Predicting oculomotor behaviour from correlated populations of posterior parietal neurons

Oculomotor function critically depends on how signals representing saccade direction and eye position are combined across neurons in the lateral intraparietal area (LIP) of the posterior parietal cortex. Here we show that populations of parietal neurons exhibit correlated variability, and that using these interneuronal correlations yields oculomotor predictions that are more accurate and also less uncertain. The structure of LIP population responses is therefore essential for reliable read-out of oculomotor behaviour.

[1]  R. M. Siegel,et al.  Encoding of spatial location by posterior parietal neurons. , 1985, Science.

[2]  L. Fogassi,et al.  Eye position effects on visual, memory, and saccade-related activity in areas LIP and 7a of macaque , 1990, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[3]  J. Movshon,et al.  A computational analysis of the relationship between neuronal and behavioral responses to visual motion , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[4]  T. Sanger,et al.  Probability density estimation for the interpretation of neural population codes. , 1996, Journal of neurophysiology.

[5]  R. Andersen,et al.  Coding of intention in the posterior parietal cortex , 1997, Nature.

[6]  B L McNaughton,et al.  Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. , 1998, Journal of neurophysiology.

[7]  Jens O. Riis,et al.  Looking into the Future , 1998, Games in Operations Management.

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

[9]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[10]  E. Jaynes Probability theory : the logic of science , 2003 .

[11]  R. Zemel,et al.  Inference and computation with population codes. , 2003, Annual review of neuroscience.

[12]  G. Davies,et al.  Knowns and Unknowns , 2003 .

[13]  R. M. Siegel,et al.  Neurons of area 7 activated by both visual stimuli and oculomotor behavior , 2004, Experimental Brain Research.

[14]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

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

[16]  Anthony J. Movshon,et al.  Optimal representation of sensory information by neural populations , 2006, Nature Neuroscience.

[17]  Wei Ji Ma,et al.  Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.

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

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

[20]  Arnulf B. A. Graf,et al.  Decoding the activity of neuronal populations in macaque primary visual cortex , 2011, Nature Neuroscience.

[21]  M. Cohen,et al.  Measuring and interpreting neuronal correlations , 2011, Nature Neuroscience.

[22]  Jocelyn Evans,et al.  Knowns and Unknowns , 2013 .

[23]  A. Pouget,et al.  Probabilistic brains: knowns and unknowns , 2013, Nature Neuroscience.

[24]  Arnulf B. A. Graf From neuronal populations to behavior: a computational journey , 2014, Front. Comput. Neurosci..

[25]  Arnulf B. A. Graf,et al.  Inferring eye position from populations of lateral intraparietal neurons , 2014, eLife.

[26]  T. Neumann Probability Theory The Logic Of Science , 2016 .