SPIKE TRIGGERED APPROACHES. Odelia Schwartz Computational Neuroscience Course 2017
|
|
- Edwina Holmes
- 5 years ago
- Views:
Transcription
1 SPIKE TRIGGERED APPROACHES Odelia Schwartz Computational Neuroscience Course 2017
2 LINEAR NONLINEAR MODELS Linear Nonlinear o Often constrain to some form of Linear, Nonlinear computations, e.g. visual receptive fields or filters, followed by nonlinear interactions
3 LINEAR NONLINEAR MODELS What type of nonlinearities?
4 DESCRIPTIVE MODELS: DIVISIVE NORMALIZATION o Canonical computation (Carandini, Heeger, 2013) o Has been applied to primary visual cortex (V1) o More broadly, to other systems and modalities, multimodal processing, value encoding, etc
5 DESCRIPTIVE MODELS: COMPLEX CELLS AND INVARIANCE o after Adelson & Bergen, 1985
6 FITTING DESCRIPTIVE MODELS TO DATA Linear Nonlinear Poisson
7 ROADMAP o Simple cell traditional approach o Simple cell (STA) o When STA fails o Complex cell (STC) o Another example (STC) o More generic model with multiple filters
8 REMINDER: RECEPTIVE FIELD Hubel and Wiesel, 1959 Stimuli Spikes
9 Primary Visual Cortex (V1) REMINDER: RECEPTIVE FIELD
10 RECEPTIVE FIELD o Response of a filter = inner/dot product/projection of filter with stimulus
11 ROADMAP o Simple cell traditional approach o Simple cell (STA) o When STA fails o Complex cell (STC) o Another example (STC) o More generic model with multiple filters
12
13 SPIKE-TRIGGERED AVERAGE
14 SPIKE-TRIGGERED AVERAGE
15 SPIKE-TRIGGERED AVERAGE
16 STA SPIKE-TRIGGERED AVERAGE
17 EFFECT OF NONLINEARITY IN MODEL?
18 EFFECT OF NONLINEARITY IN MODEL?
19 EFFECT OF NONLINEARITY IN MODEL? o Nonlinearity sets negative filter responses to zero (firing rates are positive)
20 SPIKE-TRIGGERED AVERAGE (STA) o Stimuli that are more similar to filter are more likely to elicit a spike
21 Model: SPIKE-TRIGGERED AVERAGE (STA)
22 SPIKE-TRIGGERED AVERAGE (STA) STA response Random filter response
23 Geometrical view: change in the mean Large filter response likely to elicit spike SPIKE-TRIGGERED AVERAGE (STA) Spike stimuli Raw stimuli STA response Random filter response
24 SPIKE-TRIGGERED AVERAGE (STA) STA We can also recover the nonlinearity
25 SPIKE-TRIGGERED AVERAGE (STA) We can also recover the nonlinearity
26 STEPS 1.Assume a model (filter/s, nonlinearity) (we assumed one filter and asymmetric nonlinearity) 2. Estimate model components (filter/s, nonlinearity) (we looked for changes in mean: STA)
27 ROADMAP o Simple cell traditional approach o Simple cell (STA) o When STA fails o Complex cell (STC) o Another example (STC) o More generic model with multiple filters
28 BUT STA DOES NOT ALWAYS WORK STA filter??
29 BUT STA DOES NOT ALWAYS WORK STA filter!
30 WHAT HAPPENED?? Nonlinearity sets negative filter responses to positive (firing rates are positive)
31 WHAT HAPPENED?? Spike stimuli Raw stimuli Model filter STA filter! Random filter response Large or small filter response likely to elicit spike Mean stimuli eliciting spikes = 0
32 CHANGE IN THE VARIANCE Spike stimuli Raw stimuli Positive 0 Model filter Negative STA filter! Large or small filter response likely to elicit spike Random filter response
33 SPIKE-TRIGGERED COVARIANCE (STC) Spike stimuli Raw stimuli Positive 0 Model filter Negative Standard algebra techniques (eigenvector analysis) recovers changes in variance Random filter response
34 SPIKE-TRIGGERED COVARIANCE (STC) Model filter Random filter response We can also recover the nonlinearity
35 STEPS 1.Assume a model (filter/s, nonlinearity) (we assumed one filter and symmetric nonlinearity) 2. Estimate model components (filter/s, nonlinearity) (STA failed) (we looked for changes in variance: STC)
36 SPIKE-TRIGGERED COVARIANCE (STC) o Figure from Schwartz et al. 2006; see also Rust et al. 2005, de Ruyter & Bialek 1988 o Approach estimates linear subspace and nonlinearity
37 ROADMAP o Simple cell traditional approach o Simple cell (STA) o When STA fails o Complex cell (STC) o Another example (STC) o More generic model with multiple filters
38 SPIKE-TRIGGERED COVARIANCE (STC)
39 CHANGE IN VARIANCE (STC) Spike stimuli Raw stimuli
40 CHANGE IN VARIANCE (STC) STA filter!
41 CHANGE IN VARIANCE (STC)
42 STEPS 1.Assume a model (filter/s, nonlinearity) (we assumed more than one filter and symmetric nonlinearity) 2. Estimate model components (filter/s, nonlinearity) (STA failed) (we looked for changes in variance: STC)
43 ROADMAP o Simple cell traditional approach o Simple cell (STA) o When STA fails o Complex cell (STC) o Another example (STC) o More generic model with multiple filters
44 SECOND FILTER SUPPRESSIVE (E.G., DIVISIVE)
45 SECOND FILTER SUPPRESSIVE (E.G., DIVISIVE) Second filter brings about reduction in variance! Spike stimuli Raw stimuli
46 SECOND FILTER SUPPRESSIVE (E.G., DIVISIVE) Second filter brings about reduction in variance!
47 STEPS 1.Assume a model (filter/s, nonlinearity) (we assumed more than one filter and symmetric nonlinearity) 2. Estimate model components (filter/s, nonlinearity) (we looked for changes in variance, this time reduced variance: STC)
48 SPIKE TRIGGERED APPROACES Change in the mean (STA) Complex cell Divisive normalization Changes in the variance (STC)
49 ROADMAP o Simple cell traditional approach o Simple cell (STA) o When STA fails o Complex cell (STC) o Another example (STC) o More generic model with multiple filters
50 MORE GENERAL CLASS OF MODEL Look for changes in both the mean and the variance
51 APPLICATION: V1 EXPERIMENT
52 V1 NEURAL DATA: SPIKE-TRIGGERED COVARIANCE o Example V1 neuron estimated filters from Rust et al. 2005
53 V1 NEURAL DATA: RECALL THE STANDARD MODELS But Data show multiple filters (excitatory and suppressive) for both. Are these really two different classes of neurons, or is there a continuum??
54 STC ISSUES: HOW MANY SPIKES? Filter estimate depends on: Spatial and time dimensionality of input stimulus (smaller = better estimate) Number of spikes (more = better estimate)
55 STC CAVEATS Analysis forces filters that are 90 degrees apart! Filters should not be taken literally as physiological mechanisms
56 STC CAVEATS But true filters are linear combinations of original ( span the same subspace )
57 STC CAVEATS Analysis forces filters that are 90 degrees apart! Filters should not be taken literally as physiological mechanisms Spiking in neuron may be non Poisson (bursts;; refractory period;; etc.) Filters should not be taken literally as physiological mechanisms There might be more filters affecting neural response than what analysis finds STC guaranteed to work only for Gaussian stimuli There might be changes that are not in the mean or variance (other approaches;; e.g., info theory)
58 EXAMPLE: FITTING LN-LN MODEL o o o Figure from Pagan et al describing retina and V1 with subunits (see Rust et al. 2005; Vintch et al. 2015) In Pagan et al addressing higher level brain areas See also Rowekamp et al addressing area V2
59 EXAMPLE: GENERALIZED LINEAR MODEL Stimulus filter Stochastic Nonlinearity spiking e x Post-spike filter Neuron 1 Coupling filters Neuron 2 o Figure from Pillow et al., 2008, describing retina
1/12/2017. Computational neuroscience. Neurotechnology.
Computational neuroscience Neurotechnology https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/ 1 Neurotechnology http://www.lce.hut.fi/research/cogntech/neurophysiology Recording
More informationEfficient and direct estimation of a neural subunit model for sensory coding
To appear in: Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada. December 3-6, 22. Efficient and direct estimation of a neural subunit model for sensory coding Brett Vintch Andrew D. Zaharia
More informationStatistical models for neural encoding
Statistical models for neural encoding Part 1: discrete-time models Liam Paninski Gatsby Computational Neuroscience Unit University College London http://www.gatsby.ucl.ac.uk/ liam liam@gatsby.ucl.ac.uk
More informationSUPPLEMENTARY INFORMATION
Supplementary discussion 1: Most excitatory and suppressive stimuli for model neurons The model allows us to determine, for each model neuron, the set of most excitatory and suppresive features. First,
More informationMid Year Project Report: Statistical models of visual neurons
Mid Year Project Report: Statistical models of visual neurons Anna Sotnikova asotniko@math.umd.edu Project Advisor: Prof. Daniel A. Butts dab@umd.edu Department of Biology Abstract Studying visual neurons
More informationPhenomenological Models of Neurons!! Lecture 5!
Phenomenological Models of Neurons!! Lecture 5! 1! Some Linear Algebra First!! Notes from Eero Simoncelli 2! Vector Addition! Notes from Eero Simoncelli 3! Scalar Multiplication of a Vector! 4! Vector
More informationCHARACTERIZATION OF NONLINEAR NEURON RESPONSES
CHARACTERIZATION OF NONLINEAR NEURON RESPONSES Matt Whiteway whit8022@umd.edu Dr. Daniel A. Butts dab@umd.edu Neuroscience and Cognitive Science (NACS) Applied Mathematics and Scientific Computation (AMSC)
More informationCHARACTERIZATION OF NONLINEAR NEURON RESPONSES
CHARACTERIZATION OF NONLINEAR NEURON RESPONSES Matt Whiteway whit8022@umd.edu Dr. Daniel A. Butts dab@umd.edu Neuroscience and Cognitive Science (NACS) Applied Mathematics and Scientific Computation (AMSC)
More informationDimensionality reduction in neural models: an information-theoretic generalization of spiketriggered average and covariance analysis
to appear: Journal of Vision, 26 Dimensionality reduction in neural models: an information-theoretic generalization of spiketriggered average and covariance analysis Jonathan W. Pillow 1 and Eero P. Simoncelli
More informationCharacterization of Nonlinear Neuron Responses
Characterization of Nonlinear Neuron Responses Mid Year Report Matt Whiteway Department of Applied Mathematics and Scientific Computing whit822@umd.edu Advisor Dr. Daniel A. Butts Neuroscience and Cognitive
More informationWhat is the neural code? Sekuler lab, Brandeis
What is the neural code? Sekuler lab, Brandeis What is the neural code? What is the neural code? Alan Litke, UCSD What is the neural code? What is the neural code? What is the neural code? Encoding: how
More informationStructured hierarchical models for neurons in the early visual system
Structured hierarchical models for neurons in the early visual system by Brett Vintch A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Center for
More informationThe homogeneous Poisson process
The homogeneous Poisson process during very short time interval Δt there is a fixed probability of an event (spike) occurring independent of what happened previously if r is the rate of the Poisson process,
More informationencoding and estimation bottleneck and limits to visual fidelity
Retina Light Optic Nerve photoreceptors encoding and estimation bottleneck and limits to visual fidelity interneurons ganglion cells light The Neural Coding Problem s(t) {t i } Central goals for today:
More informationCharacterization of Nonlinear Neuron Responses
Characterization of Nonlinear Neuron Responses Final Report Matt Whiteway Department of Applied Mathematics and Scientific Computing whit822@umd.edu Advisor Dr. Daniel A. Butts Neuroscience and Cognitive
More informationDimensionality reduction in neural models: An information-theoretic generalization of spike-triggered average and covariance analysis
Journal of Vision (2006) 6, 414 428 http://journalofvision.org/6/4/9/ 414 Dimensionality reduction in neural models: An information-theoretic generalization of spike-triggered average and covariance analysis
More informationModeling and Characterization of Neural Gain Control. Odelia Schwartz. A dissertation submitted in partial fulfillment
Modeling and Characterization of Neural Gain Control by Odelia Schwartz A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Center for Neural Science
More informationIdentifying Functional Bases for Multidimensional Neural Computations
LETTER Communicated by Jonathan Pillow Identifying Functional Bases for Multidimensional Neural Computations Joel Kaardal jkaardal@physics.ucsd.edu Jeffrey D. Fitzgerald jfitzgerald@physics.ucsd.edu Computational
More informationSpatiotemporal Elements of Macaque V1 Receptive Fields
Neuron, Vol. 46, 945 956, June 16, 2005, Copyright 2005 by Elsevier Inc. DOI 10.1016/j.neuron.2005.05.021 Spatiotemporal Elements of Macaque V1 Receptive Fields Nicole C. Rust, 1, * Odelia Schwartz, 3
More informationExercises. Chapter 1. of τ approx that produces the most accurate estimate for this firing pattern.
1 Exercises Chapter 1 1. Generate spike sequences with a constant firing rate r 0 using a Poisson spike generator. Then, add a refractory period to the model by allowing the firing rate r(t) to depend
More informationNeural Coding: Integrate-and-Fire Models of Single and Multi-Neuron Responses
Neural Coding: Integrate-and-Fire Models of Single and Multi-Neuron Responses Jonathan Pillow HHMI and NYU http://www.cns.nyu.edu/~pillow Oct 5, Course lecture: Computational Modeling of Neuronal Systems
More informationFlexible Gating of Contextual Influences in Natural Vision. Odelia Schwartz University of Miami Oct 2015
Flexible Gating of Contextual Influences in Natural Vision Odelia Schwartz University of Miami Oct 05 Contextual influences Perceptual illusions: no man is an island.. Review paper on context: Schwartz,
More informationLateral organization & computation
Lateral organization & computation review Population encoding & decoding lateral organization Efficient representations that reduce or exploit redundancy Fixation task 1rst order Retinotopic maps Log-polar
More informationThis cannot be estimated directly... s 1. s 2. P(spike, stim) P(stim) P(spike stim) =
LNP cascade model Simplest successful descriptive spiking model Easily fit to (extracellular) data Descriptive, and interpretable (although not mechanistic) For a Poisson model, response is captured by
More informationConvolutional Spike-triggered Covariance Analysis for Neural Subunit Models
Published in: Advances in Neural Information Processing Systems 28 (215) Convolutional Spike-triggered Covariance Analysis for Neural Subunit Models Anqi Wu 1 Il Memming Park 2 Jonathan W. Pillow 1 1 Princeton
More informationVisual motion processing and perceptual decision making
Visual motion processing and perceptual decision making Aziz Hurzook (ahurzook@uwaterloo.ca) Oliver Trujillo (otrujill@uwaterloo.ca) Chris Eliasmith (celiasmith@uwaterloo.ca) Centre for Theoretical Neuroscience,
More informationConvolutional Spike-triggered Covariance Analysis for Neural Subunit Models
Convolutional Spike-triggered Covariance Analysis for Neural Subunit Models Anqi Wu Il Memming Park 2 Jonathan W. Pillow Princeton Neuroscience Institute, Princeton University {anqiw, pillow}@princeton.edu
More informationComparison of objective functions for estimating linear-nonlinear models
Comparison of objective functions for estimating linear-nonlinear models Tatyana O. Sharpee Computational Neurobiology Laboratory, the Salk Institute for Biological Studies, La Jolla, CA 937 sharpee@salk.edu
More informationSUPPLEMENTARY INFORMATION
Spatio-temporal correlations and visual signaling in a complete neuronal population Jonathan W. Pillow 1, Jonathon Shlens 2, Liam Paninski 3, Alexander Sher 4, Alan M. Litke 4,E.J.Chichilnisky 2, Eero
More informationNeural Encoding Models
Neural Encoding Models Maneesh Sahani maneesh@gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit University College London Term 1, Autumn 2011 Studying sensory systems x(t) y(t) Decoding: ˆx(t)= G[y(t)]
More informationNeural Encoding. Mark van Rossum. January School of Informatics, University of Edinburgh 1 / 58
1 / 58 Neural Encoding Mark van Rossum School of Informatics, University of Edinburgh January 2015 2 / 58 Overview Understanding the neural code Encoding: Prediction of neural response to a given stimulus
More informationFeatures and dimensions: Motion estimation in fly vision
Features and dimensions: Motion estimation in fly vision William Bialek a and Rob R. de Ruyter van Steveninck b a Joseph Henry Laboratories of Physics, and Lewis Sigler Institute for Integrative Genomics
More information+ + ( + ) = Linear recurrent networks. Simpler, much more amenable to analytic treatment E.g. by choosing
Linear recurrent networks Simpler, much more amenable to analytic treatment E.g. by choosing + ( + ) = Firing rates can be negative Approximates dynamics around fixed point Approximation often reasonable
More informationSean Escola. Center for Theoretical Neuroscience
Employing hidden Markov models of neural spike-trains toward the improved estimation of linear receptive fields and the decoding of multiple firing regimes Sean Escola Center for Theoretical Neuroscience
More informationComparison of receptive fields to polar and Cartesian stimuli computed with two kinds of models
Supplemental Material Comparison of receptive fields to polar and Cartesian stimuli computed with two kinds of models Motivation The purpose of this analysis is to verify that context dependent changes
More informationModeling Convergent ON and OFF Pathways in the Early Visual System
Modeling Convergent ON and OFF Pathways in the Early Visual System The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Gollisch,
More informationSecond Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models
Using Minimum and Maximum Mutual Information Models Jeffrey D. Fitzgerald 1,2, Ryan J. Rowekamp 1,2, Lawrence C. Sincich 3, Tatyana O. Sharpee 1,2 * 1 Computational Neurobiology Laboratory, The Salk Institute
More informationLearning quadratic receptive fields from neural responses to natural signals: information theoretic and likelihood methods
Learning quadratic receptive fields from neural responses to natural signals: information theoretic and likelihood methods Kanaka Rajan Lewis-Sigler Institute for Integrative Genomics Princeton University
More informationTwo-dimensional adaptation in the auditory forebrain
Two-dimensional adaptation in the auditory forebrain Tatyana O. Sharpee, Katherine I. Nagel and Allison J. Doupe J Neurophysiol 106:1841-1861, 2011. First published 13 July 2011; doi:10.1152/jn.00905.2010
More informationMembrane equation. VCl. dv dt + V = V Na G Na + V K G K + V Cl G Cl. G total. C m. G total = G Na + G K + G Cl
Spiking neurons Membrane equation V GNa GK GCl Cm VNa VK VCl dv dt + V = V Na G Na + V K G K + V Cl G Cl G total G total = G Na + G K + G Cl = C m G total Membrane with synaptic inputs V Gleak GNa GK
More informationStatistical models for neural encoding, decoding, information estimation, and optimal on-line stimulus design
Statistical models for neural encoding, decoding, information estimation, and optimal on-line stimulus design Liam Paninski Department of Statistics and Center for Theoretical Neuroscience Columbia University
More informationPrimer: The deconstruction of neuronal spike trains
Primer: The deconstruction of neuronal spike trains JOHNATAN ALJADEFF 1,2, BENJAMIN J. LANSDELL 3, ADRIENNE L. FAIRHALL 4 AND DAVID KLEINFELD 1,5 1 Department of Physics, University of California, San
More informationSynaptic Input. Linear Model of Synaptic Transmission. Professor David Heeger. September 5, 2000
Synaptic Input Professor David Heeger September 5, 2000 The purpose of this handout is to go a bit beyond the discussion in Ch. 6 of The Book of Genesis on synaptic input, and give some examples of how
More informationEmergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces
LETTER Communicated by Bartlett Mel Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces Aapo Hyvärinen Patrik Hoyer Helsinki University
More informationComputation in a single neuron: Hodgkin and Huxley revisited
Computation in a single neuron: Hodgkin and Huxley revisited Blaise Agüera y Arcas, 1 Adrienne L. Fairhall, 2,3 and William Bialek 2,4 1 Rare Books Library, Princeton University, Princeton, New Jersey
More informationInferring synaptic conductances from spike trains under a biophysically inspired point process model
Inferring synaptic conductances from spike trains under a biophysically inspired point process model Kenneth W. Latimer The Institute for Neuroscience The University of Texas at Austin latimerk@utexas.edu
More informationEfficient Coding. Odelia Schwartz 2017
Efficient Coding Odelia Schwartz 2017 1 Levels of modeling Descriptive (what) Mechanistic (how) Interpretive (why) 2 Levels of modeling Fitting a receptive field model to experimental data (e.g., using
More informationTransformation of stimulus correlations by the retina
Transformation of stimulus correlations by the retina Kristina Simmons (University of Pennsylvania) and Jason Prentice, (now Princeton University) with Gasper Tkacik (IST Austria) Jan Homann (now Princeton
More informationThe Bayesian Brain. Robert Jacobs Department of Brain & Cognitive Sciences University of Rochester. May 11, 2017
The Bayesian Brain Robert Jacobs Department of Brain & Cognitive Sciences University of Rochester May 11, 2017 Bayesian Brain How do neurons represent the states of the world? How do neurons represent
More informationA Deep Learning Model of the Retina
A Deep Learning Model of the Retina Lane McIntosh and Niru Maheswaranathan Neurosciences Graduate Program, Stanford University Stanford, CA {lanemc, nirum}@stanford.edu Abstract The represents the first
More informationConsider the following spike trains from two different neurons N1 and N2:
About synchrony and oscillations So far, our discussions have assumed that we are either observing a single neuron at a, or that neurons fire independent of each other. This assumption may be correct in
More informationModeling Surround Suppression in V1 Neurons with a Statistically-Derived Normalization Model
Presented at: NIPS-98, Denver CO, 1-3 Dec 1998. Pulished in: Advances in Neural Information Processing Systems eds. M. S. Kearns, S. A. Solla, and D. A. Cohn volume 11, pages 153--159 MIT Press, Cambridge,
More informationNeural characterization in partially observed populations of spiking neurons
Presented at NIPS 2007 To appear in Adv Neural Information Processing Systems 20, Jun 2008 Neural characterization in partially observed populations of spiking neurons Jonathan W. Pillow Peter Latham Gatsby
More informationHigh-dimensional geometry of cortical population activity. Marius Pachitariu University College London
High-dimensional geometry of cortical population activity Marius Pachitariu University College London Part I: introduction to the brave new world of large-scale neuroscience Part II: large-scale data preprocessing
More informationInformation Theory. Mark van Rossum. January 24, School of Informatics, University of Edinburgh 1 / 35
1 / 35 Information Theory Mark van Rossum School of Informatics, University of Edinburgh January 24, 2018 0 Version: January 24, 2018 Why information theory 2 / 35 Understanding the neural code. Encoding
More informationNeural Codes and Neural Rings: Topology and Algebraic Geometry
Neural Codes and Neural Rings: Topology and Algebraic Geometry Ma191b Winter 2017 Geometry of Neuroscience References for this lecture: Curto, Carina; Itskov, Vladimir; Veliz-Cuba, Alan; Youngs, Nora,
More informationLikelihood-Based Approaches to
Likelihood-Based Approaches to 3 Modeling the Neural Code Jonathan Pillow One of the central problems in systems neuroscience is that of characterizing the functional relationship between sensory stimuli
More informationComparing integrate-and-fire models estimated using intracellular and extracellular data 1
Comparing integrate-and-fire models estimated using intracellular and extracellular data 1 Liam Paninski a,b,2 Jonathan Pillow b Eero Simoncelli b a Gatsby Computational Neuroscience Unit, University College
More informationVisual Motion Analysis by a Neural Network
Visual Motion Analysis by a Neural Network Kansai University Takatsuki, Osaka 569 1095, Japan E-mail: fukushima@m.ieice.org (Submitted on December 12, 2006) Abstract In the visual systems of mammals, visual
More informationEfficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model
ACCEPTED FOR NIPS: DRAFT VERSION Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model Sander M. Bohte CWI, Life Sciences Amsterdam, The Netherlands S.M.Bohte@cwi.nl September
More informationRobust regression and non-linear kernel methods for characterization of neuronal response functions from limited data
Robust regression and non-linear kernel methods for characterization of neuronal response functions from limited data Maneesh Sahani Gatsby Computational Neuroscience Unit University College, London Jennifer
More informationSimple Cell Receptive Fields in V1.
Simple Cell Receptive Fields in V1. The receptive field properties of simple cells in V1 were studied by Hubel and Wiesel [65][66] who showed that many cells were tuned to the orientation of edges and
More informationConvolutional networks. Sebastian Seung
Convolutional networks Sebastian Seung Convolutional network Neural network with spatial organization every neuron has a location usually on a grid Translation invariance synaptic strength depends on locations
More informationAn Introductory Course in Computational Neuroscience
An Introductory Course in Computational Neuroscience Contents Series Foreword Acknowledgments Preface 1 Preliminary Material 1.1. Introduction 1.1.1 The Cell, the Circuit, and the Brain 1.1.2 Physics of
More informationAnalyzing Neural Responses to Natural Signals: Maximally Informative Dimensions
ARTICLE Communicated by Pamela Reinagel Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions Tatyana Sharpee sharpee@phy.ucsf.edu Sloan Swartz Center for Theoretical Neurobiology
More informationNatural Image Statistics
Natural Image Statistics A probabilistic approach to modelling early visual processing in the cortex Dept of Computer Science Early visual processing LGN V1 retina From the eye to the primary visual cortex
More informationFinding a Basis for the Neural State
Finding a Basis for the Neural State Chris Cueva ccueva@stanford.edu I. INTRODUCTION How is information represented in the brain? For example, consider arm movement. Neurons in dorsal premotor cortex (PMd)
More informationCharles Cadieu, Minjoon Kouh, Anitha Pasupathy, Charles E. Connor, Maximilian Riesenhuber and Tomaso Poggio
Charles Cadieu, Minjoon Kouh, Anitha Pasupathy, Charles E. Connor, Maximilian Riesenhuber and Tomaso Poggio J Neurophysiol 98:733-75, 27. First published Jun 27, 27; doi:.52/jn.265.26 You might find this
More informationModelling stochastic neural learning
Modelling stochastic neural learning Computational Neuroscience András Telcs telcs.andras@wigner.mta.hu www.cs.bme.hu/~telcs http://pattern.wigner.mta.hu/participants/andras-telcs Compiled from lectures
More informationScholarOne, 375 Greenbrier Drive, Charlottesville, VA, 22901
Page 1 of 43 Information maximization as a principle for contrast gain control Journal: Manuscript ID: Manuscript Type: Manuscript Section: Conflict of Interest: Date Submitted by the Author: Keywords:
More informationMaximally Informative Stimulus Energies in the Analysis of Neural Responses to Natural Signals
Maximally Informative Stimulus Energies in the Analysis of Neural Responses to Natural Signals Kanaka Rajan*, William Bialek Joseph Henry Laboratories of Physics and Lewis Sigler Institute for Integrative
More informationGatsby Theoretical Neuroscience Lectures: Non-Gaussian statistics and natural images Parts I-II
Gatsby Theoretical Neuroscience Lectures: Non-Gaussian statistics and natural images Parts I-II Gatsby Unit University College London 27 Feb 2017 Outline Part I: Theory of ICA Definition and difference
More informationNeural coding Ecological approach to sensory coding: efficient adaptation to the natural environment
Neural coding Ecological approach to sensory coding: efficient adaptation to the natural environment Jean-Pierre Nadal CNRS & EHESS Laboratoire de Physique Statistique (LPS, UMR 8550 CNRS - ENS UPMC Univ.
More informationInformation Theory and Neuroscience II
John Z. Sun and Da Wang Massachusetts Institute of Technology October 14, 2009 Outline System Model & Problem Formulation Information Rate Analysis Recap 2 / 23 Neurons Neuron (denoted by j) I/O: via synapses
More informationNonlinear reverse-correlation with synthesized naturalistic noise
Cognitive Science Online, Vol1, pp1 7, 2003 http://cogsci-onlineucsdedu Nonlinear reverse-correlation with synthesized naturalistic noise Hsin-Hao Yu Department of Cognitive Science University of California
More informationEfficient coding of natural images with a population of noisy Linear-Nonlinear neurons
Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons Yan Karklin and Eero P. Simoncelli NYU Overview Efficient coding is a well-known objective for the evaluation and
More informationModelling temporal structure (in noise and signal)
Modelling temporal structure (in noise and signal) Mark Woolrich, Christian Beckmann*, Salima Makni & Steve Smith FMRIB, Oxford *Imperial/FMRIB temporal noise: modelling temporal autocorrelation temporal
More informationNeural Spike Train Analysis 1: Introduction to Point Processes
SAMSI Summer 2015: CCNS Computational Neuroscience Summer School Neural Spike Train Analysis 1: Introduction to Point Processes Uri Eden BU Department of Mathematics and Statistics July 27, 2015 Spikes
More informationMathematical Tools for Neuroscience (NEU 314) Princeton University, Spring 2016 Jonathan Pillow. Homework 8: Logistic Regression & Information Theory
Mathematical Tools for Neuroscience (NEU 34) Princeton University, Spring 206 Jonathan Pillow Homework 8: Logistic Regression & Information Theory Due: Tuesday, April 26, 9:59am Optimization Toolbox One
More informationTilt-aftereffect and adaptation of V1 neurons
Tilt-aftereffect and adaptation of V1 neurons Dezhe Jin Department of Physics The Pennsylvania State University Outline The tilt aftereffect (TAE) Classical model of neural basis of TAE Neural data on
More informationUsing Statistics of Natural Images to Facilitate Automatic Receptive Field Analysis
Using Statistics of Natural Images to Facilitate Automatic Receptive Field Analysis Matthew Harrison Division of Applied Mathematics Brown University Providence, RI 292 USA Matthew Harrison@Brown.EDU Stuart
More informationEXTENSIONS OF ICA AS MODELS OF NATURAL IMAGES AND VISUAL PROCESSING. Aapo Hyvärinen, Patrik O. Hoyer and Jarmo Hurri
EXTENSIONS OF ICA AS MODELS OF NATURAL IMAGES AND VISUAL PROCESSING Aapo Hyvärinen, Patrik O. Hoyer and Jarmo Hurri Neural Networks Research Centre Helsinki University of Technology P.O. Box 5400, FIN-02015
More informationMemories Associated with Single Neurons and Proximity Matrices
Memories Associated with Single Neurons and Proximity Matrices Subhash Kak Oklahoma State University, Stillwater Abstract: This paper extends the treatment of single-neuron memories obtained by the use
More informationarxiv:q-bio/ v1 [q-bio.nc] 2 May 2005
Features and dimensions: Motion estimation in fly vision William Bialek a and Rob R. de Ruyter van Steveninck b a Joseph Henry Laboratories of Physics, b Department of Molecular Biology, and the Lewis
More informationMaximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model Jonathan W. Pillow, Liam Paninski, and Eero P. Simoncelli Howard Hughes Medical Institute Center for Neural Science New York
More informationContextual modulation of V1 receptive fields depends on their spatial symmetry
DOI./s8-8-- Contextual modulation of V receptive fields depends on their spatial symmetry Tatyana O. Sharpee & Jonathan D. Victor Received: April 8 /Revised: 9 June 8 /Accepted: June 8 # Springer Science
More informationSimultaneous activity measurements on intact mammalian retina
Simultaneous activity measurements on intact mammalian retina Phil Nelson University of Pennsylvania For these slides see: www.physics.upenn.edu/~pcn Cartoon by Larry Gonick Part IV: Parallel recordings
More informationCIFAR Lectures: Non-Gaussian statistics and natural images
CIFAR Lectures: Non-Gaussian statistics and natural images Dept of Computer Science University of Helsinki, Finland Outline Part I: Theory of ICA Definition and difference to PCA Importance of non-gaussianity
More informationSpatiotemporal Response Properties of Optic-Flow Processing Neurons
Article Spatiotemporal Response Properties of Optic-Flow Processing Neurons Franz Weber, 1,3, * Christian K. Machens, 2 and Alexander Borst 1 1 Department of Systems and Computational Neurobiology, Max-Planck-Institute
More informationTHE retina in general consists of three layers: photoreceptors
CS229 MACHINE LEARNING, STANFORD UNIVERSITY, DECEMBER 2016 1 Models of Neuron Coding in Retinal Ganglion Cells and Clustering by Receptive Field Kevin Fegelis, SUID: 005996192, Claire Hebert, SUID: 006122438,
More informationA Monte Carlo Sequential Estimation for Point Process Optimum Filtering
2006 International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21, 2006 A Monte Carlo Sequential Estimation for Point Process Optimum Filtering
More informationInformation maximization in a network of linear neurons
Information maximization in a network of linear neurons Holger Arnold May 30, 005 1 Introduction It is known since the work of Hubel and Wiesel [3], that many cells in the early visual areas of mammals
More information!) + log(t) # n i. The last two terms on the right hand side (RHS) are clearly independent of θ and can be
Supplementary Materials General case: computing log likelihood We first describe the general case of computing the log likelihood of a sensory parameter θ that is encoded by the activity of neurons. Each
More informationTuning tuning curves. So far: Receptive fields Representation of stimuli Population vectors. Today: Contrast enhancment, cortical processing
Tuning tuning curves So far: Receptive fields Representation of stimuli Population vectors Today: Contrast enhancment, cortical processing Firing frequency N 3 s max (N 1 ) = 40 o N4 N 1 N N 5 2 s max
More informationBlue-Yellow Signals Are Enhanced by Spatiotemporal Luminance Contrast in Macaque V1
J Neurophysiol 93: 2263 2278, 2005; doi:10.1152/jn.00743.2004. Blue-Yellow Signals Are Enhanced by Spatiotemporal Luminance Contrast in Macaque V1 Gregory D. Horwitz, 1,2 E. J. Chichilnisky, 2 and Thomas
More informationRESEARCH STATEMENT. Nora Youngs, University of Nebraska - Lincoln
RESEARCH STATEMENT Nora Youngs, University of Nebraska - Lincoln 1. Introduction Understanding how the brain encodes information is a major part of neuroscience research. In the field of neural coding,
More informationLGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis
J Neurophysiol 87: 2741 2752, 2002; 10.1152/jn.00474.2001. LGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis TODD W. TROYER, 1 ANTON E. KRUKOWSKI, 2 AND KENNETH D. MILLER
More information60 ms. 0 ms 10 ms 30 ms. 100 ms 150 ms 200 ms 350 ms. 400 ms 470 ms 570 ms 850 ms. 950 ms 1025 ms 1100 ms 1200 ms
POSITION AND VELOCITY ESTIMATION IN THE VISUAL CORTEX Bijoy K. Ghosh 1 Zoran Nenadic Department of Systems Science and Mathematics Campus Box 1040 Washington University Saint Louis, MO 63130 U.S.A. Abstract:
More informationDimension Reduction Techniques. Presented by Jie (Jerry) Yu
Dimension Reduction Techniques Presented by Jie (Jerry) Yu Outline Problem Modeling Review of PCA and MDS Isomap Local Linear Embedding (LLE) Charting Background Advances in data collection and storage
More information