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MITOCW | 9: Receptive Fields - Intro to Neural Computation
MITOCW | 9: Receptive Fields - Intro to Neural Computation MICHALE FEE: So today, we're going to introduce a new topic, which is related to the idea of fine- tuning curves, and that is the notion of receptive fields. So most of you have probably been, at least those of you who've taken 9.01 or 9.00 maybe, have been exposed to the idea of what a receptive field is. The idea is basically that in sensory systems neurons receive input from the sensory periphery, and neurons generally have some kind of sensory stimulus that causes them to spike. And so one of the classic examples of how to find receptive fields comes from the work of Huble and Wiesel. So I'll show you some movies made from early experiments of Huble-Wiesel where they are recording in the visual cortex of the cat. So they place a fine metal electrode into a primary visual cortex, and they present. So then they anesthetize the cat so the cat can't move. They open the eye, and the cat's now looking at a screen that looks like this, where they play a visual stimulus. And they actually did this with essentially a slide projector that they could put a card in front of that had a little hole in it, for example, that allowed a spot of light to project onto the screen. And then they can move that spot of light around while they record from neurons in visual cortex and present different visual stimuli to the retina. -
Neural Coding and the Statistical Modeling of Neuronal Responses By
Neural coding and the statistical modeling of neuronal responses by Jonathan Pillow A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Center for Neural Science New York University Jan 2005 Eero P. Simoncelli TABLE OF CONTENTS LIST OF FIGURES v INTRODUCTION 1 1 Characterization of macaque retinal ganglion cell responses using spike-triggered covariance 5 1.1NeuralCharacterization.................... 9 1.2One-DimensionalModelsandtheSTA............ 12 1.3Multi-DimensionalModelsandSTCAnalysis........ 16 1.4 Separability and Subspace STC ................ 21 1.5 Subunit model . ....................... 23 1.6ModelValidation........................ 25 1.7Methods............................. 28 2 Estimation of a Deterministic IF model 37 2.1Leakyintegrate-and-firemodel................. 40 2.2Simulationresultsandcomparison............... 41 ii 2.3Recoveringthelinearkernel.................. 42 2.4Recoveringakernelfromneuraldata............. 44 2.5Discussion............................ 46 3 Estimation of a Stochastic, Recurrent IF model 48 3.1TheModel............................ 52 3.2TheEstimationProblem.................... 54 3.3ComputationalMethodsandNumericalResults....... 57 3.4TimeRescaling......................... 60 3.5Extensions............................ 61 3.5.1 Interneuronalinteractions............... 62 3.5.2 Nonlinear input ..................... 63 3.6Discussion............................ 64 AppendixA:ProofofLog-ConcavityofModelLikelihood..... 65 AppendixB:ComputingtheLikelihoodGradient........ -
Neural Oscillations As a Signature of Efficient Coding in the Presence of Synaptic Delays Matthew Chalk1*, Boris Gutkin2,3, Sophie Dene` Ve2
RESEARCH ARTICLE Neural oscillations as a signature of efficient coding in the presence of synaptic delays Matthew Chalk1*, Boris Gutkin2,3, Sophie Dene` ve2 1Institute of Science and Technology Austria, Klosterneuburg, Austria; 2E´ cole Normale Supe´rieure, Paris, France; 3Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia Abstract Cortical networks exhibit ’global oscillations’, in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a ’prediction error’ while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code. DOI: 10.7554/eLife.13824.001 *For correspondence: Introduction [email protected] Oscillations are a prominent feature of cortical activity. In sensory areas, one typically observes Competing interests: The ’global oscillations’ in the gamma-band range (30–80 Hz), alongside single neuron responses that authors declare that no are irregular and sparse (Buzsa´ki and Wang, 2012; Yu and Ferster, 2010). -
The Roles and Functions of Cutaneous Mechanoreceptors Kenneth O Johnson
455 The roles and functions of cutaneous mechanoreceptors Kenneth O Johnson Combined psychophysical and neurophysiological research has nerve ending that is sensitive to deformation in the resulted in a relatively complete picture of the neural mechanisms nanometer range. The layers function as a series of of tactile perception. The results support the idea that each of the mechanical filters to protect the extremely sensitive recep- four mechanoreceptive afferent systems innervating the hand tor from the very large, low-frequency stresses and strains serves a distinctly different perceptual function, and that tactile of ordinary manual labor. The Ruffini corpuscle, which is perception can be understood as the sum of these functions. located in the connective tissue of the dermis, is a rela- Furthermore, the receptors in each of those systems seem to be tively large spindle shaped structure tied into the local specialized for their assigned perceptual function. collagen matrix. It is, in this way, similar to the Golgi ten- don organ in muscle. Its association with connective tissue Addresses makes it selectively sensitive to skin stretch. Each of these Zanvyl Krieger Mind/Brain Institute, 338 Krieger Hall, receptor types and its role in perception is discussed below. The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218-2689, USA; e-mail: [email protected] During three decades of neurophysiological and combined Current Opinion in Neurobiology 2001, 11:455–461 psychophysical and neurophysiological studies, evidence has accumulated that links each of these afferent types to 0959-4388/01/$ — see front matter © 2001 Elsevier Science Ltd. All rights reserved. a distinctly different perceptual function and, furthermore, that shows that the receptors innervated by these afferents Abbreviations are specialized for their assigned functions. -
Center Surround Receptive Field Structure of Cone Bipolar Cells in Primate Retina
Vision Research 40 (2000) 1801–1811 www.elsevier.com/locate/visres Center surround receptive field structure of cone bipolar cells in primate retina Dennis Dacey a,*, Orin S. Packer a, Lisa Diller a, David Brainard b, Beth Peterson a, Barry Lee c a Department of Biological Structure, Uni6ersity of Washington, Box 357420, Seattle, WA 98195-7420, USA b Department of Psychology, Uni6ersity of California Santa Barbara, Santa Barbara, CA, USA c Max Planck Institute for Biophysical Chemistry, Gottingen, Germany Received 28 September 1999; received in revised form 5 January 2000 Abstract In non-mammalian vertebrates, retinal bipolar cells show center-surround receptive field organization. In mammals, recordings from bipolar cells are rare and have not revealed a clear surround. Here we report center-surround receptive fields of identified cone bipolar cells in the macaque monkey retina. In the peripheral retina, cone bipolar cell nuclei were labeled in vitro with diamidino-phenylindole (DAPI), targeted for recording under microscopic control, and anatomically identified by intracellular staining. Identified cells included ‘diffuse’ bipolar cells, which contact multiple cones, and ‘midget’ bipolar cells, which contact a single cone. Responses to flickering spots and annuli revealed a clear surround: both hyperpolarizing (OFF) and depolarizing (ON) cells responded with reversed polarity to annular stimuli. Center and surround dimensions were calculated for 12 bipolar cells from the spatial frequency response to drifting, sinusoidal luminance modulated gratings. The frequency response was bandpass and well fit by a difference of Gaussians receptive field model. Center diameters were all two to three times larger than known dendritic tree diameters for both diffuse and midget bipolar cells in the retinal periphery. -
The Visual System: Higher Visual Processing
The Visual System: Higher Visual Processing Primary visual cortex The primary visual cortex is located in the occipital cortex. It receives visual information exclusively from the contralateral hemifield, which is topographically represented and wherein the fovea is granted an extended representation. Like most cortical areas, primary visual cortex consists of six layers. It also contains, however, a prominent stripe of white matter in its layer 4 - the stripe of Gennari - consisting of the myelinated axons of the lateral geniculate nucleus neurons. For this reason, the primary visual cortex is also referred to as the striate cortex. The LGN projections to the primary visual cortex are segregated. The axons of the cells from the magnocellular layers terminate principally within sublamina 4Ca, and those from the parvocellular layers terminate principally within sublamina 4Cb. Ocular dominance columns The inputs from the two eyes also are segregated within layer 4 of primary visual cortex and form alternating ocular dominance columns. Alternating ocular dominance columns can be visualized with autoradiography after injecting radiolabeled amino acids into one eye that are transported transynaptically from the retina. Although the neurons in layer 4 are monocular, neurons in the other layers of the same column combine signals from the two eyes, but their activation has the same ocular preference. Bringing together the inputs from the two eyes at the level of the striate cortex provide a basis for stereopsis, the sensation of depth perception provided by binocular disparity, i.e., when an image falls on non-corresponding parts of the two retinas. Some neurons respond to disparities beyond the plane of fixation (far cells), while others respond to disparities in front of the plane of the fixation (near cells). -
An Optogenetic Approach to Understanding the Neural Circuits of Fear Joshua P
Controlling the Elements: An Optogenetic Approach to Understanding the Neural Circuits of Fear Joshua P. Johansen, Steffen B.E. Wolff, Andreas Lüthi, and Joseph E. LeDoux Neural circuits underlie our ability to interact in the world and to learn adaptively from experience. Understanding neural circuits and how circuit structure gives rise to neural firing patterns or computations is fundamental to our understanding of human experience and behavior. Fear conditioning is a powerful model system in which to study neural circuits and information processing and relate them to learning and behavior. Until recently, technological limitations have made it difficult to study the causal role of specific circuit elements during fear conditioning. However, newly developed optogenetic tools allow researchers to manipulate individual circuit components such as anatom- ically or molecularly defined cell populations, with high temporal precision. Applying these tools to the study of fear conditioning to control specific neural subpopulations in the fear circuit will facilitate a causal analysis of the role of these circuit elements in fear learning and memory. By combining this approach with in vivo electrophysiological recordings in awake, behaving animals, it will also be possible to determine the functional contribution of specific cell populations to neural processing in the fear circuit. As a result, the application of optogenetics to fear conditioning could shed light on how specific circuit elements contribute to neural coding and to fear learning and memory. Furthermore, this approach may reveal general rules for how circuit structure and neural coding within circuits gives rise to sensory experience and behavior. Key Words: Electrophysiology, fear conditioning, learning and circuits and the identification of sites of neural plasticity in these memory, neural circuits, neural plasticity, optogenetics circuits. -
Seeing in Three Dimensions: the Neurophysiology of Stereopsis Gregory C
Review DeAngelis – Neurophysiology of stereopsis Seeing in three dimensions: the neurophysiology of stereopsis Gregory C. DeAngelis From the pair of 2-D images formed on the retinas, the brain is capable of synthesizing a rich 3-D representation of our visual surroundings. The horizontal separation of the two eyes gives rise to small positional differences, called binocular disparities, between corresponding features in the two retinal images. These disparities provide a powerful source of information about 3-D scene structure, and alone are sufficient for depth perception. How do visual cortical areas of the brain extract and process these small retinal disparities, and how is this information transformed into non-retinal coordinates useful for guiding action? Although neurons selective for binocular disparity have been found in several visual areas, the brain circuits that give rise to stereoscopic vision are not very well understood. I review recent electrophysiological studies that address four issues: the encoding of disparity at the first stages of binocular processing, the organization of disparity-selective neurons into topographic maps, the contributions of specific visual areas to different stereoscopic tasks, and the integration of binocular disparity and viewing-distance information to yield egocentric distance. Some of these studies combine traditional electrophysiology with psychophysical and computational approaches, and this convergence promises substantial future gains in our understanding of stereoscopic vision. We perceive our surroundings vividly in three dimen- lished the first reports of disparity-selective neurons in the sions, even though the image formed on the retina of each primary visual cortex (V1, or area 17) of anesthetized cats5,6. -
6 Optogenetic Actuation, Inhibition, Modulation and Readout for Neuronal Networks Generating Behavior in the Nematode Caenorhabditis Elegans
Mario de Bono, William R. Schafer, Alexander Gottschalk 6 Optogenetic actuation, inhibition, modulation and readout for neuronal networks generating behavior in the nematode Caenorhabditis elegans 6.1 Introduction – the nematode as a genetic model in systems neurosciencesystems neuroscience Elucidating the mechanisms by which nervous systems process information and gen- erate behavior is among the fundamental problems of biology. The complexity of our brain and plasticity of our behaviors make it challenging to understand even simple human actions in terms of molecular mechanisms and neural activity. However the molecular machines and operational features of our neural circuits are often found in invertebrates, so that studying flies and worms provides an effective way to gain insights into our nervous system. Caenorhabditis elegans offers special opportunities to study behavior. Each of the 302 neurons in its nervous system can be identified and imaged in live animals [1, 2], and manipulated transgenically using specific promoters or promoter combinations [3, 4, 5, 6]. The chemical synapses and gap junctions made by every neuron are known from electron micrograph reconstruction [1]. Importantly, forward genetics can be used to identify molecules that modulate C. elegans’ behavior. Forward genetic dis- section of behavior is powerful because it requires no prior knowledge. It allows mol- ecules to be identified regardless of in vivo concentration, and focuses attention on genes that are functionally important. The identity and expression patterns of these molecules then provide entry points to study the molecular mechanisms and neural circuits controlling the behavior. Genetics does not provide the temporal resolution required to study neural circuit function directly. -
Exploring the Function of Neural Oscillations in Early Sensory Systems
FOCUSED REVIEW published: 15 May 2010 doi: 10.3389/neuro.01.010.2010 Exploring the function of neural oscillations in early sensory systems Kilian Koepsell1*, Xin Wang 2, Judith A. Hirsch 2 and Friedrich T. Sommer1* 1 Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA, USA 2 Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA Neuronal oscillations appear throughout the nervous system, in structures as diverse as the cerebral cortex, hippocampus, subcortical nuclei and sense organs. Whether neural rhythms contribute to normal function, are merely epiphenomena, or even interfere with physiological processing are topics of vigorous debate. Sensory pathways are ideal for investigation of oscillatory activity because their inputs can be defined. Thus, we will focus on sensory systems Edited by: S. Murray Sherman, as we ask how neural oscillations arise and how they might encode information about the University of Chicago, USA stimulus. We will highlight recent work in the early visual pathway that shows how oscillations Reviewed by: can multiplex different types of signals to increase the amount of information that spike trains Michael J. Friedlander, Baylor College of Medicine, USA encode and transmit. Last, we will describe oscillation-based models of visual processing and Jose-Manuel Alonso, Sociedad Espanola explore how they might guide further research. de Neurociencia, Spain; University of Connecticut, USA; State University Keywords: LGN, retina, visual coding, oscillations, multiplexing of New York, USA Naoum P. Issa, University of Chicago, USA NEURAL OSCILLATIONS IN EARLY However, the autocorrelograms are subject to *Correspondence: SENSORY SYSTEMS confounds caused by the refractory period and Oscillatory neural activity has been observed spectral peaks often fail to reveal weak rhythms. -
COGS 101A: Sensation and Perception 1
COGS 101A: Sensation and Perception 1 Virginia R. de Sa Department of Cognitive Science UCSD Lecture 4: Coding Concepts – Chapter 2 Course Information 2 • Class web page: http://cogsci.ucsd.edu/ desa/101a/index.html • Professor: Virginia de Sa ? I’m usually in Chemistry Research Building (CRB) 214 (also office in CSB 164) ? Office Hours: Monday 5-6pm ? email: desa at ucsd ? Research: Perception and Learning in Humans and Machines For your Assistance 3 TAS: • Jelena Jovanovic OH: Wed 2-3pm CSB 225 • Katherine DeLong OH: Thurs noon-1pm CSB 131 IAS: • Jennifer Becker OH: Fri 10-11am CSB 114 • Lydia Wood OH: Mon 12-1pm CSB 114 Course Goals 4 • To appreciate the difficulty of sensory perception • To learn about sensory perception at several levels of analysis • To see similarities across the sensory modalities • To become more attuned to multi-sensory interactions Grading Information 5 • 25% each for 2 midterms • 32% comprehensive final • 3% each for 6 lab reports - due at the end of the lab • Bonus for participating in a psych or cogsci experiment AND writing a paragraph description of the study You are responsible for knowing the lecture material and the assigned readings. Read the readings before class and ask questions in class. Academic Dishonesty 6 The University policy is linked off the course web page. You will all have to sign a form in section For this class: • Labs are done in small groups but writeups must be in your own words • There is no collaboration on midterms and final exam Last Class 7 We learned about the cells in the Retina This Class 8 Coding Concepts – Every thing after transduction (following Chris Johnson’s notes) Remember:The cells in the retina are neurons 9 neurons are specialized cells that transmit electrical/chemical information to other cells neurons generally receive a signal at their dendrites and transmit it electrically to their soma and axon. -
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks Wenjie Luo∗ Yujia Li∗ Raquel Urtasun Richard Zemel Department of Computer Science University of Toronto {wenjie, yujiali, urtasun, zemel}@cs.toronto.edu Abstract We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough areas in the image to capture information about large objects. We introduce the notion of an effective receptive field, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field. We analyze the effective receptive field in several architecture designs, and the effect of nonlinear activations, dropout, sub-sampling and skip connections on it. This leads to suggestions for ways to address its tendency to be too small. 1 Introduction Deep convolutional neural networks (CNNs) have achieved great success in a wide range of problems in the last few years. In this paper we focus on their application to computer vision: where they are the driving force behind the significant improvement of the state-of-the-art for many tasks recently, including image recognition [10, 8], object detection [17, 2], semantic segmentation [12, 1], image captioning [20], and many more. One of the basic concepts in deep CNNs is the receptive field, or field of view, of a unit in a certain layer in the network. Unlike in fully connected networks, where the value of each unit depends on the entire input to the network, a unit in convolutional networks only depends on a region of the input.