Prediction and decoding of retinal ganglion cell
responses with a probabilistic spiking model
J. Neuroscience. 25(47):11003-11013, November 23, 2005.
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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. Though 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
(non-repeating) 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.
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