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The Journal of Neuroscience, November 23, 2005, 25(47):11003-11013; doi:10.1523/JNEUROSCI.3305-05.2005

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Behavioral/Systems/Cognitive
Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model

Jonathan W. Pillow,¹ Liam Paninski,² Valerie J. Uzzell,³ Eero P. Simoncelli,¹ and E. J. Chichilnisky³

¹Howard Hughes Medical Institute, Center for Neural Science, and Courant Institute of Mathematical Sciences, New York University, New York, New York 10003, ²Department of Statistics, Columbia University, New York, New York 10027, and ³The Salk Institute, La Jolla, California 92037

Sensory encoding in spiking neurons depends on both the integration of sensory inputs and the intrinsic dynamics and variability of spike generation. We show that the stimulus selectivity, reliability, and timing precision of primate retinal ganglion cell (RGC) light responses can be reproduced accurately with a simple model consisting of a leaky integrate-and-fire spike generator driven by a linearly filtered stimulus, a postspike current, and a Gaussian noise current. We fit model parameters for individual RGCs by maximizing the likelihood of observed spike responses to a stochastic visual stimulus. Although compact, the fitted model predicts the detailed time structure of responses to novel stimuli, accurately capturing the interaction between the spiking history and sensory stimulus selectivity. The model also accounts for the variability in responses to repeated stimuli, even when fit to data from a single (nonrepeating) stimulus sequence. Finally, the model can be used to derive an explicit, maximum-likelihood decoding rule for neural spike trains, thus providing a tool for assessing the limitations that spiking variability imposes on sensory performance.

Key words: retinal ganglion cell; spike trains; computational model; neural coding; spike timing; precision; decoding; variability; integrate and fire

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Received Feb 23, 2005; revised September 8, 2005; accepted September 19, 2005.

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