Accounting for timing and variability of retinal
ganglion cell light responses with a stochastic integrate-and-fire model
Talk (abstract #598.4) at
Society for Neuroscience 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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