Efficient characterization of electrically evoked responses for neural interfaces

Nishal P Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan Litke, Liam Paninski, E.J. Chichilnisky

Abstract: Future neural interfaces will read and write population neural activity at cellular resolution for diverse applications. For example, an artificial retina may restore vision to the blind by electrically stimulating retinal ganglion cells. Such devices must tune their function, based on stimulating and recording, to match the function of the circuit. However, existing methods for characterizing the neural interface scale poorly with the number of electrodes, limiting their practical applicability. We test the idea that using prior information from previous experiments and closed-loop measurements may greatly increase the efficiency of characterizing the neural interface. Large-scale, high-density electrical recording and stimulation in primate retina were used as a lab prototype for an artificial retina. Three key calibration steps are optimized: spike sorting, response modeling, and adaptive stimulation. For spike sorting, exploiting the similarity of electrical artifact across electrodes and experiments substantially reduced the number of required measurements. For response modeling, a joint model that captures the inverse relationship between recorded spike amplitude and electrical stimulation threshold from previously recorded retinas resulted in greater consistency and efficiency. For adaptive stimulation, choosing which electrodes to stimulate next based on probability estimates from previous measurements also improved performance. Similar improvement in efficiency resulted from using either non-adaptive stimulation with a joint estimation model, or adaptive stimulation with a simpler independent model for each cell. Finally, image reconstruction revealed that these improvements may translate to improved performance of an artificial retina.

Code Link: https://github.com/Chichilnisky-Lab/shah-neurips-2019

CMT Num: 8189