Efficient Modeling and Calibration of Multi-Electrode Stimuli for Epiretinal Implants

Epiretinal implants are designed to restore visual function by direct stimulation of retinal ganglion cells (RGCs) with a multi-electrode array. However, the efficacy of presentday epiretinal implants is limited by indiscriminate, simultaneous activation of many RGCs of different types that normally convey distinct visual information, resulting in highly unnatural visual signals. Even with high-density multi-electrode arrays in a laboratory setting, single-electrode stimulation often cannot target an individual cell selectively. A possible solution is to use spatially patterned multi-electrode stimulation. Previous studies have demonstrated enhanced selectivity using such stimuli, but a general framework for characterizing the responses of RGCs to stimulation by multiple electrodes has yet to be realized. Here we propose a generalizable model for the response of a neuron to multi-electrode stimuli, and demonstrate that it can be used to systematically and efficiently improve the selectivity of epiretinal stimulation. The efficacy of the model and of multi-electrode stimuli is validated using three-electrode triplet stimulation. The model assumes that there are several possible activation sites on each cell where a spike can originate, and that the probability of activation at each site is determined by a weighted sum of the currents applied on the electrodes. The multi-site model provides excellent fits to experimental data as well as an interpretable mechanism of multi-electrode activation. Additionally, since the model has few parameters, it permits efficient closed-loop calibration. An optimal experimental design is presented and validated for quickly learning the model parameters and identifying multi-electrode stimuli that are more selective than can be achieved with single-electrode stimuli. The model and closed-loop algorithm could potentially be used to optimize the selectivity of other neural interfaces.