Computational Models And Tools For Developing Sophisticated Stimulation Strategies For Retinal Neuroprostheses.

Improvements to the efficacy of retinal neuroprostheses can be achieved by developing more sophisticated neural stimulation strategies to enable selective or preferential activation of specific retinal ganglion cells (RGCs). Computational models are particularly well suited for these investigations. The electric field can be accurately described by mathematical formalisms, and the population-based neural responses to the electrical stimulation can be investigated at resolutions well beyond those achievable by current state-of-the-art biological techniques. In this study, we used a biophysically-and morphologically-detailed RGC model to explore the ability of high frequency electrical stimulation (HFS) to preferentially activate ON and OFF RGC subtypes, the two major information pathways of the retina. The performance of a wide range of electrical stimulation amplitudes (0 - $100~\mu \mathbf {A}$) and frequencies (1 - 10 kHz) on functionally-distinct RGC responses were evaluated. We found that ON RGCs could be preferentially activated at relatively higher stimulation amplitudes $( > 50 {\mu } \mathrm {A})$ and frequencies $( >2$ kHz) while OFF RGCs were activated by lower stimulation amplitudes (10 to $50 {\mu } \mathrm {A})$ across all tested frequencies. These stimuli also show great promise in eliciting RGC responses that parallel RGC encoding: one RGC type exhibited an increase in spiking activity during electrical stimulation whilst another exhibited decreased spiking activity, given the same stimulation parameters.