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Neural noise sets a limit to information transmission in sensory systems. Inseveral areas, the spiking response (to a repeated stimulus) has shown a higherdegree of regularity than predicted by a Poisson process. However, a simplemodel to explain this low variability is still lacking. Here we introduce a newmodel, with a correction to Poisson statistics, which can accurately predictthe regularity of neural spike trains in response to a repeated stimulus. Themodel has only two parameters, but can reproduce the observed variability inretinal recordings in various conditions. We show analytically why thisapproximation can work. In a model of the spike emitting process where arefractory period is assumed, we derive that our simple correction can wellapproximate the spike train statistics over a broad range of firing rates. Ourmodel can be easily plugged to stimulus processing models, likeLinear-nonlinear model or its generalizations, to replace the Poisson spiketrain hypothesis that is commonly assumed. It estimates the amount ofinformation transmitted much more accurately than Poisson models in retinalrecordings. Thanks to its simplicity this model has the potential to explainlow variability in other areas.

A simple model for low variability in neural spike trains