Abstract

There are 20-50 functionally- and anatomically-distinct ganglion cell types in the mammalian retina; each type encodes a unique feature of the visual world and conveys it via action potentials to the brain. Individual ganglion cells receive input from unique presynaptic retinal circuits, and the characteristic patterns of light-evoked action potentials in each ganglion cell type therefore reflect computations encoded in synaptic input and in postsynaptic signal integration and spike generation. Unfortunately, there is a dearth of tools for characterizing retinal ganglion cell computation. Therefore, we developed a statistical model, the separable Nonlinear Input Model, capable of characterizing the large array of distinct computations reflected in retinal ganglion cell spiking. We recorded ganglion cell responses to a correlated noise (\"cloud\") stimulus designed to accentuate the important features of retinal processing in an in vitro preparation of mouse retina and found that this model accurately predicted ganglion cell responses at high spatiotemporal resolution. It identified multiple receptive fields (RFs) reflecting the main excitatory and suppressive components of the response of each neuron. Most significantly, our model succeeds where others fail, accurately identifying ON-OFF cells and segregating their distinct ON and OFF selectivity and demonstrating the presence of different types of suppressive receptive fields. In total, our computational approach offers rich description of ganglion cell computation and sets a foundation for relating retinal computation to retinal circuitry.