Accounting for timing and variability of retinal ganglion cell light responses with a stochastic integrate-and-fire model

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

Published in Annual Meeting, Neuroscience, Nov 2004.
Models used to characterize visual neurons typically involve unrealistic assumptions that fail to describe important aspects of spiking statistics. We investigated the ability of a cascade model with stochastic integrate-and-fire (IF) spiking to account for the light responses of primate retinal ganglion cells (RGCs). In the model, an initial linear filtering of the stimulus (the temporal receptive field) drives a noisy, leaky IF spike generator. An after-current is injected into the integrator after each spike, enabling the model to capture a wide range of realistic spiking statistics. This model provides a reasonable approximation to the biophysics of spike generation, and can be reliably and efficiently fit to extracellular spike time data. Here we show that the model is capable of characterizing the stimulus-dependence, timing and intrinsic variability of RGC light responses.

Multi-electrode extracellular recordings from primate RGCs were obtained from isolated retinas stimulated with spatially uniform temporal white noise (flicker). The stochastic IF model was fit to recorded spike times using maximum likelihood, and was subsequently used to predict RGC responses to multiple repeats of a novel stimulus. The model provided a more accurate description of spike rate, count variability, and timing precision of RGC responses than a commonly-used Linear-Nonlinear-Poisson cascade model. Because the model approximates a biophysical description of RGC spike generation, it provides an intuition about the origins and stimulus dependence of spike timing variability: voltage noise in a leaky integrator driven across threshold.

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