Characterizing Retinal Ganglion Cell Responses to Electrical Stimulation Using Generalized Linear Models.

The ability to preferentially stimulate different retinal pathways is an important area of research for improving visual prosthetics. Recent work has shown that different classes of retinal ganglion cells (RGCs) have distinct linear electrical input filters for low-amplitude white noise stimulation. The aim of this study is to provide a statistical framework for characterizing how RGCs respond to white-noise electrical stimulation. We used a nested family of Generalized Linear Models (GLMs) to partition neural responses into different components – progressively adding covariates to the GLM which captured non-stationarity in neural activity, a linear dependence on the stimulus, and any remaining nonlinear interactions. We found that each of these components resulted in increased model performance, but that even the nonlinear model left a substantial fraction of neural variability unexplained. The broad goal of this paper is to provide a much needed theoretical framework to objectively quantify stimulus paradigms in terms of the types of neural responses that they elicit (linear vs nonlinear vs stimulus independent variability). In turn, this aids the prosthetic community in the search for optimal stimulus parameters that avoid indiscriminate retinal activation and adaptation caused by excessively large stimulus pulses, and avoid low fidelity responses (low signal-to-noise ratio) caused by excessively weak stimulus pulses.