Prediction and decoding of retinal responses with a probabilistic spiking model

J W Pillow, L Paninski, V J Uzell, E P Simoncelli and E J Chichilnisky

Published in J. Neuroscience, vol.25(47), pp. 11003--11013, Nov 2005.

DOI: 10.1523/JNEUROSCI.3305-05.2005

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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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  • Convexity of this recurrent, noisy, leaky integrate-and-fire model: Paninski04
  • A more general book chapter on characterizing neural responses: Simoncelli03c
  • Comparison of model fit to intracellular and extracellular data: Paninski04a
  • Initial publication as conference paper: Pillow03b
  • Description of biases in reverse-correlation (spike-triggered average) estimates of the LNP model: Pillow02a
  • Listing of all publications