Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

1. The Candidate A. CANDIDATE’S BACKGROUND My research focuses on how neurons create behavior and how behavioral experience alters these neurons and their interconnections. I prepared for this integrative approach to neural plasticity throughout my career. Since there was no undergraduate neuroscience major at the University of Texas at Austin when I attended, I created my own. I obtained a BA in Psychology and a BS in Zoology in three years, graduating with high honors in 1989. As an undergraduate with Dr. Abram Amsel, I studied the role of the hippocampus in single-patterned alternation, a simple operant task (Lobaugh et al., 1989). I realized that, to understand it, the brain must be studied in an integrative manner with an array of tools that offer insights from ion channel gating to circuit dynamics. I became intensely interested in the investigation of plasticity at all levels from molecules to behavior in the powerful Aplysia model. I pursued my interest in neural plasticity in Aplysia with Drs. Thomas Carew and Leonard Kaczmarek at Yale University, graduating with a Ph.D. in 1996. I utilized multiple electrophysiological methods, including extracellular, sharp micro-electrode intracellular, and whole-cell patch recording. I learned that the excitability of a group of neuroendocrine neurons (bag cell neurons) that control reproduction in Aplysia is tightly regulated during development, with the density of some ion currents up-regulated (e.g., potassium A- current and a calcium current induced by protein kinase C) and others down-regulated (e.g., delayed- rectifier potassium current) (Nick et al., 1996a; White et al., 1998). The overall effect of the modulation of ion currents was to decrease repetitive firing in neurons of juveniles, relative to adults. Since natural release of the egg-laying hormone required repetitive firing, and since the juvenile neurons could release egg-laying hormone when artificially stimulated to do so and the young animals could respond to the hormone with appropriate behaviors, I concluded that the modulation of expression of defined ion channels controlled the development of egg-laying behaviors in Aplysia (Nick et al., 1996b). In a side project, I found that a factor found in haemolymph could modulate ion current expression in the bag cell neurons and that brain-derived neurotrophic factor could mimic these effects. However, because Aplysia does not express vertebrate neurotrophins, the project was difficult to pursue without extensive groundwork on Aplysia growth factors. This led me to pursue extrinsic modulation of neuronal excitability in a vertebrate preparation, the Xenopus neuromuscular junction (NMJ). In the laboratory of Dr. Angeles Ribera at the University of Colorado Health Sciences Center, I utilized a variety of molecular techniques, including gene cloning and sequencing, to study potassium channel expression. To examine extrinsic modulation of excitability, I cultured embryonic Xenopus NMJs in defined medium. Using whole-cell and outside-out macropatch recording, I found that myocytes exert an extrinsic modulatory influence on the motor neurons that innervate them (Nick and Ribera, 2000). When synaptic communication was blocked with an acetylcholine antagonist, α-bungarotoxin, the motor neurons developed differently than if they were allowed to synaptically communicate with muscle. The Trk neurotrophin receptor inhibitor K252a mimicked these effects and neurotrophin-3 ameliorated a subset of them. While I found this line of research engaging, I wanted to study cells and synapses “in context”, in a functioning circuit with a clear role in behavior. About this time, several groups published findings that suggested that the birdsong system would be the perfect model for my research aspirations (Dave et al., 1998; Schmidt and Perkel, 1998; Schmidt and Konishi, 1998; Mooney, 2000). In Dr. Masakazu Konishi’s laboratory at the California Institute of Technology, I used chronic population recordings to examine state-dependent and developmental modulation of activity in the song nucleus HVC (this acronym is the proper name). I found that playback of the Bird’s Own Song (BOS) activates the adult HVC more than any other auditory stimulus during slow-wave sleep (Nick and Konishi, 2001). Because the adult response was state-dependent and because all previous investigations of HVC development had been executed in anesthetized juveniles, I reasoned that the examination of juvenile responses across behavioral states might reveal a neural signal that shapes behavior during song development. Konishi (1965) showed that young birds in the process of song learning (1) compare auditory feedback and a memory of a learned tutor song “template” and (2) use the result of this comparison to shape their behavior. I sought a neural signal in the song control system that indicated matching between an auditory stimulus and the learned tutor song. Indeed, Konishi and I found the instructive signal in the awake juvenile (Nick and Konishi, 2005a). - 1 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

I was attracted to the Department of Neuroscience and Center for Neurobehavioral Development at the University of Minnesota due to the enormous research breadth that would facilitate my integrative approach. During my short time here, I have already utilized the expert advice of numerous faculty, including Drs. David Redish (multi-electrode recording) and Glenn Giesler (antidromic techniques). I am now routinely recording from awake, behaving juvenile zebra finches using multi-electrode techniques. I have, for the first time, combined antidromic identification techniques and multi-electrode recording such that single neurons can be identified in an ensemble recording (see Preliminary Data). I have found that the temporal relationships between brain activity and behavior change during vocal learning. I have set up three chronic multi-electrode recording stations, a brain slice station, and an in vivo cellular recording station. An expert in chronic recording in birds, Dr. Naoya Aoki, will arrive in April 2006 to begin a postdoctoral fellowship in my laboratory that will focus on Aims 2 and 4 of this proposal. I will use my grant from The John Merck Fund for Dr. Aoki’s salary and fringe benefits. I am well trained and well equipped to pursue the mechanisms underlying vocal learning. I have laid the methodological foundation over the last two years to complete all Aims. The faculty and institution are committed to my success. Achieving my goals will require that I am able to commit a significant amount of time to this project, which is why I am applying for a K02 award.

B. CAREER GOALS AND OBJECTIVES: SCIENTIFIC BIOGRAPHY The primary function of the nervous system is to enable adaptation to the environment. Behavioral adaptation ultimately results from cellular plasticity. I have spent my career studying neural plasticity, from proteins to behavior. Vocal learning, considered by many laypersons to be uniquely human, represents an especially intriguing form of neural plasticity that involves the highest-order brain areas, which are least understood. My career goal is to break the neural code that underlies vocal learning and to understand, down to the ion channel, how vocal plasticity is orchestrated. As an undergraduate, I realized that the brain is an integrative organ. Information acquired across time through the organism’s interaction with the environment (behavior) is stored at the cellular or subcellular level. Systems and local circuits intervene both during storage and retrieval. To understand the entire process from behavior to molecules and back again, the entire process must be studied in a single system. Much understanding has been achieved with simple systems, such as the Aplysia gill withdrawal reflex and the crustacean stomatogastric ganglion. To learn the power and approach of simple systems, I worked with Drs. Thomas Carew and Leonard Kaczmarek on Aplysia at Yale University. I found the development of reproductive behaviors is ultimately controlled by the expression of ion channels in a population of neuroendocrine cells. While I greatly appreciate the contribution of such systems to our understanding of basic principles of neural function, I found that my own career path led towards the vertebrates, due to their kinship to humans and their greater potential for revealing both higher cognitive processes and clinically- relevant results. My time working with Aplysia gave me a systematic and rational approach for taking apart and understanding each piece of a behaving system. The next stage of my training was to investigate the effects of extrinsic modulators on intrinsic excitability. In Xenopus nerve-muscle co-cultures, I found that communication with the synaptic target was critical for normal development of motor neuron excitability. This experience emphasized to me that the developing nervous system is a dynamic interacting network that is constantly shaped by extrinsic influences, as well as intrinsic genetic programs. I further pursued extrinsic shaping of the nervous system by moving to the songbird vocal learning model. I was able to record from the same animal and recording site for extremely long time periods using chronic multi-unit recording methods. I monitored brain activity across the developmental period during which a zebra finch shapes his vocalization to match a learned tutor song. Konishi and I reported several major discoveries (Nick and Konishi, 2005a, b), including: (1) the song nucleus HVC (this acronym is the proper name) receives a tutor song template-matching signal during waking within a restricted period of development; (2) the nucleus HVC responds preferentially to the Bird’s Own Song during sleep throughout development, even when the song is little more than repeated begging calls; and (3) ongoing HVC activity is greater in juveniles than in adults. I was able to obtain these data because I recorded from awake, behaving finches, instead of anesthetized finches, in which all other developmental studies had been done. No one other than me has successfully executed longitudinal studies in the developing zebra finch song - 2 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. system because of the difficulty in maintaining recordings over several weeks. These findings laid the foundation for my current work. Although rigorously tested, my findings with Konishi remain controversial due to their conflicts with established interpretations of a large body of data. Specifically, many in the birdsong field believe that the tutor song template and all sites of neural plasticity lie downstream (efferent) of HVC, whereas our data indicate that the template lies upstream (afferent) of HVC. The Anterior Forebrain Pathway (AFP; downstream of HVC) clearly has a role in vocal plasticity (Bottjer et al., 1984; Brainard and Doupe, 2000). A large number of birdsong physiologists have interpreted these data to mean that the AFP (1) stores the tutor song memory (serves as the template store; see Troyer and Doupe, 2000b); (2) compares auditory feedback to the tutor song memory (serves as the comparator); and (3) changes behavior by shaping synapses between HVC and the premotor nucleus Robustus Arcopallialis (see Hahnloser et al, 2002). However, how can the AFP be the template store and the comparator if HVC, which is upstream of the AFP, receives a template-matching signal (Nick and Konishi, 2005a)? Although loops in the song system may provide a route back to HVC from the AFP, the reigning hypothesis (i.e., the AFP contains the template) is not the most parsimonious. Data from immediate early gene studies also suggest that the template store and comparator are upstream of HVC (Mello et al., 1992; Mello and Clayton, 1994; Stripling et al., 2001). Other data suggest that the AFP may induce variability in song behavior (Kao et al., 2005; Olveczky et al., 2005). Because finches shape their songs through trial-and-error, behavioral variability is as critical for song learning as a tutor song template. Thus, consistent with our data, the AFP has an important role in song learning that has little to do with the template. Although accumulating data support our finding that HVC receives a template-matching signal during development, the birdsong field has not fully rejected the hypothesis that song system neural plasticity occurs downstream of HVC. This skepticism, though reasonable, raises the bar for all work that will be published on this subject. In response to constructive criticism, I have begun to record from single neurons in the awake, behaving juvenile HVC. In addition, I have combined multi-electrode and antidromic identification techniques for the first time in any system to obtain the identity of single neurons that carry the instructive signal. The K02 award would allow me to continue these pivotal, but very difficult, studies. Without it, I will be forced to focus on easier and lower profile experiments that I will be able to publish in a shorter timeframe. During my career, I have learned a wide variety of approaches to neural plasticity. I have brought this breadth of experience to bear on the problem of vocal learning. Here I propose to extend the work described in my R01 application and use chronic recording from awake, behaving finches during vocal learning to (1) precisely characterize the instructive template-matching signal; (2) examine the effect of perturbation of auditory feedback during singing (using two distinct perturbations); (3) resolve the destination of the signal; and (4) systematically analyze brain activity-behavior relationships during vocal learning. Relative to the R01, Aim 2 is expanded and Aim 4 is new. I am poised to execute these experiments because I: (1) have the necessary expertise; (2) have developed the necessary methods; (3) have already trained laboratory personnel in the technically difficult methods; and (4) have hired an electrophysiologist with specific skills in chronic recording of awake, behaving birds, Dr. Naoya Aoki. This work will lead to a better understanding of vocal learning, sensorimotor integration, and behavioral critical periods.

C. CAREER DEVELOPMENT/TRAINING ACTIVITIES DURING AWARD PERIOD My research goal is to obtain an integrated understanding of vocal learning. I was awarded an R01 grant from the National Institute of Deafness and Other Communication Disorders to study the neural mechanisms of song learning in the zebra finch songbird. Toward this end, I have already done most of the necessary methodological groundwork, set up a productive finch breeding colony, trained my laboratory personnel in very sophisticated and difficult methods, and hired a postdoctoral fellow skilled in chronic electrophysiological recording of awake behaving birds. However, the R01 grant was cut both in terms of modules (2) and years (from 5 to 3 years). To avoid a gap in funding, I will need to apply for renewal of the grant in only 2 years. Renewal in this short timeframe will be difficult for four reasons: (1) I am recording (i) from the song nucleus HVC in awake juveniles and (ii) over several weeks from developing juveniles, neither of which has been achieved by anyone else in my field; (2) I am applying and optimizing multi- - 3 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. electrode and spike sorting methods to the song system for the first time; (3) I am combining multi-electrode and antidromic identification methods for the first time in any system; and (4) Because of the novelty of my approach, I have discovered that the reigning hypothesis of birdsong learning (i.e., the Anterior Forebrain Pathway contains the template) is not the most parsimonious. Future experiments are likely to further undermine this hypothesis, which, as should be expected, leads my colleagues in birdsong to hold my work to a very high standard. Thus, I believe that my situation demonstrates the need for a period of intensive research focus as a means of enhancing my research career. The Independent Scientist Award (K02) would assist me in establishing my laboratory and reputation in the vocal learning field in several ways: 1. The K02 would allow more time to hone my new skills in multi-electrode recording and further optimize the application of spike sorting analysis to the song nucleus HVC with assistance from my colleague, David Redish, and to maximize the reliability of my method that combines multi-electrode, spike sorting and antidromic techniques. These methods are very difficult and thus require significant time to develop, implement, optimize, and train. 2. The K02 would enable me to apply the sophisticated methods that I have optimized for the song system over the last two years to test specific hypotheses regarding the mechanisms of vocal learning. Although most of the groundwork on the methodology has already been done, I will need a sustained period of concentrated research time to execute the proposed studies and provide concrete results. My time for active research in the laboratory would be protected by the K02. In my department, teaching and service are expected to increase to ~30% time during the first four years as an assistant professor. Should I receive this award, I could limit my teaching and service to 10% and conduct research for the other 90% of my time. 3. It would decrease the negative effects of a potential gap in funding. Without this award, my department would require that I make up for lost salary dollars by increased teaching and service over and above the 30% expected without funding, which would decrease my chances of later renewal. 4. In addition to bench work, the protected time would enable enhancement of my career potential by enabling increased collaboration with colleagues both at the University of Minnesota and elsewhere, presentation of my work at more scientific meetings, and visits to colleagues and collaborators at other institutions, such a Dr. Stephanie White, who will show me an anesthesia technique that may decrease my juvenile mortality rate.

D. TRAINING IN THE RESPONSIBLE CONDUCT OF RESEARCH The University of Minnesota has a Responsible Conduct of Research program that facilitates training in scientific integrity, policies concerning the treatment of research subjects, and bioethics. As a new faculty, I was required to attend two helpful workshops on the Responsible Conduct of Research. Throughout the training period, I will attend at least one training session per year. My postdoctoral and graduate trainees will be required to attend two workshops on Responsible Conduct of Research. In addition, all of my trainees are aware that I have a zero-tolerance policy for mistreatment of animals and falsification of data. In addition to research, I expect to participate in the following activities: 1. Teaching (5%) I expect to teach in the Developmental Neuroscience module of the summer Itasca laboratory course for incoming graduate students. This allows me to form close relationships with other developmental faculty, with whom I routinely discuss details of my studies and receive feedback, and also provides an excellent opportunity for recruiting graduate students, who may be drawn to participate in the proposed research. I will also teach five lectures in the Behavioral Neuroscience course, which allows close interaction with behavioral neuroscientists and keeps me at the forefront of the literature of closely related fields, such as bat echolocation, which informs the proposed research. 2. Service (2%) I will serve on the Colloquium Committee of the Graduate Program in Neuroscience. This provides an excellent opportunity to stay abreast of the wide array of neuroscience research occurring at the University of Minnesota. 3. Continuing Education (3%) I will attend at least two seminars per week, which will keep me informed of cutting-edge research both at my home institution and elsewhere. I will also attend journal clubs on Computational Neuroscience, Developmental Neuroscience, and Neural Plasticity. I will attend meetings of the Society for Neuroscience and the International Society for Neuroethology when they occur. I will take “mini-sabbaticals” to learn key techniques, such as a new anesthesia technique from Dr. Stephanie White. - 4 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

2. Statement by Consultant

Letters from Drs. David Redish and Stephanie White.

- 5 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

3. Environment and Institutional Commitment to Candidate

A. DESCRIPTION OF INSTITUTIONAL ENVIRONMENT

Dr. Teresa Nick proposes to unravel the mechanisms of vocal learning by taking an integrative electrophysiological approach. The University of Minnesota has strengths at every stage of analysis that she will pursue. We have a large Department with 33 faculty members and a substantial number share Teresa’s research interests. She has already established a collaborative relationship with Dr. David Redish who has developed several methods that are necessary for triode recording including the most widely used software package for data analysis. David’s lab has a weekly Computational Journal Club that focuses on neural coding, which is quite relevant to Teresa’s work. Teresa has already begun to identify neurons within the song nucleus HVC using antidromic methods. Dr. Glenn Giesler has served as a consultant on these studies. He has more than 30 years of experience using antidromic activation methods. The developmental group within the Neuroscience Department, which includes Paul Letourneau, Steve McLoon, Lorene Lanier, Yasushi Nakagawa, and Naoko Koyano, was actively involved in recruiting Teresa and has already provided a great deal of feedback on her research and ideas. The developmental group is active and highly engaged. They frequently come together, for example in their weekly journal club, to discuss ideas and approaches to understanding the developing nervous system. Each of these investigators is available to help Teresa with her research. The Center for Neurobehavioral Development, which includes Michael Georgieff and Kathleen Thomas, is also greatly interested in Teresa’s continuing success and, reflecting this interest, has given her full-member status in the Center. This group is actively involved in translational research and provides a great pre-clinical resource. Birdsong is a sensorimotor behavior, and we have strengths in both sensory perception and motor control. There is a strong auditory neuroscience group that includes Mark Bee, Neal Viemeister, Bert Schlauch, Peggy Nelson, Andrew Oxenham, and Lance Zirpel. Mark Bee has been meeting with Teresa regarding her exploration of auditory feedback in the song system. Members of our large and renowned motor group include: James Ashe, Martha Flanders, Apostolos Georgopoulos, John Soechting, and myself. As Teresa begins to take aspects of her work to the cellular level, we have faculty to assist her there as well, including Paul Mermelstein, Bob Miller, Eric Newman, Mark Thomas, and LiLian Yuan. Many of our faculty our interested in neural plasticity and actively discuss new work in the Neural Plasticity Journal Club. The Graduate Program in Neuroscience is an excellent resource. There is a weekly Neuroscience Colloquium that showcases the work of faculty and students here at the University of Minnesota and a Neuroscience Seminar Series that hosts neuroscientists from around the world. The President of the University of Minnesota, Robert Bruinicks, has recently shown his enthusiasm for neuroscience at the University by providing support for a Presidential Symposium on Neuroscience, which will occur in the Fall of 2006 and feature twelve eminent neuroscientists. In recognition of the breadth of her understanding of neuroscience, I appointed Teresa to chair the organizing committee for the Symposium. She has done an outstanding job. For her research, Teresa requires an aviary facility for an active breeding program. I arranged for a large facility to set aside for the aviary prior to her arrival. As she has expanded her program, a second room has been set up as a colonial breeding facility. The Research Animal Resources at the University has worked closely with Teresa to ensure that she has an aviary producing healthy subjects at top capacity. Teresa requires a large amount of computational resources for her work. She has recently received an award from the Super Computing Institute at the University, which allows her to utilize a super computer to process her multi-electrode data. Dr. Shuxia Zhang from the Institute has worked with Teresa to parallelize her computer code such that it runs up to 8 times faster on the super computer. In recognition of the required office space for her computers, I recently moved her to a larger office that is closer to the colleagues with which that she actively collaborates, including the developmental group, David Redish, and Glenn Giesler. We will continue to offer Teresa an environment that will facilitate and enhance her research.

- 6 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

B. INSTITUTIONAL COMMITMENT TO CANDIDATE’S RESEARCH CAREER DEVELOPMENT

The Department of Neuroscience at the University of Minnesota is delighted to enthusiastically support the application of Dr. Teresa Nick for a KO2 award. We believe that she is a perfect candidate. She is remarkably intelligent, dedicated and creative. She has had ideal training with several of the most prominent leaders in the field (Drs. Thomas Carew, Leonard Kaczmarek, Angeles Ribera, and Masakazu Konishi) at Yale, University of Colorado, and Cal Tech. She has been consistently productive, publishing exclusively in top-flight journals. Teresa works in the important area of development of song in birds, the most powerful animal model of development of speech in humans. We believe that Teresa will become a star in the field. The Department has already invested substantially in Teresa’s career and will continue to do so. Teresa arrived here in January of 2004 and was given a substantial start-up package and excellent lab space. She has already been productive in her new laboratory and is about to submit her first manuscript. She received support from the John Merck Scholars Foundation in 2005, which she will use for a postdoc salary, and recently she was awarded an RO1. As an Assistant Professor in the Department of Neuroscience, Teresa’s primary responsibility is to establish an internationally respected research reputation and laboratory. She has already made several major breakthroughs in her chosen field, which have initiated a reconsideration of established hypotheses regarding the development of birdsong behavior and reinterpretation of a large amount of previously acquired data. Because of the groundbreaking nature of her research, it has been consistently held to a very high standard. Moreover, the techniques that she uses are cutting edge. She is the first to apply an assortment of techniques to the birdsong system. This has greatly extended the publication time of her work. While this is to be expected, it occurs at a critical juncture in her career. A K02 Independent Scientist Award would enable continuation of her research during this vulnerable period. In recognition of the K02 award, I would be able to reduce Teresa’s total teaching and administrative load to 10%, allowing 90% time for research. The proposed limited teaching and administrative responsibilities that I approve complement her research goals, creating potential for graduate student recruitment and increasing interactions with key faculty. The K02 would also protect Teresa’s time for research in the unlikely event that her R01 is not renewed. The Department will certainly help her financially if she is unable to immediately renew her R01. Funds will be provided so that she can continue her work during any un- funded period. The Neuroscience Department, Medical School, and Graduate School each have programs for bridge funding. To further assist in Teresa’s career development, we are happy to support her attendance of at least two scientific meetings per year and her efforts to take “mini-sabbaticals” to learn new, important techniques in her field. We encourage our junior faculty to invite at least one speaker per year to our seminar series to enhance their interaction with colleagues in their field. In summary, I am committed to the continued career development of Teresa Nick as an independent neuroscientist. She is an outstanding young faculty at a critical point in her career. She needs a period of sustained and concentrated effort to execute her pivotal studies in vocal learning. The K02 would enhance her potential and facilitate her ability to make significant contributions to our understanding of a key model of speech.

______DATE:______

Timothy Ebner, M.D., Ph.D. Professor and Head Department of Neuroscience Visscher Chair of Physiology

- 7 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

4. Research Plan A. SPECIFIC AIMS Songbirds and humans are rare among animals in that they possess specialized sensorimotor brain areas dedicated to vocal learning and production (Doupe & Kuhl, 1999). The zebra finch songbird acquires its song by first memorizing a model song from a tutor and then matching its own vocalizations through auditory feedback to the memory trace of the tutor song, called the template (Konishi, 1965). The Template Theory, based on behavioral data, requires that the auditory feedback/template comparison sculpt the brain circuitry that drives singing. Yet, after 40 years, the key physiological evidence is missing, namely that a neural representation of the tutor song affects singing behavior. In recordings from waking juvenile finches, we recently showed that the premotor song nucleus HVC (this acronym is the proper name) is selectively tuned to the tutor song in juveniles (Nick and Konishi, 2005a), which suggests that the template is located afferent to HVC. Yet, the most popular model (Troyer and Doupe, 2000b) proposes that the template is located efferent to HVC. The proposed research will clarify the relative location of the template, test the hypothesis that the neural tuning observed in the HVC of young finches is an instructive template-matching signal, and begin to illuminate the role of this neural tuning in song development. The long-term goals of this project are to track and manipulate the putative matching signal through time and brain space, ultimately revealing fundamental principles of brain processing, such as how sensation and memory shape motor activity and behavior. Specific aims are: 1. Examine the precision of neural tuning of all HVC neurons in awake, unrestrained finches during song development. Previous reports of HVC neural tuning to the tutor song relied on neural population data (Nick and Konishi, 2005a). Recording single neurons will (1) reveal if all HVC neurons or only a subset respond to tutor song and (2) enable a fine-grained analysis of stimulus selectivity in all HVC neurons. Thus, this aim specifically assesses the auditory stimulus selectivity of single HVC neurons during the template-matching phase of song development. Auditory tuning of individual HVC neurons of awake unrestrained juvenile finches will be investigated with the techniques of song playback, multi-electrode recording and song analysis. My hypothesis predicts that the magnitude of the response of individual HVC neurons will be directly proportional to the degree of similarity between the stimulus and the tutor song. 2. Determine the activity of tutor song selective neurons during singing. The Template Theory predicts that a template-matching signal occurs during singing. This aim will focus on identifying the putative matching signal during singing in developing finches with multi-electrode techniques. Experiments consisting of a single recording session will: (1) use playback of auditory stimuli to identify the tutor song selective neurons; (2) examine the activity of these specific neurons during singing; and (3) perturb auditory feedback using two independent methods to test the hypothesis that the activity of these neurons during singing depends on auditory signals. These experiments will test if auditory feedback occurs during singing in HVC neurons that respond selectively to tutor song. 3. Resolve the destination of the putative matching signal. HVC contains three major subtypes of neurons that can be identified by their projections: those that project to the basal ganglia, those that project to the motor cortex analog, and interneurons. A corticothalamic-basal ganglia loop (the Anterior Forebrain Pathway) has roles in song learning (Bottjer et al., 1984), suggesting that HVC neurons afferent to the basal ganglia may be especially important in learning. This aim will identify which HVC subtype(s) carries the putative matching signal and thus show where the signal is transmitted. Multi-electrode recording in awake unrestrained juveniles, combined with antidromic techniques, will identify the neurons that respond selectively to tutor song and the destination of the putative matching signal. My hypothesis predicts that only neurons that project to the basal ganglia respond selectively to tutor song. 4. Test a specific mechanism through which the template-matching signal may shape behavior. The problem of synaptic delays between motor command and sensory feedback poses severe constraints on any sensorimotor system. Known cellular mechanisms of activity-dependent plasticity require close

- 8 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. temporal association of action potentials in neurons that exhibit activity-dependent strengthening (i.e., neurons that fire together, wire together). Therefore, I propose a hypothesis that would enable temporal overlap of motor command and sensory feedback: Higher order motor circuits (in HVC) that drive vocalization are active longer relative to the vocalization in juveniles than adults, allowing sensory feedback to shape synaptic connectivity via activity-dependent mechanisms. I will test this hypothesis by comparing HVC neuronal population activity during song behavior across development. My hypothesis predicts that HVC activity will outlast the vocalization in actively learning finches, but not in adults.

B. BACKGROUND AND SIGNIFICANCE While the majority of children develop complex speech abilities by the age of four years with no formal instruction, there are a large number of cases where speech is delayed or never acquired. Even when conditions such as hearing loss were excluded, more than one million of the students in U.S. public school special education classes were diagnosed with speech or language impairments (US Dept. Education, (2002). Many of these children had communication disabilities such as autism. In fact, an estimated ten percent of all Americans exhibit speech and language delay (Law et al., 2000). Unfortunately, these problems persist in those with a history of language and speech impairment, as language performance exhibits considerable stability over many years (Johnston et al., 1999). Appreciable movement towards treatment and cure of these disorders will require an understanding of the fundamental biological principles of vocal learning. Potential animal models for the investigation of speech and language development are rare. No primate other than Homo sapiens learns its vocalizations (Doupe and Kuhl, 1999). The other groups of mammals that are vocal learners, cetaceans (whales and dolphins), elephants, and bats, are difficult to study physiologically during vocal learning. Three types of birds are vocal learners: hummingbirds, parrots, and songbirds (Nottebohm, 1972; Baptista and Schuchmann, 1990). Of these, the songbirds have been studied in the most detail, both behaviorally and physiologically (Konishi, 1985; Nottebohm et al., 1990). Development of song behavior The development of song behavior dramatically resembles the development of human speech (Doupe and Kuhl, 1999). Notably, both humans and songbirds have two critical periods of development: (1) a perceptual or sensory phase during which species-typical vocalizations are learned and (2) a sensorimotor phase during which the juvenile’s vocalization is shaped through auditory feedback (Konishi, 1965). The Template Theory provided a seminal contribution to our understanding of vocal learning in songbirds (Konishi, 1965). Briefly, during the sensory phase, a memory or template of the song of a tutor is formed. Although the neural mechanisms of memory formation and storage are unknown, they may involve selective neural tuning to specific stimuli or the fine-tuning of an innate perceptual filter (Marler and Peters, 1977; Konishi, 1985). During singing in the sensorimotor phase, the Bird’s Own Song (BOS) is shaped by comparison of the template and auditory feedback or, if you will, by the passage of auditory feedback through the template perceptual filter (Fig. 1). In the model below, the hypothetical neural signal resulting from the passage of auditory feedback through the template filter will be referred to as the “template- matching signal” or “matching signal”. In summary, behavioral experiments indicate that (1) a memory (template) of the tutor song exists and (2) auditory feedback is compared to the memory during singing to produce a matching or error signal that shapes song behavior during the sensorimotor phase (Konishi, 1965).

Figure 1. A simple schematic of the sensorimotor phase of vocal learning in songbirds.

- 9 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

A forebrain pathway with clear roles in song learning Each brain hemisphere has one set of specialized nuclei that control song, collectively called the song system (Nottebohm et al., 1976). Although the nucleus HVC projects directly to the nucleus Robustus Arcopallialis (RA), which drives the brainstem nuclei that produce singing, another pathway from HVC to RA exists, the Anterior Forebrain Pathway (AFP; gray nuclei in Fig. 2). The importance of the AFP in song development was clearly shown by lesioning a song nucleus in the AFP during different stages of vocal learning (Bottjer et al., 1984). Briefly, in zebra finches, the magnocellular nucleus of the anterior nidopallium (MAN, which includes the lateral MAN, LMAN) received bilateral electrolytic lesions. Finches lesioned 35 – 50 days post-hatching sang abnormal songs for the rest of their lives. Interestingly, finches aged 55 – 65 days sang songs that were disrupted according to the status of their song before lesioning: Those with no recognizable song pattern resembled the 35 – 50 day finches, singing abnormal songs thereafter. In contrast, those finches with a recognizable song exhibited no immediate song disruption. The song of adult birds was stable for as long as 5 weeks after lesioning, indicating that, in terms of several weeks, MAN has no role in song production or in song maintenance in the adult. (Subsequent experiments have shown that LMAN enables adult song plasticity over a period of many weeks in the deafened adult (Brainard and Doupe, 2000).) Collectively, these data indicate that MAN has a critical role in the shaping of song during development (Bottjer et al., 1984). These authors keenly observed that the time course of the efficacy of MAN lesioning (~35 days to the date of emergence of a recognizable song pattern) did not match the time course of dependence on auditory feedback (~35 – 90 days; the sensorimotor phase)(Bottjer et al., 1984). This suggests that (1) the early sensorimotor phase selectively involves the AFP and (2) the behaviorally defined ‘sensorimotor phase’ may actually be divided into more than one neural processing phase.

Figure 2. A simplified schematic of the neural song system of the zebra finch. A subset of nuclei and connections are shown. The Anterior Forebrain Pathway is shown in gray.

Little evidence of a template-matching signal within the song system and the problem with anesthesia Extracellular and intracellular recordings from anesthetized adult songbirds have consistently shown that song nuclei respond selectively to the Bird’s Own Song (BOS)(Margoliash, 1983; Mooney, 2000). No widely accepted argument explains why BOS is the best song system stimulus during sleep and anesthesia, although the authors of one study speculated that song preference for BOS is related to rehearsal of song during sleep (Dave and Margoliash, 2000). BOS is also the most effective activating stimulus during anesthesia in developing songbirds (Volman, 1993; Doupe, 1997; Solis and Doupe, 1997, 1999, 2000). The overall picture painted by these studies suggests that song nuclei are not selective for any song in very young anesthetized birds (30 days in finches), but these nuclei become selective for BOS through development. By 60 days, the majority of neurons in nuclei of the Anterior Forebrain Pathway are activated most by BOS under anesthesia, while others respond more to tutor song or equally well to both tutor song and BOS (Solis and Doupe, 1999). If the recorded activity is a template-matching signal, it should be most activated by tutor song, not BOS. However, tutor song was not the overall most effective stimulus in any developmental study in anesthetized birds. What is the impetus for change? Where is the matching or error signal that indicates whether the auditory input matches or mismatches the template? This apparent paradox may be explained by the fact that the anesthetized song system behaves differently than the waking song system. Indeed, in adult finches, the song system exhibits strikingly different activity patterns - 10 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. depending on anesthesia and sleep/wake state (Dave et al., 1998; Schmidt and Konishi, 1998; Nick and Konishi, 2001). Recent studies indicate that the song selectivity measured in juvenile finches is also different during waking compared to anesthesia (Nick and Konishi, 2005a). Thus, song preference should be examined in waking juveniles. Learning studies in adults versus juveniles Previous research has examined song learning in adult zebra finches (Dave and Margoliash, 2000). In addition, studies in adults have suggested that the mechanisms of song learning are called into play during adult plasticity (Brainard and Doupe, 2000). Indeed, perturbation of auditory feedback clearly illustrates the plasticity of adult song behavior (Nordeen and Nordeen, 1992; Leonardo and Konishi, 1998), but plasticity is not equivalent to learning. Learning, by definition, involves the ACQUISITION of skills or knowledge. Zebra finches provide an extreme example of overtraining: they sing the same series of sounds hundreds to thousands of times every day throughout their adult lives. There is little evidence that adult zebra finches can learn new song material, especially at the dramatic rates evinced by juveniles (Zevin et al., 2004). All adult “learning” data are also consistent with a more parsimonious model of song MAINTENANCE. Thus, to understand song learning, song acquisition in juveniles must be examined. Single neuron recordings Examination of the activity of single neurons in the song system has relied on single wire recordings that were subjected to a simple amplitude threshold (Dave and Margoliash, 2000; Hahnloser et al., 2002). Signal-to-noise ratio traditionally has been used as a measure of single-unit quality, although it is rarely reported and subject to error. Since the cells of HVC occur in clusters, the likelihood of recording multiple units is high. Ultimately, with simple thresholding of extracellular data, the distinction between multi-unit and single-unit data is in the eye of the beholder. High impedance sharp wire electrodes required to monitor single-unit activity are impractical for recordings more than a few minutes in freely moving animals because movements of the electrode may damage the cell or cause the electrode to move away from the cell (Harris et al., 2000). Song researchers have been able to achieve some success in recording from freely moving animals with single wire electrodes under the control of miniaturized motorized microdrives (Fee and Leonardo, 2001). However, the use of the ~1.5 g microdrive in juvenile finches < 60 days old, which possess thin, flexible skulls and weak neck musculature, may not be feasible. In addition, NO evidence indicates that neurons can be recorded for longer than a few seconds with single-wire electrodes in singing finches. There are two major reasons that electrophysiologists desire stable recordings: (1) to have time to do the appropriate controls, and (2) to be sure that the observed activity is not an aberration caused by the destabilization of the neuronal plasmalemma by the mechanical pressure of the electrode. Neurons will die within a few seconds if they are firing robustly due to mechanical pressure. However, if the neuron is held only for a few seconds, distinguishing aberrant from normal activity is impossible. Multi-electrode arrays are routinely placed under the control of lighter weight manual microdrives and produce data of exceptional quality (Wilson and McNaughton, 1993; Harris et al., 2000; Redish et al., 2001; Buzsaki, 2004). The manual microdrive that will be used in the proposed experiments weighs ~400 mg (< 1/3 the weight of the Fee microdrive). Another drawback of the single wire recording technique is that only one cell can be reliably recorded at a time, which is not the case with triode recordings. Triodes enable the use of larger wires, which improve mechanical stability, and provide greatly improved methods for the sorting of spikes from the same cell, based on the temporal coherence of activity across all channels (Harris et al., 2000). Moreover, even the best single wire records exhibit error rates that are almost twice those of a three-wire multi-electrode (Harris et al., 2000). Collectively, these benefits of multi-electrodes over single wire electrodes argue for the use of multi-electrodes in developing zebra finches. State-dependence of song preference in the juvenile HVC Initial efforts to find the matching signal in waking finches focused on the premotor nucleus HVC (Nick and Konishi, 2005a). HVC is the ideal nucleus to receive template-matching information, since it (1) receives and transmits auditory signals to both RA and the AFP and (2) drives singing (e.g., stimulation of HVC during singing ‘resets’ the song (Vu et al., 1994)). Strikingly, neural population data reveal that the HVC of young finches (35 – 69 days) exhibits state-dependence of song preference: playback of BOS - 11 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. activates HVC most during sleep, as in adults, whereas the tutor song activates HVC most during waking (Nick and Konishi, 2005a). HVC exhibits neural tuning to the tutor song only during waking and only during the specific period of development (early sensorimotor phase) during which the finch matches his own song to the song of the tutor. The observed neural tuning thus appears to be a template-matching signal. Linking the template-matching signal to changes in behavior The nervous system exists to enable adaptation to the environment through changes in behavior. In the case of simple reflexes, sensory input induces a motor action. For more sophisticated behaviors, such as walking and talking, sensory feedback is used to precisely guide the motor action. The on-line perceptual guidance of actions requires that sensory inputs and motor commands interact continuously within the nervous system through sensorimotor integration. Many current hypotheses of neural modulation rely on the mechanisms of activity-dependent synaptic plasticity (electrical activity-induced change in the strength of connections between neurons). Activity-dependent plasticity requires simultaneous electrical activity in the two neurons that are connected (i.e., neurons that fire together, wire together). This poses a problem for the processing of sensorimotor signals in the central nervous system, because there are temporal delays between the transmission of the motor command and the occurrence of sensory feedback due to the relatively large number of processing stages (Fig. 3A). By the time the sensory feedback reaches the brain area that transmitted the motor command, the motor activity has already ceased.

Figure 3. Schematic of neural sensorimotor activity and the issue of temporal delays. (A) In the adult zebra finch song system, motor command activity and sensory feedback do not appear to coincide. (B) Coincidence of motor and sensory firing may occur during song learning through increased excitability in the motor command (pre-vocal) neurons. In B, the motor command may not continue to induce behavior due to the failure of transmission in the pathway that intervenes between the command and the muscle. Consistent with this hypothesis, pre-vocal activity outlasts the vocalization during song learning (see Preliminary Data).

The development of birdsong, which parallels human speech, provides an ideal model in which to investigate activity-dependent mechanisms of sensorimotor learning. It is estimated that there is a lag of at least 60 ms between the firing of pre-vocal (motor command) neurons and the sensory response in these same neurons. One model of birdsong learning that addresses the problems of sensorimotor learning suggests that motor to sensory mapping is learned via activity-dependent synaptic strengthening across this 60-ms delay (Troyer and Doupe, 2000a). Yet, other studies indicate that synaptic strengthening requires a delay of 20 ms or less (Bi and Poo, 2001). Thus, during the initial construction of the song system, a significant time gap must be closed to enable proposed activity-dependent associations to occur. Long duration action potential bursts in pre-vocal motor neurons would bridge the time gap between motor command and sensory feedback (Fig. 3B). Mark Konishi and I recently discovered that the levels of spontaneous activity in a key pre-vocal song nucleus are significantly greater during the sensorimotor phase of song development (Nick and Konishi, 2005a), during which song behavior is shaped by auditory feedback (Konishi, 1965). These data suggest the hypothesis that increased song system excitability during the sensorimotor phase allows coincident activation of both motor command and sensory feedback pathways, which enables activity-dependent modulation of the brain substrates of song behavior. Aim 4 will test a key prediction of this hypothesis. - 12 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

Tracking the engram: The matching signal in song development The electrophysiological studies of the song system over the last 20 years have focused almost exclusively on anesthetized finches (e.g., Gentner and Margoliash, 2003; Rosen and Mooney, 2003), while very few have examined waking finches (McCasland and Konishi, 1981; Hahnloser et al., 2002). Recent studies conclusively show that the activity during anesthesia and sleep by no means reflects activity during waking (Dave et al., 1998; Schmidt and Konishi, 1998; Nick and Konishi, 2001). These findings compel the birdsong field to rethink our understanding of neurophysiology in this system. We have already identified a putative matching signal expressed during waking in the developing HVC (Nick and Konishi, 2005a). The aims described in this proposal will (1) examine the information contained in the putative matching signal (Does it convey a graded signal of similarity to tutor song at the level of single neurons?); (2) test the function of the putative matching signal in template-matching (Does it occur during singing?); (3) elucidate its underlying mechanisms in shaping the developing song system (Is it sent to the Anterior Forebrain Pathway?); and (4) test a mechanism through which the template matching signal may shape behavior using well-known mechanisms of activity-dependent plasticity (Do temporal aspects of brain-behavior relationships change during vocal learning?).

C. PRELIMINARY STUDIES Section 1. Quantification of spike cluster quality The primary advantage of multi-electrode over single electrode recording is the reliability of the results. In single electrode recordings, spikes of a single unit are usually identified using a single parameter (amplitude threshold) arbitrarily selected by the experimenter. Multi-electrode data relies on multiple parameters that describe the spike amplitude as well as spike shape across more than one electrode. This decreases the likelihood of errors, such as obtaining only a subset of the activity of a unit or concluding that the activity of multiple units is only that of a single unit. Sophisticated computer algorithms such as KlustaKwik (by K. Harris) cluster multiple events in a multi-dimensional space, with each dimension reflecting a different spike parameter. However, there is no widely used method of quantifying how well a cluster of events (presumably reflecting the activity of a single neuron) is separated from other events. Quantification of spike cluster quality (i.e., how reliably the data represent all of the spiking activity of one neuron and only one neuron) will be achieved using methods established by my collaborator David Redish (Schmitzer-Torbert et al., 2005). Using measures of energy (L2-Norm; related to amplitude, see below) and the first principal component coefficient from all three electrodes, I will calculate the L-ratio, a measure of the amount of noise around a multi-dimensional cluster, and the Isolation Distance (I.D.), a measure of how distant a given cluster is from the overall noise distribution. I will use the following thresholds for inclusion in further analyses: L-ratio must be less that 0.05; Isolation Distance must be more than 16. In addition, I will ensure that there are no changes in the firing pattern of the cell (under similar conditions) over the duration of the experiment (~ 4 hrs), which would suggest that more than one cell or that only part of the activity of one cell has been clustered. I will also inspect the interspike interval for each clustered neuron. Interspike intervals of less than 200 μsec will suggest that more than one cell has been clustered, since all neurons have a refractory period during which another spike cannot be generated. An example of a spike cluster from HVC that I analyzed with spike sorting techniques is shown in the Figure 4. These data show that the activity of single neurons in awake, behaving juvenile zebra finches can be stably recorded and quantitatively analyzed using multi-electrode recording and spike sorting techniques.

- 13 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

Figure 4. Voltage spikes in HVC from three electrodes were clustered in 15 dimensions. The two dimensions shown in this figure are L2-Norm (a measure of energy of the trace) on electrodes 1 and 3. The LEFT panel shows a contour plot of the densities of all of the spiking events that occurred during the recording session. The RIGHT panel shows the scatter plot of points in the same dimensions. The red cluster is well-separated (indicating a single unit), given the L-ratio and Isolation Distance (ID), whereas the green cluster is not acceptable for further analysis. A sample of the raw data that were analyzed for these plots is shown in Fig. 5.

Section 2. Analysis of auditory responses in single neurons during singing Aim 2 examines the activity of HVC neurons during singing. A critical first step in these experiments involves production of song in the presence of masking noise. Comparable numbers of songs are produced by a juvenile finch under masking noise and silent conditions (Fig. 5; masking noise, N = 7; silence, N = 11). I now provide preliminary evidence of auditory feedback in the zebra finch during sensorimotor learning. Recordings were made from a single HVC neuron for over 4 hours. This neuron produced action potentials that were similar in amplitude and spiking pattern to tutor song selective neurons that Konishi and I have previously observed (large signal-to-noise for an extracellular recording with relatively large diameter wire; Nick and Konishi, 2005b). Spike-sorting techniques were used to evaluate the activity of a single neuron in the HVC of a juvenile finch (data from the same neuron shown in Figs. 4 - 6) during singing (3 exemplar songs are shown in Fig. 5) and during non-vocal periods. Ongoing activity of the single neuron did not change with presentation of masking noise (Fig. 6A). This is an important control to assess nonspecific auditory effects that are not related to auditory feedback. In contrast, during singing, activity of this neuron significantly decreased with masking noise compared to singing without masking noise (Fig. 6B). Since the total auditory stimulation actually increased during masking noise, these data suggest that the subset of HVC activity during singing that was decreased by masking noise was selective for the sound produced. The activity during singing of the single HVC neuron described above may have been auditory, motor, or a combination of both. My data suggest that at least part of the activity depended on auditory feedback. To thoroughly investigate the issue of whether the changes in the HVC neuron’s activity were auditory or motor-related, several controls were employed: control and masking noise songs were interleaved (changes induced during a masking noise period would be measured during the next song produced during a control period and vice-versa, taking all but the most instantaneous changes out of the equation) and the songs produced under noise and no noise conditions were compared with regard to amplitude and similarity. I measured the root mean square of the song amplitude between 5.5 – 6.5 kHz, over which masking noise was not played. If there were a simple, direct relationship between HVC activity and relative song amplitude, the activity and amplitude should change in the same direction, since increased HVC premotor activity may be required for increased song amplitude and increased song amplitude may further activate HVC through auditory feedback. To the contrary, there was no significant difference between song amplitude during noise and no noise conditions (RMS μV: Noise: 702.7 ± 10; Control: 653.6 ± 7, p = 0.34), with a trend in the opposite direction of what would have been predicted based on a simple relationship

- 14 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. between song amplitude and HVC activity. These data suggest that the decrease in HVC activity with masking noise reveals HVC activation by SELECTIVE auditory feedback.

Figure 5. Juvenile finches sing during masking noise as well as during silence. Increased neural activity in the brain area HVC accompanies song production, as in adults. TOP: Spectrogram of sound in the acoustic chamber temporally aligned with neural recordings below (high sound intensity indicated by red color). Masking noise (15 sec, 80 dB) was preceded and followed by silent periods (a window was left in the noise at 5-7 kHz to enable song identification). The juvenile finch sang both during masking noise and silence. The gray bars indicate the timing of the vocalization for comparison to the BOTTOM three panels: Recordings from HVC using a three-wire multi-electrode. Raw voltage

recordings from each electrode are shown separately.

Figure 6. Masking noise has differential effects depending on whether the finch is singing. (A) Activity of a single HVC neuron is not affected by noise if the finch is not singing (mean ± sem; p = 0.38, N(15 sec intervals) = 7,7). (B) Activity of the same neuron during singing is significantly decreased during masking noise compared to no noise (*t-test, p = 0.05; N(songs) =

11,7).

Song similarity was compared using the Sound Analysis Pro software developed by Tchernichovski and colleagues (2000). The band of frequencies between 5.5 – 6.5 kHz was compared between noise and control songs. Noise – control comparisons yielded percent similarities that were not different from control – control comparisons (% Similarity: Noise-control, 86.4 ±0.3 Control-control: 80.0 ± 1.3; p = 0.25, t-test). Collectively, these data indicate that the song produced does not change during noise playback. This finding suggests that the changes in activity observed in HVC are AUDITORY and not motor-related. If masking noise induced changes in motor activity and performance (i.e., influenced learning), then the control songs and corresponding motor activity should have changed from the beginning to the end of the session. To the contrary, during the session there was no significant change in HVC activity during song production in control periods (Spike Rate (Hz), First 5 control songs: 69.5 ± 1.3; Last 5 control songs: 75.8 ± 1.1, N.S.). In summary, my controls for motor-dependent versus auditory-evoked activity include: (1) interleaving masking noise and silence to obtain songs under control and experimental conditions during the same time periods; (2) comparison of noise/no-noise activity during non-vocal periods as well as singing; (3) comparison of song similarity and amplitude; and (4) comparison of activity during singing under control conditions from the beginning and end of the recording session. I have also increased control in these experiments by not using a female to elicit song. Although singing rate in the absence of a female is relatively low, I will be able to collect enough data from a single neuron to rigorously analyze due to the stability of the multi-electrode recording technique. My study is different from previous studies in several key areas. (1) I study much younger finches (50-65 days post-hatching). Previous studies focused on older finches (>70 days) that had passed the period of - 15 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. greatest plasticity. (2) I analyze undirected song (no female present). Previous studies used a female to elicit song (directed song) or did not record whether or not a female was present. Data indicate differential brain activity during directed and undirected singing (Jarvis et al., 1998; Hessler and Doupe, 1999). In addition, both directed and undirected songs are produced in the presence of a female, which increases variability and decreases control. (3) Although fewer songs are emitted in the absence of a female, my recording technique (multi-electrode recording) is stable enough to allow many songs to be recorded (see below). Previous studies used single-electrode recording, which is much less mechanically stable than the multi-electrode bundle (Harris et al., 2000) and, thus, enables only short-term recordings. Playback of tutor song tended to induce more activity in this neuron than conspecific song, although the difference was not significant (Fig. 7). For Aim 2, I will play back a minimum of 50 songs to clarify this issue.

Figure 7. The single HVC neuron shown above may be more active during playback of tutor song (A) than conspecific song (B)(Spike rate during N playback trials (Hz); Tutor: 4.2 ± 0.3, N = 29; Conspecific: 2.0 ± 0.2, N = 30; t-test, p = 0.22). Sonograms are shown temporally aligned above the recordings from the three-wire multi-electrode in HVC. Scale bar: 200 μV, 300 msec.

Section 3. Identification of single neurons in an ensemble recording Initial experiments in the zebra finch song system revealed that the very power of the multi-electrode technique, namely the recording of groups of neurons, interfered with the identification of antidromically- activated spikes. I antidromically stimulated song nucleus Area X (basal ganglia) and recorded from its afferent, nucleus HVC. HVC has three main subtypes of neurons: those that project to Area X, those that project to robustus arcopallialis (a premotor song area), and interneurons. The Area X-projecting HVC neurons are of particular interest because Area X and the other nuclei of the Anterior Forebrain Pathway are known to have roles in song plasticity (Bottjer et al., 1984; Brainard and Doupe, 2000). Often, antidromic stimulation of the target nucleus resulted in a burst of activity or a large stimulus artifact in the recorded nucleus, such that the antidromically-stimulated spike amplitude and waveform was obscured. Since spike sorting of multi-electrode data relies on relatively constant spike parameters, the problem was significant. However, a defining test of antidromicity provided the solution: orthodromic-antidromic spike collision (Fig. 8). Collision of a spontaneous orthodromic spike with the antidromic spike shows that the neuron that produces the orthodromic spike projects to the stimulated area. Thus, instead of using the obscured antidromic spike for my analysis, I used the spontaneous orthodromic spike. In the pre-stimulus time frame, the orthodromic spike is not obscured by antidromically triggered bursts or stimulus artifacts. It has as a high signal-to-noise ratio as any other recorded spikes and can thus be co-clustered with behaviorally- relevant spikes. Figure 8. Two key properties of antidromicity. The HVC voltage recordings in A-C are all aligned relative to the antidromic stimulus. Scale bar: 600 μV, 10 msec. (A) 15 antidromically-evoked spikes have a relatively constant latency. Inset: Higher temporal resolution of the 15 antidromic spikes reveals low latency variability. Scale bar: 600 μV, 0.5 msec. (B) A single antidromic spike for comparison with C. (C) A collision event. The orthodromic spike begins at the soma and collides with the evoked spike such that the antidromic spike does not reach the soma and is therefore not recorded. - 16 - Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

For this example, all data during a 9-hour recording period were clustered. Four high-quality (L-Ratio < 0.05; Isolation Distance > 16) clusters were obtained. Spikes that occurred within the collision time window prior to stimulation were identified from each cluster (Fig. 9). Orthodromic spikes in clusters 1,2, and 4 did not collide with the spike evoked by Area X stimulation (arrows, Fig. 9 C,D,F). In contrast, orthodromic spikes in cluster 3 routinely resulted in collisions (asterisks, Fig. 9 E), indicating that the unit in cluster 3 projected to Area X.

Figure 9. Identification of an Area X-projecting HVC neuron in the waking juvenile using combined multi-electrode and antidromic techniques. Four of six clustering parameters are shown. Energy (A) and Principle Component 1 (B) are shown for electrodes 1 and 2. In C-F, electrodes 1 and 2 are shown temporally aligned. Three instances during which spontaneous orthodromic spikes occurred during the collision time window preceding the stimulus are shown for each cluster. The spontaneous orthodromic spike is indicated by a gray box outlined by the color of the cluster shown in A and B. The stimulus artifact is indicated by a dotted line in the middle of each trace. In C, D, and F, antidromic spikes occurred (arrows). In E, collisions occurred (asterisks), which indicate that cluster 3 contained the activity of an Area-X projecting neuron. Scale bar: 600 μV, 10 msec.

Section 4. Developmental modulation of brain-behavior relationships in the song system I used multi-unit recording to monitor the activity of a population of HVC neurons for prolonged time periods during development. I found that vocal-associated activity is developmentally modulated. Figure 10 shows recordings from a birds that was recorded in my laboratory as a juvenile in the process of song learning and as an adult after song learning (crystallization). The adult HVC shows typical premotor activity, with activity beginning ~20 msec prior to vocalization and ending ~20 msec prior to the end of vocalization. In contrast, juvenile HVC activity is not well-correlated with vocalization, beginning well before or after the typical adult activity start time and lasting tens to hundreds of milliseconds beyond the vocalization. These data are consistent with the hypothesis that increased song system excitability during the sensorimotor phase allows coincident activation of both motor command and sensory feedback pathways, which enables activity-dependent modulation of the brain substrates of song behavior.

PHS 398/2590 (Rev. 09/04) Page 17 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

Figure 10. Repeated recordings across development reveal modulation of brain activity-behavior relationships. Population recordings were obtained from the same bird and the same electrode (Orange 298) during the late sensorimotor phase (LEFT; 65 days) and during adulthood (RIGHT; 104 days). Five simple calls from each age are shown with HVC activity overlaid to show temporal relationships. HVC activity outlasts the vocalization in the juvenile, but not the adult. Scale bar: 100 μV; 100 msec. D. RESEARCH DESIGN AND METHODS The songbird presents the ideal model for examining both vocal learning and fundamental mechanisms of forebrain control of behavior in vertebrates. To learn its song, the zebra finch must first memorize the song of a tutor and then match its own vocalizations to the memory trace, or template, through auditory feedback (Konishi, 1965). Shaping of the behavior requires that the results of the comparison of memory and auditory input (the template-matching signal) ultimately impact the forebrain premotor song circuitry, the nuclei HVC (this acronym is the proper name) and RA (Robustus Arcopallialis). With Mark Konishi, I recently reported the first evidence of a template-matching signal in the song control nucleus HVC (Nick and Konishi, 2005a). Specifically, the tutor song activates HVC more than any other auditory stimulus during waking in juveniles (Nick and Konishi, 2005a). The response to the Bird’s Own Song (BOS) appears to reflect the degree to which BOS resembles the tutor song (Nick, 2004), further strengthening the hypothesis that the HVC response during waking reveals a template-matching signal. The proposed experiments will use the putative matching signal to begin to unravel the brain mechanisms of vocal learning in songbirds (Table II). The studies described below will test the hypothesis that the observed selective activity functions as a matching signal during song development, as well as determine key mechanistic bases of the signal. These experiments have been designed to avoid many of the complicating factors associated with previous studies. Specifically, the proposed studies will utilize: (1) awake finches, in preference to sleeping or anesthetized finches, to study the neural bases of a behavioral process (template-matching) that occurs during waking; (2) juvenile finches, in preference to adults, to study song learning; and (3) multi-electrode (triode) and multi-unit recording, in preference to single wire single-unit recording, to stably assess the activity of single neurons or populations over extended periods. Toward accomplishing the stated goals, I have already obtained preliminary data using the powerful techniques of multi-electrode recording (Recce

PHS 398/2590 (Rev. 09/04) Page 18 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. and O'Keefe, 1989; Wilson and McNaughton, 1993) and antidromic stimulation (Ranck, 1975). In addition, an expert on multi-electrode recording, A. David Redish, will assist with these experiments.

Table II. A brief summary of the research plan. Aim Questions Techniques Preliminary Informative data? regardless of outcome? 1 Do single HVC neurons indicate the multi-electrode recording Yes Yes degree of stimulus similarity to tutor auditory playback song? 2 Does the putative matching multi-electrode recording Yes Yes signal occur during singing? auditory playback song recording 3 What HVC neurons carry the signal? multi-electrode recording Yes Yes Where is the signal transmitted? spike collision 4 Do temporal aspects of brain-behavior multi-unit recording Yes Yes relationships change during vocal song recording learning? These experiments are technically challenging due to the age of the animals (their skulls are soft and they often fail to recover after surgery) and the fact that the animals are awake and unrestrained. To my knowledge, only one group other than ours has successfully recorded chronically from the brain of the juvenile zebra finch (34 – 60 days), and they met with limited success (Adret et al., 2005). All other studies have utilized anesthetized finches and/or finches >63 days. My success rate for recording from HVC during surgery in juveniles is ~100%. However, ~40% of juveniles die within 2-3 days after surgery. In general, those that survive the first 3 post-operative days survive to adulthood. Of the juvenile finches that survive, my own preliminary data indicate that a third exhibit high-quality recordings from HVC (recordings that yield single units as defined by my strict criteria). Aim 3 requires that, in addition to these successes, the deep nuclei Area X and RA must be stimulated. In adults, the volume of Area X is approximately 1.8 cubic millimeters, whereas the volume of RA is approximately 200 cubic microns (Both values were calculated from E. Akutagawa’s zebra finch brain atlas). These nuclei are smaller in juveniles. Thus, the experiments described below are very labor intensive with regard to data acquisition. In addition, spike- sorting data analysis is time and labor intensive. Although these experiments are very difficult, I am confident in my ability to execute all four aims, due to my extensive experience with electrophysiological techniques both in the developing song system (Nick and Konishi, 2004, 2005b) and in other developing neural systems (Nick et al., 1996a; Nick and Ribera, 2000).

Aim 1. Examine the precision of neural tuning of single HVC neurons in awake, behaving finches during song development. Hypothesis: During waking in the early sensorimotor phase, individual HVC neurons are selectively activated by stimuli that resemble the tutor song. Predictions: The magnitude of the response of individual HVC neurons will be directly proportional to the degree of similarity of the given stimulus to the tutor song. It is well-accepted that the singing finch uses the comparison of auditory feedback and a tutor song memory to shape its song during development (Konishi, 1965), but neural evidence to corroborate this behavioral hypothesis has not been found. During the early sensorimotor phase, passive listening to playback of the tutor song selectively activates neurons within the song nucleus HVC (Nick and Konishi, 2005a). HVC neurons respond more to tutor song than to any other auditory stimulus, including the Bird’s Own Song (BOS) and songs of other adult male zebra finches. Moreover, the selective response to tutor song occurs only during the developmental phase when the song is sculpted to match the tutor song

PHS 398/2590 (Rev. 09/04) Page 19 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

(template-matching period/early sensorimotor phase/35 – 69 days post-hatching). The fundamental hypothesis of these studies proposes that this selective activity is a “matching signal” resulting from the comparison of incoming auditory signals and the tutor song memory or template. Measuring the putative matching signal at the level of single neurons is a critical step towards understanding the development of the song system. The putative matching signal may be either all-or-none or graded. The most parsimonious hypothesis proposes that the matching signal is graded, since this would allow the bird to sing a series of successive approximations toward the tutor song before arriving at the final version of his own song. In general, behavioral experiments support the idea of gradual shaping of the song during development (Immelmann, 1969; Tchernichovski et al., 2001), although one study suggests that fast transitions of the spectral properties of single syllables can occur (Tchernichovski et al., 2001). Knowing if the putative matching signal is graded or all-or-none will reveal a key aspect of the neural mechanisms of song learning. EXPERIMENTAL PLAN Individual HVC neurons of juvenile zebra finches in the early sensorimotor phase will be recorded with chronically implanted triodes under the control of a manual microdrive (Figs. 11 – 12). Various auditory stimuli, including multiple copies of the tutor song and the Bird’s Own Song (BOS), will be played back to the finch. Due to the requirement of several presentations of each stimulus and the large number of stimuli, these experiments will extend over several hours and, thus, would not be feasible using single-wire extracellular recording techniques. Spike sorting techniques (based on the temporal coherence of spiking events across the three electrodes of the triode) will allow analysis of the activity of individual HVC neurons. HVC neurons that respond selectively to the tutor song in preference to all other stimuli will be identified. The activity of these neurons will then be further examined for information content. Specifically, the magnitude of the response (number of action potentials) of the tutor song selective HVC neurons will be compared to the similarity between the given auditory stimulus and the tutor song. These experiments will reveal if the waking HVC response indicates similarity between the stimulus and the tutor song.

Figure 11. Schematic of the chronic recording apparatus. Figure 12. Schematic of the chronic electrode and microdrive implant. CONTROLS (Where relevant, these controls also apply to Aims 2 and 3.) Quantification of spike cluster quality. Quantification of spike cluster quality (i.e., how reliably the data represent all of the spiking activity of one neuron and only one neuron) will be achieved using methods established by my collaborator David Redish (Schmitzer-Torbert et al., 2005). For all clusters, the L-ratio, a measure of the amount of noise around a multi-dimensional cluster, and the Isolation Distance, a measure of how distant a given cluster is from the overall noise distribution, will be calculated from measurements of energy and the first principal component from all three electrodes. The following thresholds will be used for inclusion in further analyses: L-ratio must be less that 0.05; Isolation Distance must be more than 16. In addition, I will ensure that there are no changes in the firing pattern of the cell (under similar conditions) over the duration of the experiment (~ 4 hrs), which would suggest that more than one cell or that only part of the activity of one cell has been clustered. I will also inspect the interspike interval for each clustered neuron. Interspike intervals of less than 200 μsec will suggest that more than one cell has been clustered, since all neurons have a refractory period during which another spike cannot be generated.

PHS 398/2590 (Rev. 09/04) Page 20 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

Playback of a consistent song type. The general category of song that is played back to the finch is an important parameter to consider when studying behavior and neurophysiology. Data indicate that (1) songs directed to a female and undirected songs differ with regard to several parameters and (2) directed and undirected song are associated with differential activation of the brain (Jarvis et al., 1998; Hessler and Doupe, 1999). This proposal will utilize undirected song produced in the absence of a female for playback. Confirmation of stimulating and recording electrode location. All recorded finches will be anesthetized with pentobarbital and perfused with saline and 2% paraformaldehyde. Brains will be sunk in sucrose and sliced on a cryostat (10 μm slices). Tissue slices will be stained using cresyl violet and examined and photographed with a Axioskop microscope (Zeiss, Germany) to confirm electrode localization in HVC (recording electrode), near HVC (reference electrode), in Area X (stimulating electrode), and in RA (stimulating electrode). EXPERIMENTAL METHODS Animals and surgery. The same set of chronically implanted finches will be used for aims 1-3. 200 zebra finches will be reared on a 12/12 light cycle in the aviary. To ensure that the male parent serves as the sole song tutor, finches will be reared in acoustic attenuation chambers (Eckel Acoustic, Cambridge, MA). At the age of 44 - 46 days post-hatching, finches will be chronically implanted with an electrode assembly and microdrive. Finches will be weighed and anesthetized with 5 μL/g body weight of the following: ketamine hydrochloride (3 mg/mL) and xylazine hydrochloride (1.5 mg/g). Multi-electrodes, microdrive, and video monitoring. Finches will be recorded 50 – 65 days post- hatching. The implant will consist of the following: 2 stimulating tetrodes, a reference triode, a recording triode, and a ground electrically connected to a 10-pin nanoconnector. For recording, triodes will be used instead of tetrodes because (1) they provide almost as much confidence as a tetrode (that a putative single unit is actually a single neuron)(Harris et al., 2000); and (2) several major pieces of equipment in the laboratory are designed to process 4 recordings simultaneously, which allows for recording of the 3 wires of the triode and a reference electrode. For recording, triodes and a reference electrode will be made of 12 μm nichrome-formvar wire (Kanthal Palm Coast, FL). All three wires of the triode and the reference electrode will be referenced to an animal ground (100 μm bare silver wire). Differential electrode recordings will be obtained by referencing the triodes within HVC to a reference electrode that will be placed in a less active brain area outside of HVC. Triode and reference recordings will be unity-gain amplified with a quad op-amp (Texas Instruments, Dallas, TX) connected directly to the 10-pin connector (Omnetics) on the bird’s head (Schmidt and Konishi, 1998; Nick and Konishi, 2001). An 11-channel mercury commutator (Herb Adams Engineering, Pasadena, CA) will enable reliable recording while the bird moves freely about a small Plexiglas cage. A headstage controller (Caltech Biology Electronics Shop, Pasadena, CA) will power the headstage amplifier and facilitate differential recording. Each of the electrode signals from the triode and the reference electrode will be passed to a 4-channel AC amplifier (A-M Systems) for amplification (1000x) and filtering (300 – 10,000 Hz). These signals will then be passed, along with recorded sound, to a National Instruments (Austin, TX) PCI card via a BNC interface. Sounds will be recorded with a high quality omnidirectional microphone (Earthworks SRO, Milford, NH). Data collection software for song and electrode data was custom-written in LabView (National Instruments) per my specifications (Datafleet, Minneapolis). Playback experiments will begin approximately 1 hour after the sound attenuation chamber lights are turned on, during the bird’s normal lights-on cycle. All birds will be presented with sets of 50 - 100 trials consisting of multiple stimuli, in random order: silence, the tutor song (3-4 copies), conspecific song (3-4 copies with varying similarity to the tutor song), heterospecific song (from a Bengalese finch), white noise (3 sec), and BOS (3-4 copies). To test if the putative matching signal is graded, multiple versions of the current BOS, as well as older versions of BOS, will be played back. The intertrial recovery period will last 15-s. Song recording and playback. For song recording, male zebra finches will be isolated in acoustic attenuation chambers. Songs will be recorded, digitized, and played back at 44,100 Hz. Songs will be edited with MATLAB (Mathworks, Natick, MA). Undirected (song not directed at a female) and directed PHS 398/2590 (Rev. 09/04) Page 21 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

(song directed at a female) songs differ in terms of consistency and speed of delivery (Sossinka and Bohner, 1980; Bischof et al., 1981). When a female is present, songs may be undirected as well as directed. Thus, all songs for playback will be recorded with no female present (undirected) to control for this variation. Analysis of Song Playback Data. Song similarity will be scored with Sound Analysis (Tchernichovski et al., 2000). Electrophysiological data will be analyzed with MATLAB functions included with the MATLAB software or written by a member of the Nick laboratory or A. David Redish (MClust; see below). A MATLAB function will automatically discard trials with movement artifacts (revealed by thresholding of large deflections of the reference electrode record) and trials during which the bird vocalizes. Vocalizations will be identified by subtraction of a filtered amplitude envelope of an exemplar trial from a filtered amplitude envelope of the trial under consideration. Any increases in sound amplitude at any point in the trial, from 1- sec preceding the trial to 20-msec after the playback stimulus ends, will lead to rejection of the trial. As in prior finch studies (Dave et al., 1998; Dave and Margoliash, 2000; Rauske et al., 2003), I will use behavioral criteria to determine wake state. To be classed as ‘awake’, the birds will have to have open eyes and feathers that lay close to the body and are not ruffled. Multi-electrode (triode) recording is superior to single electrode recording, which has traditionally been used in birdsong research. Triodes provide more mechanical stability, an ~50% lower error rate, and the ability to record from more than one neuron simultaneously (Harris et al., 2000). Individual neurons can be identified in a set of adjacent multi-unit records with the techniques of spike sorting or ‘clustering’. Spike sorting relies on the fact that each action potential waveform can be described by a set of parameters (e.g., spike height, first principal component). In addition, these parameters will be different depending on the spatial location of each electrode relative to the neuron, which enables an even larger parameter set for each spike event. The parameter vector for each spike can be plotted in a multi-dimensional space. The points describing the spikes of a given neuron tend to cluster in multi-dimensional space (McNaughton et al., 1983). For all experiments, the activity of individual neurons (the number and timing of action potentials during song playback) will be determined using the MClust suite of spike sorting/clustering functions written in MATLAB (MClust written by A.D. Redish). The chief author of this software, A. David Redish, has agreed to assist me with this analysis (Schmitzer-Torbert et al., 2005). Selectivity will be determined by comparing the number of action potentials during tutor song playback to the number of action potentials during playback of other stimuli. A one-way ANOVA (with a post-hoc Tukey- Kramer) will compare the number of action potentials during each stimulus across stimuli. Cells with significantly more action potentials during tutor song playback will be considered ‘selective’. These cells carry the putative matching signal. To examine the graded or all-or-none nature of responses, the number of action potentials in response to a given stimulus will be plotted against the similarity of that stimulus to tutor song. Scatter plots thus constructed for each neuron, as defined by spike sorting, will be fit with a linear regression. Goodness of fit will assess the graded nature of the response. Aim 2. Identify and manipulate the activity of tutor song selective neurons during singing. Hypothesis: The putative matching signal occurs during singing. Predictions: HVC recording during singing will reveal activity in the same neurons that respond passively to tutor song. Further, the singing-associated activity of these neurons will be eliminated by perturbation of auditory feedback. The Template Theory proposes that a matching signal during singing sculpts the developing song (Konishi, 1965). If the observed HVC neural tuning to the tutor song is a template-matching signal, then (1) the neurons that selectively respond to tutor song should fire during singing, and (2) perturbation of auditory feedback should decrease their activity (the putative matching signal) during singing. Reversible auditory feedback will be perturbed with two distinct methods: masking noise and paralysis of the middle ear muscles with lidocaine injections. Masking noise can be used as an immediate and reversible means of perturbing auditory feedback. By virtue of its random frequency and time relationship, masking noise obscures

PHS 398/2590 (Rev. 09/04) Page 22 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. concomitant sounds. Behavioral data already indicate that masking noise interferes with auditory feedback during singing (Iyengar and Bottjer, 2002; Zevin et al., 2004). The proposed experiments will test the hypothesis that auditory feedback occurs during singing in the developing zebra finch with particular focus on the HVC neurons that respond to playback of the tutor song. Moreover, this aim will show whether the putative matching signal for sensory and template information occurs within the premotor nucleus HVC during song behavior. Previous studies concluded that perturbation of auditory feedback with masking noise does not affect HVC activity in adult zebra finches (Kozhevnikov and Fee, 2003, 2004). The proposed experiment would be executed in juvenile zebra finches, that (1) are clearly more plastic in terms of both normal behavior (Immelmann, 1969; Tchernichovski et al., 2001) and behavior following perturbations such as deafening (Konishi, 1965) and lesioning (Bottjer et al., 1984); and (2) exhibit the putative matching signal (Nick and Konishi, 2005a). The proposed study specifically focuses on the neurons that respond passively to tutor song playback in the wake state. The study will be informative irrespective of the outcome. EXPERIMENTAL PLAN The goal of this study is to assess the presence of a matching signal in the sensorimotor nucleus HVC during singing. To accomplish this goal, I propose to identify neurons (single units identified by spike sorting) that respond to passive playback of the tutor song and then compare, within the same individual neurons, activity during singing with and without perturbation of auditory feedback. I will compare HVC activity during normal singing (with normal auditory feedback) and HVC activity during singing with auditory feedback signals obscured (with masking noise or middle ear paralysis)(Fig. 13). If the aim were to assess auditory signals, per se, then this experiment would not make sense, since auditory signals will still impact the cochlea. In fact, there will be more auditory information than without masking noise. However, the underlying hypothesis proposes that the only signals that make it through a “template filter” are those that are similar to the tutor song. Obscuring the auditory signal prevents it from passing through the filter. Thus, there will be no matching signal if the obscured auditory signal does not match the memorized tutor song. The proposed matching signal only occurs in a narrow developmental window: the template-matching period (during early sensorimotor phase; ~50 – 65 days post-hatching). Thus, this experiment will only be performed on these young animals. Juvenile finches have thin, flexible skulls and weaker neck musculature, compared to adults. Thus, I will not attempt to employ single-unit recording with motorized microdrives, as several other birdsong groups are currently applying in adult finches (Fee and Leonardo, 2001). Instead, I will use triode recording, which will allow the use of lighter, manual microdrives. Triodes provide a means for recording the same group of neurons for longer periods of time than is possible with single-unit techniques because triodes (1) are more mechanically stable than a single wire and (2) enable the tracking of a cluster of characteristics of a given unit over time, even as the electrode makes slight movements (Harris et al., 2000).

Figure 13. Simple schematic of the experiment described in Aim 2, executed during the early sensorimotor phase. Masking noise will obscure auditory feedback and, according to the hypothesis, prevent the template filter from generating a matching signal. HVC, a premotor song nucleus, will be recorded using multi-electrode techniques. The hypothesis predicts that less HVC activity will be recorded during perturbation than during normal singing. CONTROLS Nonspecific effects of masking noise. Activity of each single neuron will be compared between noise and no noise for both singing and non-singing conditions to evaluate nonspecific auditory effects of noise. PHS 398/2590 (Rev. 09/04) Page 23 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

Changes in song during the recording session. Perturbation of auditory feedback may influence the song produced in two ways: (1) instantaneous feedback plasticity, such that the finch sings differently because he cannot hear himself; and (2) maintained plasticity, such that the finch retains a “memory” of the previous song that was subjected to auditory feedback at least long enough for the memory to affect production of the next song. Instantaneous feedback plasticity will be monitored by comparing the song similarity and amplitude of song produced during silence and during masking noise. A window will be made in the masking noise between 5 – 7 kHz such that song similarity and amplitude can be monitored. Maintained plasticity will be monitored through comparison of the songs produced and corresponding activity during silence throughout the 4-hour recording session. Preliminary Data indicate that neither of these forms of plasticity in song production occur during the time course of the experiment. Changes in song between recording sessions. Changes in song production may require a period of sleep or quiet waking for consolidation of plasticity induced during the recording session. Thus, data will NOT be compared across days. We have already shown that there is a sensitive period in development for expression of the tutor song response in HVC (Nick and Konishi, 2005a). The current study focuses exclusively on the tutor song response during time periods of several hours. Each recording session will be treated as a separate experiment, with all three aims executed within each day. Saline injections. For the middle ear paralysis experiment, a set of control saline injections will complement the lidocaine injections. Saline injections will control for nonspecific isoflurane, stress, or fluid effects that are not due to middle ear paralysis. EXPERIMENTAL METHODS Animals and surgery. The same set of chronically implanted finches will be used for aims 1-3. Please see Aim 1 for details. Triodes, microdrive, video monitoring, and lidocaine/saline injections. For this section, see Aim 1 except for details of perturbation of auditory feedback: For the masking noise experiment, all birds will be presented with sets of 50 - 100 trials consisting of multiple stimuli, in random order: silence, the tutor song (1 copy), conspecific song (1 copy), a recent copy of BOS (<72 hrs old; 1 copy), and 4 copies of masking noise (15 sec duration with a 5 –7 kHz window). The intertrial interval will be 20-s. Thus, masking noise will be played for 60 out of every 160 seconds. For the middle ear paralysis experiment, playback stimuli will be identical except the 4 copies of masking noise will be replaced with silence trials. Birds will be temporarily anesthetized with isoflurane and injected in the middle ear with either 0.9% saline or 2% lidocaine hydrochloride. A small hole in the skull dorsoventral to the ear canal opens directly to the middle ear (E. Rubel, personal communication; personal observations). Song recording and masking noise playback. For song recording, male zebra finches will be isolated in acoustic attenuation chambers. Songs and masking noise will be recorded, digitized, and played back at 44,100 Hz. Songs will be edited with MATLAB (Mathworks, Natick, MA). All songs for playback will be recorded with no female present (undirected). Masking noise for playback (created with MATLAB) will be band-pass filtered such that the noise contains no energy at frequencies between 5 – 7 kHz and above 10 kHz. Power in frequencies between 5 – 7 kHz will be used to determine the onset and offset of singing and the amplitude and sequence of syllables that are produced. Analysis of Singing/Masking noise Playback Data. All data will be analyzed with Matlab functions. A MATLAB function will be used to find songs. For both perturbed and normal singing data sets, a trained blind human observer will check the sound segments identified by MATLAB as songs. Neuronal activity patterns during the motif will be assessed for the tutor song selective neurons (identified in the Song Playback Studies that precede the Feedback Perturbation experiment). The amount of each neuron’s activity during perturbed and normal singing will be compared using an unpaired, two-tailed Student’s t-test. I predict that perturbation of auditory feedback will specifically decrease the activity of tutor song selective HVC neurons.

PHS 398/2590 (Rev. 09/04) Page 24 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

Aim 3. Identify the destination of the matching signal. Hypothesis: The matching signal is conveyed to the Anterior Forebrain Pathway (AFP), known to have roles in song learning. Prediction: Antidromic identification of HVC neurons that carry the matching signal will reveal that they project to Area X (basal ganglia) of the AFP. HVC contains three major subtypes of neurons defined by where they project: (1) basal ganglia (Area X; HVC-X), (2) the motor cortex analog (Robustus Arocopallialis; HVC-RA), and (3) within HVC (interneurons). Only a subset of these may carry the matching signal. There are two well-established methods that could be used to determine the identity of the tutor song selective neurons: antidromic techniques and filling the cells with vital dye. Dye fills require restraint of the finch, which may affect neuronal activity. In contrast, antidromic techniques do not require restraint. To decrease the potential plasticity induced by repeated antidromic stimulation, I will execute the antidromic identification tests between and not during recording sessions. I will cautiously interpret data that suggest that interneurons are responsive to tutor song (see Controls and Potential Results below). We hypothesize that the HVC neurons that respond selectively to tutor song project to Area X, which is part of the corticothalamic-basal ganglia loop known as the Anterior Forebrain Pathway (AFP). The AFP clearly has a role in song development, since lesioning a nucleus in this pathway in juveniles abruptly disrupts song learning (Bottjer et al., 1984). EXPERIMENTAL PLAN To test the hypothesis that HVC-X projection neurons carry the matching signal, I will examine tutor song selectivity in HVC neurons in awake juveniles and identify their projection targets using antidromic techniques (Fig. 14). Awake juveniles in the early sensorimotor phase (50 – 65 days) will be chronically implanted with recording and stimulating electrodes. After each recording session that addresses Aim 1 or Aim 2, neurons will be tested using antidromic techniques (Preliminary Data, Figs. 8 - 9). Spikes recorded during the Aim 1/2 recording session and during antidromic testing will be sorted (clustered) together, without regard or knowledge of spike timing or the context of each action potential. CONTROLS Tests for antidromicity. Two tests will be used to distinguish between antidromic activation and synaptic excitation, which is a concern since HVC contains interneurons that can be activated through antidromic activation of either Area X or RA. Rheobase (minimum current necessary to stimulate a spike) will be established at the beginning of each experiment. Stimulation current will be equal to rheobase, with the established standard duration of 200 μsec (Ranck, 1975). After the experiment, location of the stimulating electrodes to Area X and RA will be confirmed with cresyl violet histology (see Aim 1 Controls). A short and invariant latency. The time between the beginning of the stimulus and the peak of the extracellular spike will be compared for 10 stimuli that were not preceded by a spontaneous spike within a time period equaling T. T is the absolute refractory period plus 2 times the antidromic latency (Lipski, 1981). The absolute refractory period will be conservatively estimated at 300 μsec. The antidromic latency will be experimentally determined as maximal delay between the beginning of the stimulus and the peak of the antidromic spike. Fixed latency will be defined as spike latency variance ≤ 200 μsec. Variances of ≥ 200 μsec will indicate that the cell is activated through synaptic mechanisms and not through antidromic stimulation. Collision of antidromic response with orthodromic spontaneous spikes. Spike collision is the best test for antidromicity and will be applied to every cell recorded. Area X and RA will be stimulated during and after the time period T, relative to the spontaneous spike. Antidromic stimulation will be revealed by a consistent ability to stimulate the neuron after, but not during, T. Synaptic activation of interneurons. Stimulation of Area X and RA causes synaptic activation of neurons within HVC (Hahnloser et al., 2002; M. Fee, personal communication). Interneurons can be activated within the same latencies as projection neurons. Cells that are not antidromically stimulated by electrodes that are within Area X and RA will tentatively be categorized as ‘interneurons’ (see Potential Results). PHS 398/2590 (Rev. 09/04) Page 25 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A.

Figure 14. Schematic representation of the experiment proposed in Aim 3, executed during the early sensorimotor phase. This aim will delve into the motor circuitry (see brain below). Five of the brain song nuclei are shown. Anterior is right; dorsal is up. The nucleus HVC, also recorded in the other 2 aims, will be recorded with a triode. All recorded neurons HVC will be tested with antidromic stimulation of Area X and RA. The Anterior Forebrain Pathway is indicated by the gray nuclei. Stimulation of fibers of passage. The nucleus HVC has only two efferent targets: Area X (basal ganglia) and the nucleus RA (motor cortex analog). Stimulating fibers of passage is generally not a concern, since these nuclei are located near the most anterior (Area X) and posterior (RA) extents of the finch cerebrum. However, if very high amplitude currents are used, it may be possible to stimulate both sets of fibers. Therefore, stimuli will be the lowest current amplitude up to 500 μA that reliably evokes a spike. If 500 μA fails to evoke a spike, the neuron will be considered “not projecting” to the stimulated nucleus. A stimulus of amplitude 500 μA and duration 200 μsec will travel a spherical radius of approximately 2 mm (Ranck, 1975), which is enough to stimulate the nucleus in which the electrode is placed without stimulating the other target nucleus or HVC. EXPERIMENTAL METHODS Animals and surgery. The same set of chronically implanted finches will be used for aims 1-3. Please see Aim 1 for details. The cathode of the bipolar stimulating electrode will be placed in the nucleus to be stimulated. Multi-electrodes, microdrive, and video monitoring. For this section, see Aim 1 except for details of the stimulating electrode implantation: Stimulating electrodes will be tetrodes made of 25-μm nichrome-formvar. One lead of each tetrode will be wired to the 10-pin connector that connects the electrodes from the bird’s head to the headstage. Tetrodes instead of a single wire will be used for rigidity, which will facilitate placement of the stimulating electrode in the appropriate deep brain area (Area X or RA). Stereotaxic coordinates, combined with electrophysiological monitoring during surgery, will ensure implantation of the stimulating electrodes in the correct brain area. Both Area X and RA have unique electrophysiological activity profiles under ketamine-xylazine anesthesia. Antidromic techniques. Stimulation of Area X and RA will be achieved with a Master-8 stimulator and an IsoFlex Stimulus Isolation Unit (A.M.P.I., Israel). Orthodromic-antidromic spike collision will be achieved by triggering the Master-8 off the spontaneous orthodromic spikes using an APM window discriminator (FHC, Bowdoinham, ME). There is an inherent delay of 1-msec in the electronics from trigger to antidromic stimulus. The least amount of current that can routinely elicit an antidromic spike (rheobase) will be used. Finding other action potentials of the antidromically-stimulated neuron. Identification of the synaptic target of a single unit must be done in the larger context of identifying that neuron’s activity patterns during playback of auditory stimuli (Aim 1) and during singing (Aim 2). To link activity patterns to an identifiable HVC neuronal subtype, I will cluster all action potentials from the preceding Aim 1 or Aim 2 recording session with the action potentials that are obtained from the antidromic tests that follow. Each clustered neuron will be examined for spike collision. If the clustered unit collides with the antidromically-stimulated spike, it will be identified as a projection neuron. Spike timing information will be extracted from the clustered data and examined relative to auditory stimuli and song behavior. Neurons that do not pass the spike collision test will be classed as putative interneurons.

PHS 398/2590 (Rev. 09/04) Page 26 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. 4. Test a specific mechanism through which the template-matching signal may shape behavior. Hypothesis: The temporal relationships between HVC brain activity and song behavior change during song learning, revealing a potential mechanism for activity-dependent shaping of vocal behavior. Predictions: During the sensorimotor phase, HVC activity will (1) correlate less with behavior than in adults and (2) outlast the vocalization. The problem of delays between motor command and sensory feedback poses severe constraints on any sensorimotor system. Known cellular mechanisms of activity-dependent plasticity require close temporal association of action potentials in neurons that exhibit activity-dependent strengthening (e.g., neurons that fire together, wire together). The song system of juvenile finches that are actively learning song is more electrically active than that of adults (Nick and Konishi, 2005a). Therefore, I propose a hypothesis that would enable temporal overlap of motor command and sensory feedback: Higher order motor circuits (in HVC) that drive vocalization are active longer than those in adults, allowing sensory feedback to shape synaptic connectivity via activity-dependent mechanisms. I will test this hypothesis by comparing HVC neuronal population activity during song behavior across development. My hypothesis predicts that HVC activity will outlast the vocalization in actively learning finches, but not in adults. EXPERIMENTAL PLAN This experiment aims to compare activity in the same neuronal population across developmental stages. The best method for achieving this goal is multi-unit recording. Previously, I have successfully obtained recordings for many weeks in developing finches (Nick and Konishi, 2005a, b). Ten juveniles in the early sensorimotor phase (50 – 65 days) will be chronically implanted with multi-unit recording electrodes. After 3 days recovery, their vocalizations and corresponding brain activity will be recorded. These finches will not experience auditory playback, as described above, since sound playback affects brain activity in vocal areas in awake juveniles and may confound my results (Nick and Konishi, 2005a, b). CONTROLS Circadian effects. Because circadian effects on vocal activity in the song system are completely unknown, birds will be kept on a strict 12:12 light cycle with vocal recordings for this experiment occurring only during the first five hours of waking. Directed versus Undirected Singing. The presence of a female finch affects song system activity during singing. Thus, to decrease variability and increase control, only undirected singing (in the absence of a female) will be studied. If time permits, I will also examine activity during directed singing across development. EXPERIMENTAL METHODS Animals and surgery. Please see Aim 1 for details of surgical implantation. Ten juvenile zebra finches age 50-65 will be implanted with two 25 μm multi-unit recording electrodes within HVC and a 25 μm reference just outside of HVC. Multi-unit electrodes and video monitoring. For this section, see Aim 1, since recording parameters will the same. Only calls less than 300 msec in duration and with 500 msec of silence before and after will be analyzed. Analysis of multi-unit data. Multi-unit data will be analyzed relative to the vocalization. The root mean square will be obtained for the voltage trace in 20-msec bins, beginning 300 msec prior to vocalization and ending 60 msec after vocalization onset and beginning 60 msec prior to vocalization offset and ending 300 msec after the vocalization. The mean data for each bin will normalized to the maximum bin for each recording day. The combined data for each bin, relative to the vocalization, will be compared between juveniles and adults using two-way ANOVA.

SUMMARY Both humans and songbirds are vocal learners that share behavioral and biological characteristics (Doupe and Kuhl, 1999). Notably, both humans and songbirds exhibit a vocal learning phase during which vocalizations are matched to learned species-typical sounds. The proposed experiments will test the hypothesis that a template-matching signal occurs within a premotor song nucleus (HVC) during song learning

PHS 398/2590 (Rev. 09/04) Page 27 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. and extend knowledge of the neural events that occur during vocal learning. The proposed study is specifically designed to avoid pitfalls that have confounded previous studies, such as the use of anesthesia. Key aspects of the putative matching signal will be investigated: (1) What information does the matching signal convey? (2) Does it occur during singing? (3) What brain area receives the matching signal? (4) Do brain-behavior relationships change during development and facilitate effects of the matching signal? I am particularly well qualified to execute these experiments because of my extensive experience with electrophysiological techniques in developing neurons and neural systems (Nick et al., 1996a; Nick and Ribera, 2000) and my familiarity with the physiology of the developing HVC (Nick and Konishi, 2005a, b). Future experiments will build on these findings by tracking and manipulating the putative matching signal through the song system. Key questions for future study include: (1) How is the tutor song memory (template) stored? (2) What brain space compares the template to auditory input? (3) How are changes in motor circuits and behavior induced? Understanding the brain mechanisms of vocal learning may enable extension or reinstantiation of vocal plasticity in humans and treatment of manifold communication disorders that affect our society.

TIME-LINE I propose to utilize the same juvenile finches for the first three aims, when possible. These aims will run concurrently (Fig. 15), but I expect to move more quickly toward the primary goals of Aims 1 and 2, due to the difficulty of the experiments described in Aim 3. I will begin to work on Aim 4 during the third year of funding. Should my R01 not be renewed upon the first submission, I will support the laboratory with my remaining start up funds and a grant from the John Merck Fund. Due to the time delays inherent in working with breeding pairs and the extremely labor intensive techniques involved, it would be impossible to complete these aims in less than 5 years. At the end of 5 years, several key aspects of vocal learning in songbirds will be revealed: (1) the information encoded by a putative template- matching signal; (2) whether the putative matching signal occurs during singing; (3) the identity and targets of the neurons that carry the putative matching signal; and (4) a potential mechanism for transforming the template-matching signal into changes in behavior. Further progress in understanding vocal learning will be difficult without this information.

Figure 15. Timeline of the proposed experiments. Darkness indicates projected output. Expected publications are shown below.

E. Human Subjects None.

PHS 398/2590 (Rev. 09/04) Page 28 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. F. Vertebrate Animals

1. Description of the proposed use of animals. 200 male zebra finches, aged 45 – 65 days will be utilized in the proposed work. The same finches will be used for all aims. 2. Justification. The neurophysiological mechanisms of vocal learning, a complex behavior, are completely unknown. Therefore, computational modeling or cell culture experiments are not possible. Due to the invasive nature of the experiments, use of humans to examine these questions would be unethical. Therefore, the use of animals is warranted. There are only six groups of vocal learners in the animal kingdom: humans, whales and dolphins, a genus of bats, parrots, hummingbirds, and songbirds. Of these the best characterized and most tractable for study is the songbird. Zebra finches are the favorite model for birdsong physiology, due to their hardy physique, rapid breeding in captivity, and well-characterized behavioral development and brain architecture. 200 finches will be used for the experiments, because a minimum of 10 finches is required for rigorous statistics and I expect a minimum success rate of 5%. 3. Veterinary care. Veterinary care will be provided by the University of Minnesota’s Research Animal Resources Department. Finches will be observed daily. Any signs of ill health will be reported immediately to the veterinarian in charge.

4. Minimization of discomfort. Finch discomfort will be minimized in a number of ways. Prior to chronic implant surgery, finches will be anesthetized with a general anesthetic (ketamine/xylazine, see Methods). Post-operative analgesia will be provided by injection with meloxicam.

5. Euthanasia. Finches will be euthanized with a systemic injection of sodium pentobarbital (300 μg/g). This method of euthanasia is widely used in the birdsong community. Sodium pentobarbital results in a rather quick, painless death and yet enables perfusion of the brain with fixative, which is required for histological analyses.

G. Literature Cited (2002) To assure the free appropriate public education of all children with disabilities: Twenty-Fourth Annual Report to Congress on the Implementation of the Individuals with Disabilities Education Act. In: U.S. Department of Education. Adret P, Chi Z, Margoliash D (2005) Neural patterns of premotor activity and electroencephalography associated with subsong in the juvenile songbird. In: Society for Neuroscience Abstracts, p 79.11. Baptista LF, Schuchmann KL (1990) Song learning in the anna hummingbird (Calypte anna). Ethology 84:15- 26. Bi G, Poo M-M (2001) Synaptic modification by correlated activity: Hebb's postulate revisited. Annu Rev Neurosci 24:139-166. Bischof HJ, Bohner J, Sossinka R (1981) Influence of external stimuli on the quality of song of the Zebra Finch (Taeniopygia guttata castanotis). Z Tierpsychol 57:261-267. Bottjer S, Miesner E, Arnold A (1984) Forebrain lesions disrupt development but not maintenance of song in passerine birds. Science 224:901-903. Brainard MS, Doupe AJ (2000) Interruption of a basal ganglia-forebrain circuit prevents plasticity of learned vocalizations. Nature 404:762-766. Buzsaki G (2004) Large-scale recording of neuronal ensembles. Nature Neurosci 7:446-451. Dave AS, Margoliash D (2000) Song replay during sleep and computational rules for sensorimotor vocal learning. Science 290:812-816.

PHS 398/2590 (Rev. 09/04) Page 29 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. Dave AS, Yu AC, Margoliash D (1998) Behavioral state modulation of auditory activity in a vocal motor system. Science 282:2250-2254. Doupe AJ (1997) Song- and order-selective neurons in the songbird anterior forebrain and their emergence during vocal development. J Neurosci 17:1147-1167. Doupe AJ, Kuhl PK (1999) Birdsong and human speech: Common themes and mechanisms. Annu Rev Neurosci 22:567-631. Fee MS, Leonardo A (2001) Miniature motorized microdrive and commutator system for chronic neural recording in small animals. Journal of Neuroscience Methods 112:83-94. Hahnloser RHR, Kozhevnikov AA, Fee MS (2002) An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419:65-70. Harris KD, Henze DA, Csicsvari J, Hirase H, Buzsaki G (2000) Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J Neurophys 84:401-414. Hessler NA, Doupe AJ (1999) Social context modulates singing-related neural activity in the songbird forebrain. Nature Neurosci 2:209-211. Immelmann K (1969) Song development in the zebra finch and other estrilid finches. In: Bird Vocalizations (Hinde RA, ed), pp 61-74. Cambridge: Cambridge University Press. Iyengar S, Bottjer SW (2002) The role of auditory experience in the formation of neural circuits underlying vocal learning in zebra finches. J Neurosci 22:946-958. Jarvis ED, Scharff C, Grossman M, Ramos JA, Nottebohm F (1998) For whom the bird sings: context- dependent gene expression. Neuron 21:775-788. Johnston CJ, Beitchman JH, Young A (1999) Fourteen-year follow-up of children with and without speech/language impairments: speech/language stability and outcomes. J Speech, Lang, Hear Res 42:744-760. Kao MH, Doupe AJ, Brainard MS (2005) Contributions of an avian basal ganglia-forebrain circuit to real-time modulation of song. Nature 433:638-643. Konishi M (1965) The role of auditory feedback in the control of vocalization in the white-crowned sparrow. Z Tierpsychol 22:770-783. Konishi M (1985) Birdsong: from behavior to neuron. Annu Rev Neurosci 8:125-170. Law J, Boyle J, Harris F, Harkness A, Nye C (2000) Prevalence and natural history of primary speech and language delay: findings from a systematic review of the literature. Int J Lang Comm Dis 35:165-188. Leonardo A, Konishi M (1998) Decrystallization of adult birdsong by perturbation of auditory feedback. Nature 399:466-470. Lipski J (1981) Antidromic activation of neurones as an analytical tool in the study of the central nervous system. J Neurosci Methods 4:1-32. Lobaugh N, Greene P, Grant M, Nick T, Amsel A (1989) Patterned (single) alternation in infant rats after combined or separate lesions of hippocampus and amygdala. Behav Neurosci 103:1159-1167. Margoliash D (1983) Acoustic parameters underlying the responses of song-specific neurons in the white- crowned sparrow. J Neurosci 3:1039-1057. Marler P, Peters S (1977) Selective vocal learning in a sparrow. Science 198:519-521. McCasland JS, Konishi M (1981) Interaction between auditory and motor activities in an avian song control nucleus. PNAS, USA 78:7815-7819. McNaughton BL, O'Keefe J, CA B (1983) The stereotrode: a new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records. J Neurosci Methods 8:391- 397. Mello CV, Clayton DF (1994) Song-induced ZENK gene expression in auditory pathways of songbird brain and its relation to the song control system. J Neurosci 14. Mello CV, Vicario DS, Clayton DF (1992) Song presentation induces gene expression in the songbird forebrain. PNAS, USA 89:6818-6822.

PHS 398/2590 (Rev. 09/04) Page 30 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. Mooney R (2000) Different subthreshold mechanisms underlie song selectivity in identified HVc neurons of the zebra finch. J Neurosci 20:5420-5436. Nick TA, Ribera AB (2000) Synaptic activity modulates presynaptic excitability. Nature Neurosci 3:142-149. Nick TA, Konishi M (2001) Dynamic control of auditory activity during sleep: Correlation between song response and EEG. PNAS, USA 98:14012-14016. Nick TA, Konishi M (2004) Neural song preference during vocal learning in the zebra finch depends on age and state. J Neurobiol 62:231-242. Nick TA, Konishi M (2005a) Neural song preference during vocal learning in the zebra finch depends on age and state. J Neurobiol 62:231-242. Nick TA, Konishi M (2005b) Neural auditory selectivity develops in parallel with song. J Neurobiol 62:469- 481. Nick TA, Kaczmarek LK, Carew TJ (1996a) Ionic currents underlying developmental regulation of repetitive firing in Aplysia bag cell neurons. J Neurosci 16:7583-7598. Nick TA, Moreira JE, Kaczmarek LK, Carew TJ, Wayne NL (1996b) Developmental dissociation of excitability and secretory ability in Aplysia bag cell neurons. J Neurophysiol 76:3351-3359. Nordeen KW, Nordeen EJ (1992) Auditory feedback is necessary for the maintenance of stereotyped song in adult zebra finches. Behav Neural Biol 57:58-66. Nottebohm F (1972) The origins of vocal learning. Am Nat 106:116-140. Nottebohm F, Stokes TM, Leonard CM (1976) Central control of song in the canary, Serinus canarius. J Comp Neurol 165:457-486. Nottebohm F, Alvarez-Buylla A, Cynx J, Kirn J, Ling C-Y, Nottebohm M, Suter R, Tolles A, Williams H (1990) Song learning in birds: the relation between perception and production. Phil Trans R Soc Lond B 329:115-124. Olveczky BP, Andalman AS, Fee MS (2005) Vocal experimentation in the juvenile songbird requires a basal ganglia circuit. PLOS Biology 3:0902-0909. Ranck JB (1975) Which elements are excited in electrical stimulation of mammalian central nervous system: a review. Brain Res 98:417-440. Rauske PL, Shea SD, Margoliash D (2003) State and neuronal class-dependent reconfiguration in the avian song system. J Neurophys 89:1688-1701. Recce M, O'Keefe J (1989) The tetrode: a new technique for multi-unit extracellular recording. Soc Neurosci Abs 15:1250. Redish AD, Battaglia FP, Chawla MK, Ekstrom AD, Gerrard JL, Lipa P, Rosenszweig ES, Worley PF, Guzowski JF, McNaughton BL, CA B (2001) Independence of firing correlates of anatomically proximate hippocampal pyramidal cells. J Neurosci 21:134RC. Schmidt M, Perkel D (1998) Slow synaptic inhibition in nucleus HVC of the adult zebra finch. J Neurosci 18:895-904. Schmidt MF, Konishi M (1998) Gating of auditory responses in the vocal control system of awake songbirds. Nature Neurosci 1:513-518. Schmitzer-Torbert N, Jackson J, Henze D, Harris KD, Redish AD (2005) Quantitative measures of cluster quality for use in extracellular recordings. Neurosci 131:1-11. Solis MM, Doupe AJ (1997) Anterior forebrain neurons develop selectivity by an intermediate stage of birdsong learning. J Neurosci 17:6447-6462. Solis MM, Doupe AJ (1999) Contributions of tutor and bird's own song experience to neural selectivity in the songbird anterior forebrain. J Neurosci 19:4559-4584. Solis MM, Doupe AJ (2000) Compromised neural selectivity for song in birds with impaired sensorimotor learning. Neuron 25:109-121. Sossinka R, Bohner J (1980) Song types in the Zebra Finch (Poephila guttata castanotis). Z Tierpsychol 53:123- 132.

PHS 398/2590 (Rev. 09/04) Page 31 Continuation Format Page

Principal Investigator/Program Director (Last, First, Middle): Nick, Teresa A. Stripling R, Kruse AA, Clayton DF (2001) Development of song responses in the zebra finch caudomedial neostriatum: role of genomic and electrophysiological activities. J Neurobiol 48:163-180. Tchernichovski O, Mitra PP, Lints T, Nottebohm F (2001) Dynamics of the vocal imitation process: how a zebra finch learns its song. Science 291:2564-2569. Tchernichovski O, Nottebohm F, Ho CE, Pesaran B, Mitra PP (2000) A procedure for an automated measurement of song similarity. Anim Behav 59:1167-1176. Troyer TW, Doupe AJ (2000a) An associational model of birdsong sensorimotor learning I. Efference copy and the learning of song syllables. J Neurophys 84:1204-1223. Troyer TW, Doupe AJ (2000b) An associational model of birdsong sensorimotor learning: II. Temporal hierarchies and the learning of song sequence. J Neurophys 84:1224-1239. Volman SF (1993) Development of neural selectivity for birdsong during vocal learning. J Neurosci 13:4737- 4747. Vu ET, Mazurek ME, Kuo YC (1994) Identification of a forebrain motor programming network for the learned song of zebra finches. J Neurosci 14:6924-6934. White BH, Nick TA, Carew TJ, Kaczmarek LK (1998) Protein kinase C regulates a vesicular class of calcium channels in the bag cell neurons of Aplysia. J Neurophysiol 80:2514-2520. Wilson MA, McNaughton BL (1993) Dynamics of the hippocampal ensemble code for space. Science 261:1055-1058. Zevin JD, Seidenberg MS, Bottjer SW (2004) Limits on reacquisition of song in adult zebra finches exposed to white noise. J Neurosci 24:5849-5862.

PHS 398/2590 (Rev. 09/04) Page 32 Continuation Format Page

SUMMARY STATEMENT PROGRAM CONTACT: ( Privileged Communication ) Release Date: 08/01/2006 Dan Sklare (301) 496-1804 [email protected] Application Number: 1 K02 DC008521-01 Principal Investigator NICK, TERESA A PHD, UNIVERSITY OF MINNESOTA TWIN CITIES Review Group: CDRC Communication Disorders Review Committee

Meeting Date: 06/21/2006 RFA/PA: PA00-020 Council: OCT 2006 PCC: VS20 Requested Start: 12/01/2006

Project Title: Mechanisms of vocal learning in the zebra finch SRG Action: Priority Score: 184 Human Subjects: 10-No human subjects involved Animal Subjects: 30-Vertebrate animals involved - no SRG concerns noted

Project Direct Costs Estimated Year Requested Total Cost 1 98,373 106,242 2 98,373 106,242 3 98,373 106,242 4 98,373 106,242 5 98,373 106,242 ______TOTAL 491,865 531,210

ADMINISTRATIVE BUDGET NOTE: The budget shown is the requested budget and has not been adjusted to reflect any recommendations made by reviewers. If an award is planned, the costs will be calculated by Institute grants management staff based on the recommendations outlined below in the COMMITTEE BUDGET RECOMMENDATIONS section.

NEW INVESTIGATOR

1 K02 DC008521-01 2 CDRC NICK, T

1 K02 DC008521-01 NICK, TERESA SCIENTIFIC REVIEW ADMINISTRATOR’S NOTES

RESUME AND SUMMARY OF DISCUSSION: This Independent Scientist Award application requests five years of support to study how auditory-selective neurons in a vocal-control nucleus (HVC) of the zebra finch brain that are tuned to the learned song contribute to the matching signal and song production. The study of learning and song production in birds is relevant to the development of speech in humans. The Candidate has expertise in bird song, excellent training in electrophysiology and will develop a recording technique (multi-electrode) allowing characterization of unit activity and antidromic activation to identify projection neurons in awake behaving birds. Overall, this is a well presented research application from a strong Candidate. There are a few concerns: some experiments, particularly those addressing aim one, are not described in adequate detail. More detail for the linear regression analysis is needed. The rationale might have been presented more clearly, and more discussion of alternative approaches and interpretations should be included. Some of the aims are not sufficiently focused to advance our understanding of the mechanisms that underlie vocal learning, or the functional role of the auditory-selective neurons in the process. The research is anticipated to generate interesting results. The request for support of the Investigator is appropriate because developing the recording technique and carrying out the proposed studies will be difficult and time consuming. The Candidate has the support of her department Chair, and will be free to commit up to 90% effort to the research. The scientific environment includes good collaborators.

DESCRIPTION (provided by applicant): Communication disorders affect millions of people. Understanding the neural bases of vocal learning will enable early diagnosis and effective treatment of these diseases. There are a few nonhuman vocal learners, of which the songbirds offer the best- characterized model in terms of physiology and behavior. As with humans, the zebra finch songbird learns its song in two phases: a sensory phase during which the song of an adult tutor is memorized and a sensorimotor phase during which the birds own vocalizations are shaped through auditory feedback to match the tutor song. The shaping of the vocalization requires that the comparison between auditory feedback and the memory of the tutor song sculpt the song motor control circuitry. We recently found that the tutor song selectively activates a key nucleus of the song premotor pathway (Nick & Konishi, 2005a). This selective activation occurs only during waking and only during the period of development when the tutor song memory is used to shape vocalizations. This suggests that the comparison of auditory feedback with the tutor song memory generates an instructive matching signal that is relayed to the premotor nucleus. There are 4 specific hypotheses: (1) responses of individual neurons convey the degree of similarity between stimuli and the tutor song memory; (2) the matching signal occurs during singing; (3) the matching signal is relayed to the basal ganglia; and (4) the mechanism that transforms the matching signal into behavioral change involves sustained neural activity in the song system that enables temporal overlap of motor command and sensory feedback and subsequent activity-dependent plasticity. The study will utilize three powerful techniques in awake juveniles: multi-electrode recording, which enables the stable assessment of the activity of many single neurons, antidromic stimulation, which enables the identification of individual neurons, and long-term population recordings. The ultimate goal is to use the matching signal to illuminate the role of memory and sensation in shaping vocal behavior. The candidate, Dr. Teresa A. Nick, is uniquely qualified to execute these experiments. She has received training on (1) the development of neurons and circuits from Drs. Thomas Carew and Leonard Kaczmarek (Yale); (2) extrinsic modulation of neuronal development from Dr. Angeles Ribera (Univ. Colorado); and (3) the development, state-dependent modulation, and learning of birdsong from Dr. Masakazu Konishi (Caltech). She has published extensively on neural development and has discovered the first evidence for a template-matching signal. She has already applied several novel techniques to the song system and developed a new method combining multi-electrode and antidromic techniques. The University of Minnesota provides the ideal environment in which to pursue these experiments, due to strengths in auditory processing, multi- electrode techniques, and antidromic methods.

CRITIQUE 1 1 K02 DC008521-01 3 CDRC NICK, T

Candidate: Teresa Nick is qualified to carry out the proposed experiments, as she has extensive training in electrophysiological methods and has generated novel and interesting data in the area of vocal learning in songbirds. Thus, she has the expertise and initial data, upon which to build. The proposed approach may advance our knowledge of the mechanisms of vocal learning during sensitive periods. The Investigator has the potential to become a leader in her field, and the potential for the proposed experiments to result in significant contributions is high. The number of publications from the Investigator has not been particularly high (9 peer-reviewed publications over the course of 18 years, starting as an undergraduate), but the publications have been of high quality and published in excellent journals. Her proposal is thoughtful and well-organized, and her background and experience combine to make this a strong proposal. If supported this work will provide Dr. Nick with the opportunity to learn new techniques (triode/tetrode recording in awake behaving songbirds) and develop new hypotheses for the mechanisms of developmentally-regulated vocal learning.

Career Development Plan: The likelihood that the proposed training plan will contribute substantially to the development of the Candidate is high. Although the research plan is ambitious, developing reliable triode/tetrode recordings in songbird brain would be a substantial advance. As described, the plan would enable the Candidate to focus her efforts (90%) on research, which is an essential pre- requisite for developing the new and challenging techniques proposed.

Research Plan: In general, the research plan is very strong. One general comment is that the individual experiments should be described in greater depth in terms of methodological details, possible outcomes and conclusions, and alternate outcomes and potential pitfalls.

As a first aim, the Candidate proposes to make triode recordings in HVC in awake behaving juvenile birds during song playback, with the idea that this technique will confer much greater stability for extended recordings, allow multiple neurons to be recorded, and enable better identification of individual units. The various auditory stimuli and methods for presentation are not described in adequate detail. The only stimuli mentioned are tutor song and the bird’s own song (BOS) – but not other conspecific songs, which would seem to be an important control. This study will measure the “number of action potentials” in neurons that are responsive to tutor songs relative to “other” songs (presumably this means spike rates, post-stimulus time histograms [PSTHs], and/or other). It will also be important to measure the exact proportion of HVC neurons that are or are not selective for tutor song, and if not, whether they exhibit alternate preferences.

Aim 2 is to identify HVC neurons that respond to tutor song playback in juvenile birds using triode recordings, and examine the activity of such neurons during song production with or without perturbations of auditory feedback. The rationale is to test whether the activity of HVC neurons that respond to hearing tutor song also receive auditory feedback during singing. Pilot data indicates that playback of masking noise does not alter the amplitude or form of song production, and one neuron showed a small but significant decline in spike rate when masking noise was presented during singing. This is an important, but difficult, experiment. Dr. Nick will also use middle ear inactivation as an alternate means of perturbing auditory feedback; this is a good idea, and might show larger effects. One reason that only small-magnitude effects might be observed is that individual HVC neurons might exhibit both pre-motor and auditory activity. Presumably this could be resolved in this study, and would be very interesting. This is another example of something that needs to be discussed in more detail. It will also be important to determine whether neurons that do NOT respond selectively to tutor song playback show a change in activity during perturbation of auditory feedback during singing. There is no a priori reason to think that the neurons that respond to passive playback of tutor song are the same ones that may respond to auditory feedback. If these experiments go smoothly, the Investigator might consider inactivating the higher-order auditory areas NCM and CMM. Recent evidence suggests that one or both of these areas may be the repository of the template (these studies are not cited in this proposal, which is an unfortunate oversight . . .). If a template signal in NCM and/or CMM is conveyed to HVC, then inactivation of these areas would be expected to alter activity in one or more subsets of 1 K02 DC008521-01 4 CDRC NICK, T

HVC neurons. In any case, this proposed study will generate important results, but may not resolve whether the response to tutor song or to auditory feedback in HVC serves as “the matching signal”.

Aim 3 is to use antidromic identification techniques to determine if the HVC neurons that respond to tutor song project to Area X in juvenile birds. The Investigator presents preliminary data showing the feasibility of this technique, and the experiment seems straightforward. Although the emphasis as described is on Area X, it is known that HVC neurons that project to RA also have auditory sensitivity. Thus, if tutor-selective neurons are projecting to Area X, does another class of auditory neurons in HVC project to RA? This is not discussed. The Investigator needs to state clearly if different auditory stimuli will be presented, combined with antidromic stimulation in both Area X and RA.

Aim 4 is to pursue an interesting aspect of the preliminary data that suggests that multi-unit activity in HVC is greater in juveniles and then decreases during the course of song learning. This study will test the degree of correlation between activity in HVC and song production, as well as measure the overall amplitude and duration of activity. The results will provide an initial documentation of patterns of neural activity in HVC during song production at different stages of vocal development, which are important normative data.

The Candidate should avoid overstating conclusions based on previous findings. She has made an important discovery, which is that neurons in the cortical song-control nucleus HVC are tuned to the tutor song that serves as the model for learning during a restricted period of development. This finding raises the possibility that HVC neurons tuned to the tutor song may participate in the process by which birds are able to gradually match their vocal output to that of the tutor song. However, it does not mean that the she has “found the instructive signal” (p. 19) or that HVC “receives a tutor song template- matching signal during waking within a restricted period of development”. There is no evidence that the auditory tuning of HVC neurons is used for template matching. Indeed, this is the fundamental question that the current proposal addresses. Further, the idea that there is a prevalent view in which the AFP stores the tutor song memory in order to serve as the template is over stated. In fact, one of the aims is to test the idea that a “template-matching” signal will be projected to the AFP (Area X) from HVC; such a signal might look like the auditory tuning discovered in HVC. Also the implicit idea that the template will be localized to only one brain region is potentially an over-simplification; template storage and template matching may be carried out in multiple locations.

Simply put, the importance of Dr. Nick’s previous work is considerable: the search for any auditory tuning that might constitute physiological evidence for a “template” has not yet been successful. Her discovery that the tutor song serves as the most effective stimulus for HVC neurons in awake juvenile birds is therefore a key discovery that may lead to studies of how such auditory tuning is used in the process of song refinement.

A postdoctoral trainee who will have a major involvement in this proposal (Dr. Aoki, see p. 20) was not listed under Other Significant Contributors (no Biographical Sketch was included). Despite the fact that Dr. Aoki’s salary is from another source, she should be listed given that she will be contributing to two of the four aims.

Environment and Institutional Commitment: The environment is excellent, and includes excellent colleagues, one of whom has expertise in triode recordings and will serve as a consultant. In addition, the Investigator has strong support from her department chair. Dr. Nick has established at least initial facility with all of the techniques used in the proposal, and has sought out collaborations with other investigators to enhance the research.

Vertebrate Animals: No concerns.

CRITIQUE 2 1 K02 DC008521-01 5 CDRC NICK, T

Candidate: This is a strong Candidate with broad education and training in Psychology, Zoology, and Neuroscience, whose area of expertise is electrophysiological approaches to neural plasticity and learning. The Candidate is clearly an independent researcher who has developed a novel multi-unit recording approach for identifying the neural representation of auditory memories in the songbird brain. The results will either strengthen established interpretations of prior results, or lead the way to a new understanding of the coding of auditory memories.

The Candidate’s research productivity is strong, although the number of publications is not high. However, the breadth of the research areas in which she has published is impressive. In addition, her recent songbird work, reporting chronic recordings from the brains of developing birds, is labor intensive. She is a recently established faculty member (her position at UM started in 04), who recently obtained an NIH funded R01 and two smaller grants from private foundations for her work.

Her current research is an extension of work that she began as a post-doctoral fellow in the lab of Masakazu Konishi at CalTech. She addresses a long-standing issue in neuroscience: how the brain stores a memory of a past event (in this case, an auditory memory), and how this memory can then be referenced to shape present behavior (in this case, a song pattern). It seems highly likely that her research findings in songbirds will provide important insight into the nature and treatment of human vocal disorders.

Given her extensive education and training in electrophysiological techniques, current approach to a difficult important issue in neuroscience, and demonstrated ability to generate important new findings, Dr. Teresa Nick is a strong Candidate for a K02 award.

Career Development Plan: The Candidate states that her motivation in applying for this award is to focus intensively on the development of her research program. Because her laboratory is still at an early stage of development, and her R01 was funded for only three years, she indicates that a funding gap is likely if she is not able to dedicate the majority of her time to the laboratory. Gaps in funding will likely lead to such things as an increased teaching load, which the Candidate feels would further jeopardize chances for successful renewal of her R01. Thus, K02 funding presents a means by which she can focus 90% of her time on the development of her laboratory and research program and limit her institutional commitments to 10%.

Overall, the research development plan outlined by the Candidate could place her laboratory among an elite group of songbird electrophysiology labs around the country. However, her career development plan would be strengthened by greater collaboration with other active researchers. While the collaborations listed may provide limited opportunity for growth in new directions, it is worth noting that the Candidate’s primary motivation in applying for a K02, is to dedicate most of her time to her research.

Research Plan: Before a songbird can learn to sing, it must first hear an example of adult song and store a long-term memory of that adult song (this memory is often referred to as the ‘template’). Song learning then appears to involve a trial-and-error process that requires auditory feedback – in other words, the memory of the adult song must be referenced to correct errors in vocal production. In 2005, the Candidate reported that she had identified a putative matching (or template) signal in the brain of juvenile birds that are learning song; this signal was located in HVC, a brain region positioned at the top of a hierarchical forebrain network that controls song. These findings stand in contrast to the most widely held view, which is that the template must lie somewhere downstream from HVC. (Although the evidence for this has never been overwhelming, especially since most of the data came from adult birds that had already learned their songs.) The research plan is to extend her original 2005 finding of tutor- selective neurons in HVC. Overall enthusiasm for the proposed program of research is high, although the 3rd and 4th aims are weaker than the first two. Aim 1: Examine the precision of neural tuning of single HVC neurons during song development. This experiment is a modification of the Candidate’s original study showing that HVC of juvenile birds shows 1 K02 DC008521-01 6 CDRC NICK, T a selective response to the tutor song. However, the objective is to compare the responses of individual HVC neurons as birds hear their own partially-learned song, various other auditory stimuli, and the tutor song. The number of action potentials generated during playback of the tutor song will be compared to the number of action potentials generated during other auditory events. The objective is to determine whether the tutor-selective neurons in HVC generate an ‘all-or-none’ signal (only respond to the tutor song) or a graded signal (respond best to tutor song, but respond to other sounds based on similarity to the tutor song). This experiment is interesting regardless of the outcome.

Aim 2: Identify and manipulate the activity of tutor-selective neurons during singing. This aim is to determine whether HVC neurons that respond to playback of the tutor song are active during singing. If so, the plan is to determine if perturbed auditory feedback alters the singing-driven activity of these neurons (masking noise and middle ear paralysis will be used to perturb auditory feedback). The Candidate hypothesizes that singing-driven activity in these neurons is dependent on auditory input that approximates the tutor song. Thus, these neurons could serve as a real-time matching system, responding only (or best) when the bird produced a vocal pattern that resembled or matched the tutor song. This experiment will generate interesting results.

Aim 3: Identify destination of the ‘matching signal’. This aim is to determine which of HVC’s three neuron types carries the putative matching signal. The Candidate hypothesizes that the matching signal will be carried by HVC neurons that project into a basal ganglia circuit that is necessary for vocal learning. Antidromic electrophysiological techniques will be used to identify the efferent targets of tutor- selective neurons. A potential weakness of this approach is that recordings are made with a multi- electrode array, and it will not be possible to anatomically label (dye fill) the tutor-selective neurons. Moreover, the Candidate does not discuss how the results will be interpreted if the tutor-selective neurons project to RA (HVC’s other efferent target), or are HVC interneurons.

Aim 4: In juveniles, test whether HVC activity correlates less with behavior in adults, and whether HVC activity in juveniles outlasts vocalization. The Candidate predicts that when HVC activity is measured across the period of vocal development, she will find that HVC activity in juveniles will exhibit a lower correlation with behavior that will gradually improve as birds learn their songs, and that HVC activity in juvenile birds will ‘outlast’ the vocalization, allowing sensory feedback to shape HVC circuits towards the learning goal. Both predictions are interesting, but there is no consideration of how negative results will be interpreted.

Training in the Responsible Conduct of Research: There are no concerns related to the proposed course of training.

Environment and Institutional Commitment: Institutional commitment to the development of the Candidate’s research career is strong. The Department will reduce her teaching and institutional commitments to 10% upon funding of this proposal, leaving her with a 90% commitment to research..

Budget: There are no concerns.

CRITIQUE 3 Candidate: Teresa Nick is an excellent Candidate for a K02 fellowship at the Univ. of Minnesota. Although she has only recently joined the faculty and has not fully established her research program, she has great potential. She is addressing difficult experimental questions that are central to the advancement of the field of birdsong research, and may make outstanding contributions to the field. She has generated high quality data and publications in the labs of her previous Mentors. Her previous research covered a wide range of models, from cellular to circuits and complex systems, resulting in significant contributions. She has not published a large number of papers, but the difficulty related to the experimental approaches is a reasonable justification. Her research proposal demonstrates her 1 K02 DC008521-01 7 CDRC NICK, T

ability to formulate long-term research plans and career goals. She has excellent training and the expertise to carry out the proposed experiments.

Career Development Plan: The award would allow the Candidate to dedicate her efforts towards research and be relived from substantial teaching and institutional responsibilities. This is especially important, because of the nature of the proposed experiments that require full-time involvement. Her goal is to not have research productivity reduced, which could adversely impact her ability to retain funding. The proposed plan relates directly to the Candidate’s stated career goals. Although collaborations are not a major strength of the application, the Candidate will likely gain further expertise in specific electrophysiology techniques and surgical methods through interactions with other scientists (David Redish, at Univ. Minnesota, and Stephanie White, at UCLA). The Candidate states that she will attend an annual training session on the Responsible Conduct of Research, but there is no mention of specific content and duration of training.

Research Plan: The Candidate proposes an exciting approach to address fundamental questions in the field of vocal learning. The proposal is generally well-written and the hypotheses are clearly stated. The proposed experiments are supported by substantial preliminary data and should advance the field considerably regardless of the results obtained.

Specifically, the Candidate has evidence that neuronal cells within HVC, a major song nucleus in the zebra finch brain, respond selectively to the sound of the tutor’s song. This finding is exciting and potentially highly relevant to the field, as it may represent the first evidence for an acquired memory of the tutor’s song (the so-called acquired template) that will guide the bird’s own vocal development. The key to this discovery was the recordings in awake birds, since the responses in the song system are highly sensitive to behavioral state. The recordings in juveniles during the early sensorimotor phase of vocal learning demonstrate the persistence and dedication of the researcher to the project.

The Candidate proposes to extend these findings. In the first Aim, she will determine if the song responses of HVC neurons in juveniles are proportional to the degree of similarity between the stimulus and tutor songs. For this purpose she will use multi-electrode (triode) recordings and spike sorting and clustering algorithms developed by her collaborator A. Redish. She will use a series of the bird’s own song (BOS) with varying degrees of similarity to the tutor song for the primary comparison, as well as various other auditory stimuli as controls. This is a well thought out experiment and the results should be informative. A relatively minor issue is that it is not clear that the BOS recorded at earlier ages will provide a broad enough range of songs with different degrees of similarity to the tutor song for a regression analysis, especially for recordings performed at the earlier ages, when BOS may still be very dissimilar from the tutor’s song. Thus, it may be necessary to generate also a set of artificial stimuli that differ from the tutor’s song by defined amounts of specific song parameters for a clear correlation analysis. In addition to examining the overall response to song, it would be interesting to examine whether tutor song selective HVC neurons respond preferentially to specific components of the tutor’s song, and whether different units pay attention to different components. The Candidate did not discuss the expected frequency of HVC units that show selective responses to tutor’s song, to give a clearer indication of the amount of effort required to achieve the proposed goal. Also, she did not propose to examine tutor song selectivity in naïve juveniles. If the phenomenon observed reflects an internalized template, it should be absent or less obvious in birds that have not yet been exposed to the sound of conspecific song. A comparison between the responses before vs. after such exposure would provide the most direct evidence that the responses to tutor song were acquired through experience.

In the second Aim, the Candidate will examine the effect of disruptions in auditory feedback on the recorded activity of HVC neurons during singing. This is a central tenet that has been untested in the field, namely that auditory information reaches the nuclei that encode the vocal motor program during singing, in order to potentially modify vocal motor representations. The major difficulty lies in the fact that vocal motor neurons are activated during singing, which makes it difficult to identify concomitant sensory responses triggered by auditory feedback. The Candidate has produced some very tantalizing 1 K02 DC008521-01 8 CDRC NICK, T preliminary data suggesting that she has identified a component of the activity of vocal motor neurons in HVC that corresponds to auditory feedback activity. Namely, a significant decrease in the activity (spike rate) is observed when comparing singing with or without a masking noise. The rationale is that the mask disrupts auditory feedback from singing, resulting in the observed drop in activity. She now intends to systematically test this effect in identified HVC neurons that respond to tutor song stimulation. For auditory feedback disruption she will use masking noise or the temporary paralysis of the middle ear with a local anesthetic. For the latter procedure, it is not clear that the birds would recover from anesthesia and sing while the local anesthetic is still effective. Are there any data to support this technique? In general, this is an exciting aim the experiments are likely to be informative.

The third Aim is to identify the neurons that respond selectively to the tutor’s song between the two types of projection neurons in HVC, X- and RA-projecting cells. This may identify the target of the processed auditory responses in HVC (towards the motor pathway or the anterior pathway involved in vocal plasticity). This is a difficult experiment, as it involves methods to test for antidromic activation in conjunction with the recording of auditory responses. The implantation of stimulating tetrodes to the desired targets will be required in addition to the recording triodes. If successful, this is another very strong and important component of the proposal that will raise further intriguing questions. For example, if tutor song-selective cells turn out to be X-projecting, what is the functional relevance of activating the anterior forebrain pathway when these cells are activated by a matching stimulus? Or perhaps the activation of these cells might have an inhibitory effect on the final output of that pathway and on vocal plasticity.

In the fourth aim, the Candidate will examine the duration of the activity of HVC neurons during singing to test the prediction that such activity extends further (beyond the end of vocalizations) in juveniles than in adults. Although this experiment is also likely to be informative, based on the preliminary data, it will not necessarily demonstrate that auditory feedback may shape motor programs in juveniles. Even if the recorded activity in juveniles extends beyond the end of the vocalizations, it is unclear whether this extension reflects auditory feedback, an extended motor-related activity that is not tightly correlated with the vocalization emitted, or both. Thus, this experiment seems to be the weakest in terms of testing the proposed hypothesis.

Environment and Institutional Commitment: There are clear signs of institutional commitment and support for the Candidate as an independent scientist and integral part of the research program. The Univ. on Minnesota has a high commitment to scientific research and provides an excellent environment for the Candidate’s further growth in the neurosciences, including interactions with several other well established colleagues, opportunities for collaborations, and exposure to other researchers through seminars and visits.

Budget: The period and amount of support appear to be appropriate for the Candidate’s goals and the proposed aims. This support will be important if the Candidate encounters difficulties in renewing her current R01.

Vertebrate Animals: The animal care provided is adequate. There are also adequate measures to avoid unnecessary pain and discomfort to the animals, and the number of animals to be used is well justified.

THE FOLLOWING RESUME SECTIONS WERE PREPARED BY THE SCIENTIFIC REVIEW ADMINISTRATOR TO SUMMARIZE THE OUTCOME OF DISCUSSIONS OF THE REVIEW COMMITTEE ON THE FOLLOWING ISSUES:

VERTEBRATE ANIMALS: The committee has no concerns regarding the number of animals proposed for use. All procedures within this application, including husbandry, surgical, anesthetic, analgesic and euthanasia, are appropriate and have been approved by the Investigator’s Institutional Animal Care and Use Committee. 1 K02 DC008521-01 9 CDRC NICK, T

SCIENTIFIC REVIEW ADMINISTRATOR’S NOTES: Responsible Conduct of Research – A course on the responsible conduct of research is mentioned but the course contents are not adequately described, so it is not possible to determine if the course fulfills the requirements. Clarification on this must be provided to the NIDCD training program director before any potential award can be made.

COMMITTEE BUDGET RECOMMENDATIONS: If approved for funding, the budget is recommended as requested.

NOTICE: The NIH has modified its policy regarding the receipt of amended applications. Detailed information can be found by accessing the following URL address: http://grants.nih.gov/grants/policy/amendedapps.htm

NIH announced implementation of Modular Research Grants in the December 18, 1998 issue of the NIH Guide to Grants and Contracts. The main feature of this concept is that grant applications (R01, R03, R21, R15) will request direct costs in $25,000 modules, without budget detail for individual categories. Further information can be obtained from the Modular Grants Web site at http://grants.nih.gov/grants/funding/modular/modular.htm

MEETING ROSTER

Communication Disorders Review Committee NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERS CDRC June 21, 2006 - June 22, 2006

CHAIRPERSON CATTS, HUGH W , PHD * GILLAM, RONALD B., PHD PROFESSOR AND CHAIR PROFESSOR UNIVERSITY OF KANSAS COMMUNICATION SCIENCES AND DISORDERS SPEECH-LANGUAGE-HEARING JESSE H. JONES COMMUNICATION CENTER LAWRENCE, KS 66045 UNIVERSITY OF TEXAS AT AUSTIN AUSTIN, TX 78705 CHAN, ROGER W, PHD * ASSISTANT PROFESSOR MEMBERS DEPARTMENT OF OTOLARYNGOLOGY - HNS UNIVERSITY OF TEXAS BEESON, PELAGIE M PHD, PHD * SOUTHWESTERN MEDICAL CENTER ASSOCIATE PROFESSOR DALLAS, TX 753909035 DEPARTMENT OF SPEECH AND HEARING SCIENCES UNIVERSITY OF ARIZONA TUCSON, AZ 85721 CHEATHAM, MARY ANN, PHD RESEARCH PROFESSOR DEPARTMENT OF COMMUNICATION SCIENCES & BIRK, DAVID E, PHD * DISORDERS PROFESSOR NORTHWESTERN UNIVERSITY DEPARTMENT OF PATHOLOGY, ANATOMY EVANSTON, IL 60208 AND CELL BIOLOGY JEFFERSON MEDICAL COLLEGE PHILADELPHIA, PA 19107 CLELAND, THOMAS A, PHD * PROFESSOR NEUROBIOLOGY AND BEHAVIOR BOATMAN, DANA F., PHD CORNELL UNIVERSITY ASSOCIATE PROFESSOR ITHACA, NY 14853 DEPARTMENT OF NEUROLOGY JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE COLLINS, LESLIE M., PHD * BALTIMORE, MD 212877222 ASSISTANT PROFESSOR DEPT OF ELECTRICAL & COMPUTER ENGINEERING DUKE UNIVERSITY BOPPANA, SURESH B, MD DURHAM, NC 27708 ASSOCIATE PROFESSOR DEPARTMENT OF PEDIATRICS & MICROBIOLOGY UNIVERSITY OF ALABAMA AT BIRMINGHAM DEMARIA, THOMAS F, PHD * BIRMINGHAM, AL 352331711 PROFESSOR DEPARTMENT OF OTOLARYNGOLOGY COLLEGE OF MEDICINE BOTTJER, SARAH W., PHD * OHIO STATE UNIVERSITY PROFESSOR COLUMBUS, OH 43210 DEPARTMENT OF BIOLOGICAL SCIENCES UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES, CA 900892520 DI LORENZO, PATRICIA M, PHD * PROFESSOR DEPT OF PSYCHOLOGY CAMPAGNARI, ANTHONY A, PHD * BINGHAMTON UNIVERSITY PROFESSOR BINGHAMTON, NY 139026000 DEPARTMENT OF MICROBIOLOGY AND IMMUNOLOGY SCHOOL OF MEDICINE AND BIOMEDICAL SCIENCE STATE UNIVERSITY OF NEW YORK AT BUFFALO DRONKERS, NINA F., PHD BUFFALO, NY 14214 DIRECTOR CENTER FOR APHASIA & RELATED DISORDERS VA NORTHERN CALIFORNIA HEALTH CARE SYSTEM CARNEY, LAUREL H., PHD MARTINEZ, CA 94553 PROFESSOR DEPT. OF BIOMEDICAL & CHEMICAL ENGINEERING, & ELECTRICAL ENGINEERING & COMPUTER SCIENCE EISENBERG, LAURIE S, PHD SYRACUSE UNIVERSITY SCIENTIST II SYRACUSE, NY 132445290 CHILDREN'S AUDITORY RESEARCH AND EVALUATION CENTER HOUSE EAR INSTITUTE LOS ANGELES, CA 90057

FADOOL, DEBRA A., PHD JOHNSON, JAMES F, PHD * ASSOCIATE PROFESSOR ASSOCIATE PROFESSOR PROGRAM IN NEUROSCIENCE & MOLECULAR DEPARTMENT OF PSYCHOLOGY BIOPHYSICS FLORIDA STATE UNIVERSITY FLORIDA STATE UNIVERSITY TALLAHASSEE, FL 32306 TALLAHASSEE, FL 32306 KANDLER, KARL , PHD * FOWLER, CAROL A, PHD * ASSOCIATE PROFESSOR PRESIDENT AND DIRECTOR OF RESEARCH DEPARTMENT OF NEURIOBIOLOGY HASKINS LABORATORIES UNIVERSITY OF PITTSBURGH NEW HAVEN, CT 06511 PITTSBURGH, PA 15261

FRITZ, ANDREAS , PHD * KANWAL, JAGMEET S., PHD ASSOCIATE PROFESSOR ASSOCIATE PROFESSOR DEPARTMENT OF BIOLOGY DEPARTMENT OF PHYSIOLOGY & BIOPHYSICS EMORY UNIVERSITY GEORGETOWN UNIVERSITY MEDICAL CENTER ATLANTA, GA 30322 WASHINGTON, DC 20007

FRITZSCH, BERND , PHD * KIDD, KENNETH K., MD, PHD * PROFESSOR PROFESSOR OF GENETICS, PSYCHIATRY AND BIOLOGY DEPARTMENT OF BIOMEDICAL SCIENCES DEPARTMENT OF GENETICS CREIGHTON UNIVERSITY YALE UNIVERSITY OMAHA, NE 68178 NEW HAVEN, CT 06520

GENTER, MARY BETH , PHD * LOCKWOOD, ALAN H, MD * ASSOCIATE PROFESSOR PROFESSOR OF NEUROLOGY AND NUCLEAR MEDICINE UNIVERSITY OF CINCINNATI VA. WESTERN NY HEALTHCARE SYSTEM ENVIRONMENTAL HEALTH CENTER FOR PET (115P) 144 KETTERING LABORATORY 3495 BAILEY AVENUE 3223 EDEN AVE. BUFFALO, NY 14215 CINCINNATI, OH 45267-005 MELLO, CLAUDIO V, MD, PHD * GRIGORENKO, ELENA L, PHD * ASSISTANT SCIENTIST ASSOCIATE PROFESSOR NEUROLOGICAL SCIENCES INSTITUTES CHILD STUDY CENTER OREGON HEALTH AND SCIENCES UNIVERSITY AND DEPARTMENT OF PSYCHOLOGY BEAVERTON, OR 97006 YALE UNIVERSITY NEW HAVEN, CT 065113720 MERCHANT, SAUMIL N., MD * ASSOCIATE PROFESSOR OF OTOLOGY & HELLER, STEFAN , PHD * LARYNGOLOGY ASSOCIATE PROFESSOR DEPARTMENT OF OTOLARYNGOLOGY DEPARTMENT OF OTOLARYNGOLOGY MASSACHUSETTS EYE & EAR INFIRMARY STANFORD UNIVERSITY BOSTON, MA 02114 SCHOOL OF MEDICINE STANFORD, CA 940358739 MICHEL, WILLIAM C, PHD * PROFESSOR HERNESS, M. SCOTT, PHD DEPARTMENT OF PHYSIOLOGY PROFESSOR UNIVERSITY OF UTAH DEPARTMENT OF ORAL BIOLOGY SALT LAKE CITY, UT 84108 COLLEGE OF DENTISTRY OHIO STATE UNIVERSITY MIDDLEBROOKS, JOHN C., PHD * COLUMBUS, OH 43210 PROFESSOR KRESGE HEARING RESEARCH INSTITUTE HULLAR, TIMOTHY E, MD * MEDICAL SCHOOL ASSOCIATE PROFESSOR UNIVERSITY OF MICHIGAN DEPT OF OTOLARYNGOLOGY ANN ARBOR, MI 481090506 WASHINGTON UNIVERSITY ST LOUIS, MO 63110 MOELLER, MARY PAT , PHD * DIRECTOR JIANG, JACK J, MD, PHD * CENTER FOR CHILDHOOD DEAFNESS ASSOCIATE PROFESSOR BOYS TOWN NATIONAL RESEARCH HOSPITAL DIVISON OF OTOLARYNGOLOGY OMAHA, NE 68131 DEPARTMENT OF SURGERY UNIVERSITY OF WISCONSIN MEDICAL SCHOOL MADISON, WI 53706

NEWLANDS, SHAWN D., MD, PHD * TYLER, RICHARD S, MSC, PHD * CHAIRMAN PROFESSOR AND DIRECTOR OF AUDIOLOGY DEPARTMENT OF OTOLARYNGOLOGY DEPARTMENT OF OTOLARYNGOLOGY UNIVERSITY OF TEXAS HEAD AND NECK SURGERY GALVESTON, TX 775550521 UNIVERSITY OF IOWA IOWA CITY, IA 52242 NIGHORN, ALAN J, PHD * ASSOCIATE PROFESSOR WARCHOL, MARK E., PHD * UNIVERSITY OF ARIZONA RESERACH ASSOCIATE PROFESSOR ARIZONA RESEARCH LABS CENTRAL INSTITUTE FOR THE DEAF TUCSON, AZ 85721 DEPARTMENTS OF OTOLARYNGOLOGY AND ANATOMY AND NEUROBIOLOGY RICE, MABEL L., PHD SCHOOL OF MEDICINE DISTINGUISHED PROFESSOR WASHINGTON UNIVERSITY CHILD LANGUAGE DOCTORAL PROGRAM ST. LOUIS, MO 63110 THE UNIVERSITY OF KANSAS LAWRENCE, KS 66045 WILBUR, RONNIE B, PHD * PROFESSOR & DIRECTOR RYUGO, DAVID K., PHD LINGUISTICS PROGRAM PROFESSOR PURDUE UNIVERSITY DEPARTMENT OF OTOLARYNGOLOGY WEST LAFAYETTE, IN 47907 & NEUROSCIENCE JOHNS HOPKINS UNIVERSITY WONG, PATRICK C, PHD * SCHOOL OF MEDICINE ASSISTANT PROFESSOR BALTIMORE, MD 21205 DEPARTMENT OF COMMUNICATION SCIENCES AND DISORDERS SCHICK, BRENDA S., PHD * NORTHWESTERN UNIVERSITY ASSOCIATE PROFESSOR EVANSTON, IL 60208 DEPARTMENT OF SPEECH, LANGUAGE, AND HEARING SCIENCES ZUO, JIAN , PHD * UNIVERSITY OF COLORADO ASSOCIATE MEMBER BOULDER, CO 803040409 ST. JUDE CHILDRENS HOSPITAL DEVELOPMENTAL NEUROBIOLOGY SHAKER, REZA , MD * MEMPHIS, TN 380152794 PROFESSOR AND CHIEF DIVISION OF GASTROENTEROLOGY AND HEPATOLOGY ZWIEBEL, LAURENCE J, PHD * MEDICAL COLLEGE OF WISCONSIN PROFESSOR MILWAUKEE, WI 53226 DEPARTMENT OF BIOLOGICAL SCIENCES VANDERBILT UNIVERSITY STEINSCHNEIDER, MITCHELL , MD, PHD NASHVILLE, TN 37235 PROFESSOR OF NEUROLOGY & NEUROSCIENCE DEPARTMENT OF NEUROLOGY SCIENTIFIC REVIEW ADMINISTRATOR ALBERT EINSTEIN COLLEGE OF MEDICINE OAKS, STANLEY C. JR. BRONX, NY 10461 SCIENTIFIC REVIEW ADMINISTRATOR DIVISION OF EXTRAMURAL ACTIVITIES STONE, JENNIFER S., PHD NATIONAL INSTITUTE ON DEAFNESS AND RESEARCH ASSISTANT PROFESSOR OTHER COMMUNICATION DISORDERS DEPARTMENT OF OTOLARYNGOLOGY BETHESDA, MD 20892 HEAD AND NECK SURGERY UNIVERSITY OF WASHINGTON STICK, MELISSA J., MPH, PHD SEATTLE, WA 98105 CHIEF, SCIENTIFIC REVIEW BRANCH DIVISION OF EXTRAMURAL ACTIVITIES THIBEAULT, SUSAN L, PHD * NATIONAL INSTITUTE OF DEAFNESS AND ASSISTANT PROFESSOR OTHER COMMUNICATION DISORDERS DEPARTMENT OF SURGERY/OTOLARYNOLOGY BETHESDA, MD 20892 HEAD AND NECK SURGERY UNIVERSITY OF UTAH GRANTS TECHNICAL ASSISTANT SALT LAKE CITY, UT 841322301 JOHNSON, NICHELLE GRANTS TECHNICAL ASSISTANT TURNER, CHRISTOPHER W., PHD SCIENTIFIC REVIEW BRANCH PROFESSOR DIVISION OF EXTRAMURAL ACTIVITIES DEPARTMENT OF PATHOLOGY & AUDIOLOGY NIDCD, NIH UNIVERSITY OF IOWA BETHESDA, MD 20892 IOWA CITY, IA 52242 * Temporary Member. For grant applications, temporary members may participate in the entire meeting or may review only selected applications as needed.

Consultants are required to absent themselves from the room during the review of any application if their presence would constitute or appear to constitute a conflict of interest.