Sensory Systems Review

Total Page:16

File Type:pdf, Size:1020Kb

Sensory Systems Review 7.29/9.09 Sensory Systems Review Sensory Vision Hearing Smell Taste Touch System Signal Photon Sound wave Chemical Chemical Pressure, vibration, temperature, chemicals (pain) Encoded Color (cones) Amplitude Odor concentration Taste type (sugary, salt, Modality (touch, temp, information Spatial location Frequency Odor identity umami, bitter, sour) pain) (receptive field) Location Intensity Intensity Timing Sensory cell: Photoreceptor Inner hair cell Olfactory neuron (ON), Taste cells arranged in Dorsal root ganglion (DRG) AP or graded - Graded potential - Graded potential cilia project into nasal taste bud cell, nerve endings in - Depolarized / releasing - Do not regenerate mucosa - AP periphery response? NT in the dark - AP - Lots of turnover - AP - Lots of turnover Signal Rhodopsin: GPCR bound Trp mechanoreceptor, Many GPCRs, each - Sweet, umami: GPCR - Trp mechanoreceptors receptor to 11-cis-retinal coupled to extracellular encoded by unique dimer - MEC mechanosensitive ion tip link and cytoskeleton gene (~1000 in - Bitter: GPCR monomer, channels mammals), each ON many - Degenerins expresses only 1 - Salt: Amiloride-sensitive receptor Na channels (degenerins) - Sour: Trp channel + GPCR Signal Photon converts retinal Trp channel opens -> K+, GPCR -> Golf -> - Bitter, sweet, umami: Activated transduction to all-trans-retinal -> Ca influx -> depolarization adenylate cyclase -> GPCR -> PLC, no details mechanoreceptors allow ion Rhodopsin activated -> of hair cell, NT release via cAMP -> open cAMP - Salt: Pass directly flux -> nerve depolarization pathway in G-protein transducin -> dense body gated Na, Ca channel -> through Na channel sensory cell phosphodiesterase -> depolarized cell fires AP cGMP levels decrease -> cGMP gated Na, Ca channel closes -> cell hyperpolarizes 1 Coding - “Off” retinal ganglion 1. Place code: where hair - 1 receptor/ON, Labelled line model: Taste Slowly adapting receptors -> cells firing in dark, “On” cell is on basilar though 1 receptor can cells are tuned to single signal pressure and shape of retinal ganglion cells membrane determines bind multiple odors taste modality, as are object firing in light frequency - Increasing odor secondary afferent Rapidly adapting receptors - - Center surround 2. Frequency code: concentration activates neurons > signal motion of objects antagonism mediated by auditory neuron fires at greater number of ONs on skin inhibitory horizontal and sound wave frequency - All ONS expressing 1 amacrine cells (true up to 1000 Hz) receptor project to - Mangocellular retinal 3. Stellate chopper cells same glomerulus in ganglion cells (RGCs) fire at frequency of sound olfactory bulb encode “where” info, wave, bushy cells respond Parvocellular RGCs to start/end of stimulus: encode “what” info sound localization Mechanisms 1. Ca influx relieves 1. Ca influx activates 1. Activated GPCR is Not discussed Slow or fast depending on of inhibition of guanylyl myosin motors, physically phosphorylated and stimulus type cyclase -> cGMP levels move and close Trp desensitized adaptation increase -> channel 2. Ca influx -> photoreceptor 2. Outer hair cells vibrate calmodulin -> close depolarizes and dampen basilar cAMP gated channel 2. Pupil dilates in low membrane light, constricts in high 3. Middle ear muscles light contract and reduce stapes vibration Mechanisms 1 photon -> 1 rhodopsin Tympanic membrane is Not discussed Not discussed Not discussed of -> activates 100 35X larger than oval transducins -> each window amplification activates 1 phosphodiesterase -> cleaves 1,000 cGMP: 105 cGMP cleaved / photon Information Photoreceptor -> Bipolar Hair cell -> Cochlear Olfactory neuron -> Taste cell -> Bipolar - Touch/Proprioception: pathway in cell -> Ganglion cell -> nucleus (medulla) -> Olfactory bulb neuron -> Gustatory DRG -> Spinal cord dorsal Thalamus LGN -> Cortex Superior olivary nucleus glomeruli -> Pyriform nucleus (medulla) -> column -> synapse in dorsal CNS layer IV, Superior (pons) -> Inferior cortex, hippocampus, Thalamus VPN -> column nucleus (medulla) -> colliculus (Tectum), colliculus -> Thalamus amygdala Gustatory cortex Cross midline -> Thalamus Superchiasmatic nucleus MGN -> primary auditory ** Only sensory system (temporal lobe) VPN -> Post-central gyrus (Hypothalamus) cortex (temporal lobe) that doesn’t relay (parietal) 2 Dorsal flow (to region through thalamus - Nocioception: DRG neuron MT): “where” before reaching cortex -> synapse in spinal cord Ventral stream (to dorsal horn -> Cross midline region IT): “what” -> Spinothalamic tract -> Thalamus VPN -> cortex Cortex Ocular dominance Cortical neurons grouped Not discussed Not discussed Somatosensory map on mapping columns (each eye by sound frequency cortex; inputs grouped segregated), orientation depending on spatial columns, “blobs” location on body segregated by color Important Myopia, astigmatism, Conductive deafness, Not discussed Not discussed Referred pain, phantom pathologies rod disease, macular sensorineural deafness, limb, hyperanalgesia, degeneration, color central deafness chronic neuropathic pain blindness, optic neuritis, glaucoma, diabetic retinopathy, cataracts, amblyopia, agnosia 3 MIT OpenCourseWare http://ocw.mit.edu 7.29J / 9.09J Cellular Neurobiology Spring 2012 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms..
Recommended publications
  • Representation of Sound Localization Cues in the Auditory Thalamus of the Barn Owl
    Proc. Natl. Acad. Sci. USA Vol. 94, pp. 10421–10425, September 1997 Neurobiology Representation of sound localization cues in the auditory thalamus of the barn owl LARRY PROCTOR* AND MASAKAZU KONISHI Division of Biology, MC 216–76, California Institute of Technology, Pasadena, CA 91125 Contributed by Masakazu Konishi, July 14, 1997 ABSTRACT Barn owls can localize a sound source using responses of neurons throughout the N.Ov to binaural sound either the map of auditory space contained in the optic tectum localization cues to determine what, if any, transformations or the auditory forebrain. The auditory thalamus, nucleus occur in the representation of auditory information. ovoidalis (N.Ov), is situated between these two auditory areas, and its inactivation precludes the use of the auditory forebrain METHODS for sound localization. We examined the sources of inputs to the N.Ov as well as their patterns of termination within the Adult barn owls (Tyto alba), while under ketamineydiazepam nucleus. We also examined the response of single neurons anesthesia (Ketaset, Aveco, Chicago; 25 mgykg diazepam; 1 within the N.Ov to tonal stimuli and sound localization cues. mgykg intramuscular injection), were placed in a stereotaxic Afferents to the N.Ov originated with a diffuse population of device that held the head tilted downward at an angle of 30° neurons located bilaterally within the lateral shell, core, and from the horizontal plane. A stainless steel plate was attached medial shell subdivisions of the central nucleus of the inferior to the rostral cranium with dental cement, and a reference pin colliculus. Additional afferent input originated from the ip- was glued to the cranium on the midline in the plane between silateral ventral nucleus of the lateral lemniscus.
    [Show full text]
  • Master Project Interactive Media Design Master Program Södertörn Högskola
    Master Project Interactive Media Design Master Program Södertörn Högskola Supervisor: Minna Räsänen External: Annika Waern (MobileLife Centre) Student: Syed Naseh Title: Mobile SoundAR: Your Phone on Your Head Table of Contents Abstract....................................................................................................................................... 3 Keywords.................................................................................................................................... 3 ACM Classification Keywords................................................................................................... 3 General Terms............................................................................................................................. 3 Introduction.................................................................................................................................4 Immersion issues without head-tracking.....................................................................................5 Front-back confusion.................................................................................................... 5 Interaural Time Difference ITD ................................................................................... 5 Interaural Intensity Difference IID .............................................................................. 6 Design Problem...........................................................................................................................7 Design Methodology...................................................................................................................7
    [Show full text]
  • Discrimination of Sound Source Positions Following Auditory Spatial
    Discrimination of sound source positions following auditory spatial adaptation Stephan Getzmann Ruhr-Universität Bochum, Kognitions- & Umweltpsychologie; Email: [email protected] Abstract auditory contrast effect in sound localization increases with in- Based on current models of auditory spatial adaptation in human creasing adapter duration, indicating a time dependence in the sound localization, the present study evaluated whether auditory strength of adaptation. Analogously, improvements in spatial dis- discrimination of two sound sources in the median plane (MP) crimination should be stronger following a long adapter, and improves after presentation of an appropriate adapter sound. Fif- weaker following a short adapter. In order to test this assumption, teen subjects heard two successively presented target sounds (200- in a third condition a short adapter sound preceded the target ms white noise, 500 ms interstimulus interval), which differed sounds. Best performance was expected in the strong-adapter con- slightly in elevation. Subjects had to decide whether the first dition, followed by the weak- and the no-adapter condition. sound was emitted above or below the second. When a long adap- ter sound (3-s white noise emitted at the elevation of the first tar- 2. Method get sound) preceded the stimulus pair, performance improved in Fifteen normal hearing subjects (age range 19-52 years) volun- speed and accuracy. When a short adapter sound (500 ms) pre- teered as listeners in the experiment which was conducted in a ceded, performance improved only insignificantly in comparison dark, sound-proof, anechoic room. In the subjects' median plane, with a no-adapter control condition. These results may be linked 31 broad-band loudspeakers (Visaton SC 5.9, 5 x 9 cm) were to previous findings concerning sound localization after the pres- mounted ranging from -31° to +31° elevation.
    [Show full text]
  • Of Interaural Time Difference by Spiking Neural Networks Zihan Pan, Malu Zhang, Jibin Wu, and Haizhou Li, Fellow, IEEE
    JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Multi-Tones’ Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Networks Zihan Pan, Malu Zhang, Jibin Wu, and Haizhou Li, Fellow, IEEE Abstract—Inspired by the mammal’s auditory localization (in humans 20Hz to 2kHz) for MSO and high frequencies (in pathway, in this paper we propose a pure spiking neural humans >2kHz) for LSO [7]. From the information flow point network (SNN) based computational model for precised sound of view, the location-dependent information from the sounds, localization in the noisy real-world environment, and implement this algorithm in a real-time robotic system with microphone or aforementioned time or intensity differences, are encoded array. The key of this model relies on the MTPC scheme, which into spiking pulses in the MSO and LSO. After that, they are encodes the interaural time difference (ITD) cues into spike projected to the inferior colliculus (IC) in the mid-brain, where patterns. This scheme naturally follows the functional structures the IC integrates both cues for estimating the sound source of human auditory localization system, rather than artificially direction [8]. The most successful model for ITD encoding computing of time difference of arrival. Besides, it highlights the advantages of SNN, such as event-driven and power efficiency. in MSO and cortex decoding might be the “place theory” by The MTPC is pipeliend with two different SNN architectures, an American psychologist named Lloyd Jeffress [9], which is the convolutional SNN and recurrent SNN, by which it shows the also referred to as Jeffress model hereafter.
    [Show full text]
  • Binaural Sound Localization
    CHAPTER 5 BINAURAL SOUND LOCALIZATION 5.1 INTRODUCTION We listen to speech (as well as to other sounds) with two ears, and it is quite remarkable how well we can separate and selectively attend to individual sound sources in a cluttered acoustical environment. In fact, the familiar term ‘cocktail party processing’ was coined in an early study of how the binaural system enables us to selectively attend to individual con- versations when many are present, as in, of course, a cocktail party [23]. This phenomenon illustrates the important contributionthat binaural hearing makes to auditory scene analysis, byenablingusto localizeandseparatesoundsources. Inaddition, the binaural system plays a major role in improving speech intelligibility in noisy and reverberant environments. The primary goal of this chapter is to provide an understanding of the basic mechanisms underlying binaural localization of sound, along with an appreciation of how binaural pro- cessing by the auditory system enhances the intelligibility of speech in noisy acoustical environments, and in the presence of competing talkers. Like so many aspects of sensory processing, the binaural system offers an existence proof of the possibility of extraordinary performance in sound localization, and signal separation, but it does not yet providea very complete picture of how this level of performance can be achieved with the contemporary tools of computational auditory scene analysis (CASA). We first summarize in Sec. 5.2 the major physical factors that underly binaural percep- tion, and we briefly summarize some of the classical physiological findings that describe mechanisms that could support some aspects of binaural processing. In Sec. 5.3 we briefly review some of the basic psychoacoustical results which have motivated the development Computational Auditory Scene Analysis.
    [Show full text]
  • Neuroethology in Neuroscience Why Study an Exotic Animal
    Neuroethology in Neuroscience or Why study an exotic animal Nobel prize in Physiology and Medicine 1973 Karl von Frisch Konrad Lorenz Nikolaas Tinbergen for their discoveries concerning "organization and elicitation of individual and social behaviour patterns". Behaviour patterns become explicable when interpreted as the result of natural selection, analogous with anatomical and physiological characteristics. This year's prize winners hold a unique position in this field. They are the most eminent founders of a new science, called "the comparative study of behaviour" or "ethology" (from ethos = habit, manner). Their first discoveries were made on insects, fishes and birds, but the basal principles have proved to be applicable also on mammals, including man. Nobel prize in Physiology and Medicine 1973 Karl von Frisch Konrad Lorenz Nikolaas Tinbergen Ammophila the sand wasp Black headed gull Niko Tinbergen’s four questions? 1. How the behavior of the animal affects its survival and reproduction (function)? 2. By how the behavior is similar or different from behaviors in other species (phylogeny)? 3. How the behavior is shaped by the animal’s own experiences (ontogeny)? 4. How the behavior is manifested at the physiological level (mechanism)? Neuroethology as a sub-field in brain research • A large variety of research models and a tendency to focus on esoteric animals and systems (specialized behaviors). • Studying animals while ignoring the relevancy to humans. • Studying the brain in the context of the animal’s natural behavior. • Top-down approach. Archer fish Prof. Ronen Segev Ben-Gurion University Neuroethology as a sub-field in brain research • A large variety of research models and a tendency to focus on esoteric animals and systems (specialized behaviors).
    [Show full text]
  • Neural Development of Binaural Tuning Through Hebbian Learning Predicts Frequency-Dependent Best Delays
    11692 • The Journal of Neuroscience, August 10, 2011 • 31(32):11692–11696 Behavioral/Systems/Cognitive Neural Development of Binaural Tuning through Hebbian Learning Predicts Frequency-Dependent Best Delays Bertrand Fontaine1,2 and Romain Brette1,2 1Laboratoire Psychologie de la Perception, CNRS and Universite´ Paris Descartes, 75006 Paris, France, and 2Equipe Audition, De´partement d’Etudes Cognitives, Ecole Normale Supe´rieure, 75005 Paris, France Birds use microsecond differences in the arrival times of the sounds at the two ears to infer the location of a sound source in the horizontal plane. These interaural time differences (ITDs) are encoded by binaural neurons which fire more when the ITD matches their “best delay.” In the textbook model of sound localization, the best delays of binaural neurons reflect the differences in axonal delays of their monaural inputs, but recent observations have cast doubts on this classical view because best delays were found to depend on preferred frequency.Here,weshowthattheseobservationsareinfactconsistentwiththenotionthatbestdelaysarecreatedbydifferencesinaxonal delays, provided ITD tuning is created during development through spike-timing-dependent plasticity: basilar membrane filtering results in correlations between inputs to binaural neurons, which impact the selection of synapses during development, leading to the observed distribution of best delays. Introduction BD Ͻ 1/(2CF), solid curves). From a functional point of view, In many species, interaural time difference (ITD) is the main cue
    [Show full text]
  • Sound Localization in Birds - Physical and Neurophysiological Mechanisms of Sound Localization in the Chicken
    Technische Universität München Lehrstuhl für Zoologie Sound Localization in Birds - Physical and Neurophysiological Mechanisms of Sound Localization in the Chicken Hans A. Schnyder Vollständiger Abdruck der von der Fakultät Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften genehmigten Dissertation. Vorsitzender: Prof. Dr. Rudolf Fries Prüfer der Dissertation: 1. Prof. Dr. Harald Luksch 2. Prof. Dr. Bernhard Seeber Die Dissertation wurde am 09.08.2017 bei der Technischen Universität München eingereicht und durch die Fakultät Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt am 22.01.2018 angenommen. Summary Accurate sound source localization in three-dimensional space is essential for an animal’s orientation and survival. While the horizontal position can be determined by interaural time and intensity differences, localization in elevation was thought to require external structures that modify sound before it reaches the tympanum. Here I show that in birds even without external structures such as pinnae or feather ruffs, the simple shape of their head induces sound modifications that depend on the elevation of the source. A model of localization errors shows that these cues are sufficient to locate sounds in the vertical plane. These results suggest that the head of all birds induces acoustic cues for sound localization in the vertical plane, even in the absence of external ears. Based on these findings, I investigated the neuronal processing of spatial acoustic cues in the midbrain of birds. The avian midbrain is a computational hub for the integration of auditory and visual information. While at early stages of the auditory pathway time and intensity information is processed separately, information from both streams converges at the inferior colliculus (IC).
    [Show full text]
  • Temporal Weighting of Binaural Cues for Sound Localization Andrew D
    Temporal weighting of binaural cues for sound localization Andrew D. Brown A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2012 Reading Committee: George C. Stecker, Chair Julie A. Bierer Joseph A. Sisneros Lynne A. Werner Authorized to Offer Degree: Speech and Hearing Sciences University of Washington Abstract Temporal weighting of binaural cues for sound localization Andrew D. Brown Chair of the Supervisory Committee: Associate Professor G. Christopher Stecker Normal-hearing human listeners localize sound sources accurately and effortlessly across an effectively limitless variety of acoustic environments by responding to, among other cues, small differences in the timing and intensity of sound arriving at the two ears (interaural time differences [ITD] and interaural level differences [ILD]). This dissertation explores the temporal dynamics of normal sensitivity to these cues by assessing normal-hearing listeners’ weighting of ITD or ILD cues over the duration of binaurally dynamic stimuli in a variety of psychophysical experiments. Experimental stimuli were rapidly amplitude- modulated signals mimicking those typical of echoic environments (e.g., rooms), including rapid sequences of filtered impulses carrying varied ITD or ILD over their duration, and pairs and trains of pairs of clicks carrying discrete ITD or ILD in first and second click of each pair. Consistent with past studies of the precedence effect in sound localization, onset dominance in lateralization and binaural adaptation, data demonstrated a prime salience of ILD and especially ITD at signal onset. Somewhat more surprisingly, data also indicated greater sensitivity to post-onset ILD than post-onset ITD (i.e., a weaker precedence effect for ILD).
    [Show full text]
  • A Critical Period for the Recovery of Sound Localization Accuracy Following Monaural Occlusion in the Barn Owl’
    0270.6474/84/0404-1012$02.00/O The Journal of Neuroscience Copyright 0 Society for Neuroscience Vol. 4, No. 4, pp. 1012-1020 Printed in U.S.A. April 1984 A CRITICAL PERIOD FOR THE RECOVERY OF SOUND LOCALIZATION ACCURACY FOLLOWING MONAURAL OCCLUSION IN THE BARN OWL’ ERIC I. KNUDSEN,’ PHYLLIS F. KNUDSEN, AND STEVEN D. ESTERLY Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305 Received September 2, 1983; Accepted November 23, 1983 Abstract We studied the ability of barn owls to recover accurate sound localization after being raised with one ear occluded. Most of the owls had ear plugs inserted before they reached adult size, and therefore they never experienced normal adult localization cues until their ear plugs were removed. Upon removal of their ear plugs, these owls exhibited large systematic sound localization errors. The rate at which they recovered accurate localization decreased with the age of the bird at the time of plug removal, and recovery essentially ceased when owls reached 38 to 42 weeks of age. We interpret this age as the end of a critical period for the consolidation of associations between auditory cues and locations in space. Owls that had experienced adult localization cues for a short period of time before ear plugging recovered normal accuracy rapidly, even if they remained plugged well past the end of the critical period. This suggests that a brief exposure to normal adult cues early in the critical period is sufficient to enable the recovery of localization accuracy much later in life. Barn owls localize sounds by associating small differ- Materials and Methods ences in the timing and intensity of sound at the two Eleven barn owls (Tyto &a) served as experimental ears with locations in space (Knudsen and Konishi, 1979; subjects.
    [Show full text]
  • 1.32 Neural Computation Theories of Learning
    1.32 Neural Computation Theories of Learningq Samat Moldakarimov and Terrence J Sejnowski, University of California – San Diego, La Jolla, CA, United States; and Salk Institute for Biological Studies, La Jolla, CA, United States Ó 2017 Elsevier Ltd. All rights reserved. 1.32.1 Introduction 579 1.32.2 Hebbian Learning 580 1.32.3 Unsupervised Learning 581 1.32.4 Supervised Learning 581 1.32.5 Reinforcement Learning 583 1.32.6 Spike Timing–Dependent Plasticity 584 1.32.7 Plasticity of Intrinsic Excitability 586 1.32.8 Homeostatic Plasticity 586 1.32.9 Complexity of Learning 587 1.32.10 Conclusions 588 References 588 1.32.1 Introduction The anatomical discoveries in the 19th century and the physiological studies in the 20th century showed that the brain was made of networks of neurons connected together through synapses (Kandel et al., 2012). These discoveries led to a theory that learning could be the consequence of changes in the strengths of the synapses (Hebb, 1949). The Hebb’s rule for synaptic plasticity states that: When an axon of cell A is near enough to excite cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’sefficiency, as one of the cells firing B, is increased. Hebb (1949). This postulate was experimentally confirmed in the hippocampus, where high-frequency stimulation (HFS) of a presynaptic neuron causes long-term potentiation (LTP) in the synapses connecting it to the postsynaptic neurons (Bliss and Lomo, 1973). LTP takes place only if the postsynaptic cell is also active and sufficiently depolarized (Kelso et al., 1986).
    [Show full text]
  • Chapter 12: Sound Localization and the Auditory Scene
    Chapter 12: Sound Localization and the Auditory Scene • What makes it possible to tell where a sound is coming from in space? • When we are listening to a number of musical instruments playing at the same time, how can we perceptually separate the sounds coming from the different instruments? • Why does music sound better in some concert halls than in others? Chapter 12: Sound Localization and the Auditory Scene • What makes it possible to tell where a sound is coming from in space? • When we are listening to a number of musical instruments playing at the same time, how can we perceptually separate the sounds coming from the different instruments? • Why does music sound better in some concert halls than in others? Auditory Localization; the ‘Where’ pathway for the auditory system • Auditory space - surrounds an observer and exists wherever there is sound • Researchers study how sounds are localized in space by using – Azimuth coordinates - position left to right – Elevation coordinates - position up and down – Distance coordinates - position from observer Auditory Localization • On average, people can localize sounds – Directly in front of them most accurately – To the sides and behind their heads least accurately Location cues are not contained in the receptor cells like on the retina in vision; location for sounds must be calculated through other cues. 3 primary cues for auditory localization: 1. Interaural time difference (ITD) 2. Interaural level difference (ILD) 3. Head-related transfer function (HRTF) Cues for Auditory Location •
    [Show full text]