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The Journal of Neuroscience, December 10, 2008, 28(50):13522-13531; doi:10.1523/JNEUROSCI.4390-08.2008

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Behavioral/Systems/Cognitive
The Neural Basis for Combinatorial Coding in a Cortical Population Response

Leslie C. Osborne,^1 * Stephanie E. Palmer,^3 * Stephen G. Lisberger,^1,2 and William Bialek³

¹Sloan-Swartz Center for Theoretical Neurobiology, W. M. Keck Foundation Center for Integrative Neuroscience, and Department of Physiology, and ²Howard Hughes Medical Institute, University of California at San Francisco, San Francisco, California 94143, and ³Joseph Henry Laboratories of Physics and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544

Correspondence should be addressed to Stephanie E. Palmer, Department of Physics, Princeton University, Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Room 238, Princeton, NJ 08544. Email: sepalmer{at}princeton.edu

We have used a combination of theory and experiment to assess how information is represented in a realistic cortical population response, examining how motion direction and timing is encoded in groups of neurons in cortical area MT. Combining data from several single-unit experiments, we constructed model population responses in small time windows and represented the response in each window as a binary vector of 1s or 0s signifying spikes or no spikes from each cell. We found that patterns of spikes and silence across a population of nominally redundant neurons can carry up to twice as much information about visual motion than does population spike count, even when the neurons respond independently to their sensory inputs. This extra information arises by virtue of the broad diversity of firing rate dynamics found in even very similarly tuned groups of MT neurons. Additionally, specific patterns of spiking and silence can carry more information than the sum of their parts (synergy), opening up the possibility for combinatorial coding in cortex. These results also held for populations in which we imposed levels of nonindependence (correlation) comparable to those found in cortical recordings. Our findings suggest that combinatorial codes are advantageous for representing stimulus information on short time scales, even when neurons have no complicated, stimulus-dependent correlation structure.

Key words: visual motion; MT; spike trains; information theory; smooth pursuit; correlated variability

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Received Sept. 14, 2008; revised Oct. 23, 2008; accepted Oct. 27, 2008.

Correspondence should be addressed to Stephanie E. Palmer, Department of Physics, Princeton University, Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Room 238, Princeton, NJ 08544. Email: sepalmer{at}princeton.edu

This article has been cited by other articles:

				L. C. Osborne and S. G. Lisberger Spatial and Temporal Integration of Visual Motion Signals for Smooth Pursuit Eye Movements in Monkeys J Neurophysiol, October 1, 2009; 102(4): 2013 - 2025. [Abstract] [Full Text] [PDF]

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