Abstract

Traditionally, it has been assumed that the important information from a visual scene is encoded within the average firing rate of a retinal ganglion cell. Many modelling techniques thus focus solely on estimating a firing rate rather than a cells temporal response. It has been argued however that the latter is more important, as intricate details of the visual scene are stored within the temporal nature of the code. In this paper, we present a model that accurately describes the input/output response of a retinal ganglion cell in terms of its temporal coding. The approach borrows a concept of layout from popular implementations, such as the linearnonlinear Poisson method that produces an estimated spike rate prior to generating a spiking output. Using the wellknown Izhikevich neuron as the spike generator and various approaches for spike rate estimation, we show that the resulting overall system predicts a retinal ganglion cells response to novel stimuli in terms of bursting and periods of silence with reasonable accuracy.