J. Neurosci., 30(5), pp. 1760–1765. doi: 10.1523/jneurosci.5636-09.2010.

Wilson, D. A. et al. (2011) ‘State-dependent functional connectivity of rat olfactory system assessed by fMRI’, Neuroscience Letters, 497(2), pp. 69–73. doi: http://dx.doi.org/10.1016/j.neulet.2011.04.031.

Wilson, D. A. and Stevenson, R. J. (2006) Learning to smell : olfactory perception from neurobiology to behavior. Baltimore: Johns Hopkins University Press. Available at: http://www.loc.gov/catdir/toc/ecip0519/2005027714.html.

Wilson, D. A. and Yan, X. (2010) ‘Sleep-like states modulate functional connectivity in the rat olfactory system.’, Journal of neurophysiology, in press.

Wilson, R. I. and Mainen, Z. F. (2006) ‘Early events in olfactory processing’, Annu Rev Neurosci, 29, pp. 163–201. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=16776583. Wimmer, R. D. et al. (2015) ‘Thalamic control of sensory selection in divided attention’,

196

Xia, C. Z., Adjei, S. and Wesson, D. W. (2015) ‘Coding of odor stimulus features among secondary olfactory structures.’, Journal of neurophysiology, 114(1), pp. 736–45.

Yamamoto, C. and Iwama, K. (1961) ‘Arousal reaction of the olfactory bulb’, The Japanese Journal of Physiology, 11(3), pp. 335–345. doi: 10.2170/jjphysiol.11.335.

Yantis, S. and Serences, J. T. (2003) ‘Cortical mechanisms of space-based and object- based attentional control’, Current Opinion in Neurobiology, pp. 187–193. doi: 10.1016/S0959-4388(03)00033-3.

Yost, W. A. (1997) ‘The cocktail party problem: Forty years later’, in Binaural and spatial hearing in real and virtual environments, pp. 329–348.

Youngentob, S. L. et al. (1991) ‘Odorant identification in rats: an update’, Physiol Behav, 49, pp. 1293–1296.

Yu, C. et al. (2018) ‘Theta Oscillations Organize Spiking Activity in Higher-Order Visual Thalamus during Sustained Attention’, eneuro, p. ENEURO.0384-17.2018. doi: 10.1523/ENEURO.0384-17.2018.

Zaborszky, L. et al. (1986) ‘Cholinergic and GABAergic afferents to the olfactory bulb in the rat with special emphasis on the projection neurons in the nucleus of the horizontal limb of the diagonal band.’, J Comp Neurol, 243(4), pp. 488–509.

Zelano, C. et al. (2005) ‘Attentional modulation in human primary olfactory cortex’, Nat Neurosci, 8(1), pp. 114–120. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citati on&list_uids=15608635.

Zhang, Z. et al. (2017) ‘Whole-Brain Mapping of the Inputs and Outputs of the Medial Part of the Olfactory Tubercle’, Frontiers in Neural Circuits, p. 52. doi: 10.3389/fncir.2017.00052.

197