Neural correlations, population coding and computation

How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.

[1]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[2]  F. Attneave Some informational aspects of visual perception. , 1954, Psychological review.

[3]  O. L. Zangwill,et al.  Current problems in animal behaviour , 1962 .

[4]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[5]  P. Milner A model for visual shape recognition. , 1974, Psychological review.

[6]  D. Sparks,et al.  Size and distribution of movement fields in the monkey superior colliculus , 1976, Brain Research.

[7]  K O Johnson,et al.  Sensory discrimination: decision process. , 1980, Journal of neurophysiology.

[8]  Masanao Toda,et al.  Decision Process , 2019, CIRP Encyclopedia of Production Engineering.

[9]  S. Laughlin,et al.  Predictive coding: a fresh view of inhibition in the retina , 1982, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[10]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[11]  Philip Bard,et al.  Cognitive spatial-motor processes 2. Information transmitted by the direction of two-dimensional arm movements and by neuronal populations in primate motor cortex and area 5 , 1988 .

[12]  D. Sparks,et al.  Population coding of saccadic eye movements by neurons in the superior colliculus , 1988, Nature.

[13]  Joseph J. Atick,et al.  Towards a Theory of Early Visual Processing , 1990, Neural Computation.

[14]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[15]  Prof. Dr. Valentino Braitenberg,et al.  Anatomy of the Cortex , 1991, Studies of Brain Function.

[16]  P König,et al.  Direct physiological evidence for scene segmentation by temporal coding. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[17]  TJ Gawne,et al.  How independent are the messages carried by adjacent inferior temporal cortical neurons? , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[18]  H Sompolinsky,et al.  Simple models for reading neuronal population codes. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[19]  C. Gross,et al.  Neural ensemble coding in inferior temporal cortex. , 1994, Journal of neurophysiology.

[20]  J. Nadal,et al.  Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer Network 5 , 1994 .

[21]  Christoph von der Malsburg,et al.  The Correlation Theory of Brain Function , 1994 .

[22]  Ehud Zohary,et al.  Correlated neuronal discharge rate and its implications for psychophysical performance , 1994, Nature.

[23]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[24]  Stefano Panzeri,et al.  The Upward Bias in Measures of Information Derived from Limited Data Samples , 1995, Neural Computation.

[25]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[26]  A. Aertsen,et al.  Dynamics of neuronal interactions in monkey cortex in relation to behavioural events , 1995, Nature.

[27]  A. Treisman The binding problem , 1996, Current Opinion in Neurobiology.

[28]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[29]  J. Hertz,et al.  Adjacent visual cortical complex cells share about 20% of their stimulus-related information. , 1996, Cerebral cortex.

[30]  William Bialek,et al.  Entropy and Information in Neural Spike Trains , 1996, cond-mat/9603127.

[31]  A. Treves,et al.  The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex , 1997, Experimental Brain Research.

[32]  D. Perrett,et al.  The `Ideal Homunculus': decoding neural population signals , 1998, Trends in Neurosciences.

[33]  Y. Dan,et al.  Coding of visual information by precisely correlated spikes in the lateral geniculate nucleus , 1998, Nature Neuroscience.

[34]  Peter Dayan,et al.  The Effect of Correlated Variability on the Accuracy of a Population Code , 1999, Neural Computation.

[35]  Carrie J. McAdams,et al.  Effects of Attention on the Reliability of Individual Neurons in Monkey Visual Cortex , 1999, Neuron.

[36]  E T Rolls,et al.  Correlations and the encoding of information in the nervous system , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[37]  C. Koch,et al.  Attention activates winner-take-all competition among visual filters , 1999, Nature Neuroscience.

[38]  A. Pouget,et al.  Reading population codes: a neural implementation of ideal observers , 1999, Nature Neuroscience.

[39]  R. Zemel,et al.  Information processing with population codes , 2000, Nature Reviews Neuroscience.

[40]  Si Wu,et al.  Population Coding with Correlation and an Unfaithful Model , 2001, Neural Computation.

[41]  Stefano Panzeri,et al.  Objective assessment of the functional role of spike train correlations using information measures , 2001 .

[42]  G. Orban,et al.  Practising orientation identification improves orientation coding in V1 neurons , 2001, Nature.

[43]  B J Richmond,et al.  Excess synchrony in motor cortical neurons provides redundant direction information with that from coarse temporal measures. , 2001, Journal of neurophysiology.

[44]  H Barlow,et al.  Redundancy reduction revisited , 2001, Network.

[45]  P. Latham,et al.  Retinal ganglion cells act largely as independent encoders , 2001, Nature.

[46]  Aapo Hyvärinen,et al.  A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images , 2001, Vision Research.

[47]  H. Sompolinsky,et al.  Population coding in neuronal systems with correlated noise. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  J. Gold,et al.  Neural computations that underlie decisions about sensory stimuli , 2001, Trends in Cognitive Sciences.

[49]  M. Diamond,et al.  Population Coding of Stimulus Location in Rat Somatosensory Cortex , 2001, Neuron.

[50]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[51]  Claude E. Shannon,et al.  A mathematical theory of communication , 1948, MOCO.

[52]  John H. R. Maunsell,et al.  Physiological correlates of perceptual learning in monkey V1 and V2. , 2002, Journal of neurophysiology.

[53]  Christian W. Eurich,et al.  Representational Accuracy of Stochastic Neural Populations , 2002, Neural Computation.

[54]  Michael J. Berry,et al.  Synergy, Redundancy, and Independence in Population Codes , 2003, The Journal of Neuroscience.

[55]  Daeyeol Lee,et al.  Neural Noise and Movement-Related Codes in the Macaque Supplementary Motor Area , 2003, The Journal of Neuroscience.

[56]  Sheila Nirenberg,et al.  Decoding neuronal spike trains: How important are correlations? , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[57]  S. Panzeri,et al.  An exact method to quantify the information transmitted by different mechanisms of correlational coding. , 2003, Network.

[58]  M. Schnitzer,et al.  Multineuronal Firing Patterns in the Signal from Eye to Brain , 2003, Neuron.

[59]  M. Young,et al.  Correlations, feature‐binding and population coding in primary visual cortex , 2003, Neuroreport.

[60]  A. Georgopoulos,et al.  Neural activity in prefrontal cortex during copying geometrical shapes , 2003, Experimental brain research.

[61]  Apostolos P. Georgopoulos,et al.  Neural activity in prefrontal cortex during copying geometrical shapes , 2003, Experimental Brain Research.

[62]  R. Romo,et al.  Correlated Neuronal Discharges that Increase Coding Efficiency during Perceptual Discrimination , 2003, Neuron.

[63]  M. Paradiso,et al.  A theory for the use of visual orientation information which exploits the columnar structure of striate cortex , 2004, Biological Cybernetics.

[64]  Haim Sompolinsky,et al.  Nonlinear Population Codes , 2004, Neural Computation.

[65]  Emilio Salinas,et al.  Vector reconstruction from firing rates , 1994, Journal of Computational Neuroscience.

[66]  A. Pouget,et al.  Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations , 2004, Nature Neuroscience.

[67]  J. T. Massey,et al.  Cognitive spatial-motor processes , 2004, Experimental Brain Research.

[68]  Daeyeol Lee,et al.  Coding and transmission of information by neural ensembles , 2004, Trends in Neurosciences.

[69]  J. Maunsell,et al.  The Effect of Perceptual Learning on Neuronal Responses in Monkey Visual Area V4 , 2004, The Journal of Neuroscience.

[70]  A. P. Georgopoulos,et al.  Cognitive spatial-motor processes , 2004, Experimental Brain Research.

[71]  Michael J. Berry,et al.  Redundancy in the Population Code of the Retina , 2005, Neuron.

[72]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[73]  M. Laubach,et al.  Redundancy and Synergy of Neuronal Ensembles in Motor Cortex , 2005, The Journal of Neuroscience.

[74]  P. Latham,et al.  Synergy, Redundancy, and Independence in Population Codes, Revisited , 2005, The Journal of Neuroscience.

[75]  Daeyeol Lee,et al.  Effects of noise correlations on information encoding and decoding. , 2006, Journal of neurophysiology.