Literature DB >> 23542219

Population codes in the visual cortex.

Seiji Tanabe1.   

Abstract

Every sensory event elicits activity in a broad population of cells that is distributed within and across cortical areas. How these neurons function together to represent the sensory environment is a major question in systems neuroscience. A number of proposals have been made, and recent advances in multi-neuronal recording have begun to allow researchers to test the predictions of these population-coding theories. In this review, I provide an introduction to some of the key concepts in population coding and describe several studies in the recent literature. The focus of this review is on sensory representation in the visual cortex and related perceptual decisions. The frameworks used to study population coding include population vectors, linear decoders, and Bayesian inference. Simple examples are provided to illustrate these concepts. Testing theories of population coding is an emerging subject in systems neuroscience, but advances in multi-neuronal recording and analysis suggest that an understanding is within reach.
Copyright © 2013 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

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Year:  2013        PMID: 23542219      PMCID: PMC3688279          DOI: 10.1016/j.neures.2013.03.010

Source DB:  PubMed          Journal:  Neurosci Res        ISSN: 0168-0102            Impact factor:   3.304


  36 in total

1.  Optimal representation of sensory information by neural populations.

Authors:  Mehrdad Jazayeri; J Anthony Movshon
Journal:  Nat Neurosci       Date:  2006-04-16       Impact factor: 24.884

2.  Weak pairwise correlations imply strongly correlated network states in a neural population.

Authors:  Elad Schneidman; Michael J Berry; Ronen Segev; William Bialek
Journal:  Nature       Date:  2006-04-09       Impact factor: 49.962

3.  The structure of multi-neuron firing patterns in primate retina.

Authors:  Jonathon Shlens; Greg D Field; Jeffrey L Gauthier; Matthew I Grivich; Dumitru Petrusca; Alexander Sher; Alan M Litke; E J Chichilnisky
Journal:  J Neurosci       Date:  2006-08-09       Impact factor: 6.167

4.  Bayesian inference with probabilistic population codes.

Authors:  Wei Ji Ma; Jeffrey M Beck; Peter E Latham; Alexandre Pouget
Journal:  Nat Neurosci       Date:  2006-10-22       Impact factor: 24.884

5.  Cortical preparatory activity: representation of movement or first cog in a dynamical machine?

Authors:  Mark M Churchland; John P Cunningham; Matthew T Kaufman; Stephen I Ryu; Krishna V Shenoy
Journal:  Neuron       Date:  2010-11-04       Impact factor: 17.173

6.  The effect of correlated variability on the accuracy of a population code.

Authors:  L F Abbott; P Dayan
Journal:  Neural Comput       Date:  1999-01-01       Impact factor: 2.026

7.  Vector reconstruction from firing rates.

Authors:  E Salinas; L F Abbott
Journal:  J Comput Neurosci       Date:  1994-06       Impact factor: 1.621

8.  Adjacent visual cortical complex cells share about 20% of their stimulus-related information.

Authors:  T J Gawne; T W Kjaer; J A Hertz; B J Richmond
Journal:  Cereb Cortex       Date:  1996 May-Jun       Impact factor: 5.357

Review 9.  Measuring and interpreting neuronal correlations.

Authors:  Marlene R Cohen; Adam Kohn
Journal:  Nat Neurosci       Date:  2011-06-27       Impact factor: 24.884

10.  A theory for the use of visual orientation information which exploits the columnar structure of striate cortex.

Authors:  M A Paradiso
Journal:  Biol Cybern       Date:  1988       Impact factor: 2.086

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  2 in total

1.  Visual illusion susceptibility in autism: A neural model.

Authors:  Sangwook Park; Basilis Zikopoulos; Arash Yazdanbakhsh
Journal:  Eur J Neurosci       Date:  2022-06-22       Impact factor: 3.698

2.  In Vivo Imaging Reveals Composite Coding for Diagonal Motion in the Drosophila Visual System.

Authors:  Yuanlei Yue; Shanshan Ke; Wei Zhou; Jin Chang
Journal:  PLoS One       Date:  2016-10-03       Impact factor: 3.240

  2 in total

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