Stimulus-Dependent Correlations and Population Codes

The magnitude of correlations between stimulus-driven responses of pairs of neurons can itself be stimulus dependent. We examine how this dependence affects the information carried by neural populations about the stimuli that drive them. Stimulus-dependent changes in correlations can both carry information directly and modulate the information separately carried by the firing rates and variances. We use Fisher information to quantify these effects and show that, although stimulus-dependent correlations often carry little information directly, their modulatory effects on the overall information can be large. In particular, if the stimulus dependence is such that correlations increase with stimulus-induced firing rates, this can significantly enhance the information of the population when the structure of correlations is determined solely by the stimulus. However, in the presence of additional strong spatial decay of correlations, such stimulus dependence may have a negative impact. Opposite relationships hold when correlations decrease with firing rates.

[1]  M. A. Smith,et al.  Spatial and Temporal Scales of Neuronal Correlation in Primary Visual Cortex , 2008, The Journal of Neuroscience.

[2]  Carl D. Meyer,et al.  Matrix Analysis and Applied Linear Algebra , 2000 .

[3]  H. Cramér Mathematical methods of statistics , 1947 .

[4]  Jaime de la Rocha,et al.  Supplementary Information for the article ‘ Correlation between neural spike trains increases with firing rate ’ , 2007 .

[5]  David S. Greenberg,et al.  Population imaging of ongoing neuronal activity in the visual cortex of awake rats , 2008, Nature Neuroscience.

[6]  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.

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

[8]  Sailes K. Sengijpta Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

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

[10]  Steven Reece,et al.  An information theoretic approach to the contributions of the firing rates and the correlations between the firing of neurons. , 2003, Journal of neurophysiology.

[11]  A. P. Georgopoulos,et al.  Variability and Correlated Noise in the Discharge of Neurons in Motor and Parietal Areas of the Primate Cortex , 1998, The Journal of Neuroscience.

[12]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[13]  Eric Shea-Brown,et al.  Correlation and synchrony transfer in integrate-and-fire neurons: basic properties and consequences for coding. , 2008, Physical review letters.

[14]  David W Tank,et al.  Behavioral/systems/cognitive Correlated Discharge among Cell Pairs within the Oculomotor Horizontal Velocity-to-position Integrator Materials and Methods , 2022 .

[15]  A. Pouget,et al.  Neural correlations, population coding and computation , 2006, Nature Reviews Neuroscience.

[16]  R K Powers,et al.  Relationship between simulated common synaptic input and discharge synchrony in cat spinal motoneurons. , 2001, Journal of neurophysiology.

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

[18]  Valentin Dragoi,et al.  Adaptive coding of visual information in neural populations , 2008, Nature.

[19]  K. O. Johnson,et al.  Sensory discrimination: neural processes preceding discrimination decision. , 1980, Journal of neurophysiology.

[20]  R. Christopher deCharms,et al.  Primary cortical representation of sounds by the coordination of action-potential timing , 1996, Nature.

[21]  P. L. V. Kan,et al.  Response covariance in cat visual cortex , 2004, Experimental Brain Research.

[22]  J. Movshon,et al.  The analysis of visual motion: a comparison of neuronal and psychophysical performance , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[23]  Eric Shea-Brown,et al.  Time scales of spike-train correlation for neural oscillators with common drive. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[25]  A. B. Bonds,et al.  Cooperation between Area 17 Neuron Pairs Enhances Fine Discrimination of Orientation , 2003, The Journal of Neuroscience.

[26]  M. A. Smith,et al.  Stimulus Dependence of Neuronal Correlation in Primary Visual Cortex of the Macaque , 2005, The Journal of Neuroscience.

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

[28]  Jason M. Samonds,et al.  Cooperative synchronized assemblies and orientation discrimination , 2010 .

[29]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[30]  Robert Rosenbaum,et al.  Mechanisms That Modulate the Transfer of Spiking Correlations , 2011, Neural Computation.

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

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

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

[34]  Bruno B. Averbeck,et al.  Noise Correlations and Information Encoding and Decoding , 2009 .

[35]  J. Ko Sensory discrimination: neural processes preceding discrimination decision. , 1980 .

[36]  Adam Kohn,et al.  How do stimulus-dependent correlations between V1 neurons affect neural coding? , 2007, Neurocomputing.

[37]  Haim Sompolinsky,et al.  Correlation Codes in Neuronal Populations , 2001, NIPS.

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

[39]  Pieter R. Roelfsema,et al.  Noise Correlations Have Little Influence on the Coding of Selective Attention in Area V1 , 2008, Cerebral cortex.

[40]  T. Harkany,et al.  Pyramidal cell communication within local networks in layer 2/3 of rat neocortex , 2003, The Journal of physiology.

[41]  Haim Sompolinsky,et al.  Erratum: Population coding in neuronal systems with correlated noise [Phys. Rev. E 64, 051904 (2001)] , 2002 .

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

[43]  E. Seidemann,et al.  Optimal decoding of correlated neural population responses in the primate visual cortex , 2006, Nature Neuroscience.

[44]  Stephane Molotchnikoff,et al.  Synchrony between orientation-selective neurons is modulated during adaptation-induced plasticity in cat visual cortex , 2008, BMC Neuroscience.

[45]  W. Singer,et al.  Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.

[46]  D. W. Wheeler,et al.  Brightness Induction: Rate Enhancement and Neuronal Synchronization as Complementary Codes , 2006, Neuron.

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

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

[49]  C. R. Rao,et al.  Information and the Accuracy Attainable in the Estimation of Statistical Parameters , 1992 .

[50]  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.

[51]  Haim Sompolinsky,et al.  Implications of Neuronal Diversity on Population Coding , 2006, Neural Computation.

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

[53]  James Demmel,et al.  Applied Numerical Linear Algebra , 1997 .

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

[55]  D. Butts,et al.  Tuning Curves, Neuronal Variability, and Sensory Coding , 2006, PLoS biology.

[56]  Maurice J Chacron,et al.  Population coding by electrosensory neurons. , 2008, Journal of neurophysiology.