Vision restoration with retinal implants is limited by indiscriminate simultaneous activation of many cells and cell types, which is incompatible with reproducing the neural code of the retina. Recent work has shown that macaque retinal ganglion cells (RGCs), which transmit visual information to the brain, can be directly electrically activated with single-cell, single-spike, cell-type precision – however, this possibility has never been tested in the human retina. Here, the electrical activation properties of identified RGC types in the human retina were examined using large-scale, multi-electrode recording and stimulation ex vivo and were compared directly to results from macaque. Precise activation was often possible without activating overlying axon bundles, at low stimulation current levels similar to those observed in macaque. The major RGC types could be identified and targeted based on their distinctive electrical signatures. The measured electrical activation properties of RGCs, combined with a dynamic stimulation algorithm, was sufficient to produce a nearly optimal evoked visual signal. These results reveal the possibility of high-fidelity vision restoration using bi-directional retinal implants.

Electrical stimulation of retinal ganglion cells (RGCs), which transmit visual information to the brain, is used in retinal implants to treat blindness caused by photoreceptor degeneration. However, the performance of existing clinical implants is limited by indiscriminate stimulation of many cells and cell types. Recent work in isolated macaque retina has shown the ability to precisely evoke spikes in the major RGC types by direct electrical stimulation at safe current levels, with single-cell, single-spike resolution and avoidance of axon bundle activation in many cases. However, these findings have not been verified in the human retina. Here, electrical activation of the major human RGC types was examined using large-scale, multi-electrode recording and stimulation and compared to results from several macaque retinas obtained using the same methods. Electrical stimulation of the major human RGC types closely paralleled results in macaque, with similar somatic and axonal stimulation thresholds, cellular and cell type selectivity of stimulation, avoidance of axon bundle stimulation by calibration, targeting of different cell types based on their distinct electrical signatures, and potential efficacy of real-time stimulus optimization for artificial vision. The results indicate that the macaque retina provides a quantitatively accurate picture of how focal electrical stimulation can be used in future high-resolution implants.

Objective
Retinal implants are designed to stimulate retinal ganglion cells (RGCs) in a way that restores sight to individuals blinded by photoreceptor degeneration. Reproducing high-acuity vision with these devices will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without being able to measure them directly. Here we demonstrate an inference approach that exploits intrinsic electrophysiological features of primate RGCs.

Approach
First, ON-parasol and OFF-parasol RGC types were identified using their intrinsic electrical features in large-scale multi-electrode recordings from macaque retina. Then, the electrically inferred somatic location, inferred cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. The accuracy of the cell type classification and of reproducing measured light responses with the model were evaluated.

Main results
A cell-type classifier trained on 246 large-scale multi-electrode recordings from 148 retinas achieved 95% mean accuracy on 29 test retinas. In five retinas tested, the inferred models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses (an upper bound). Linear decoding of natural images from predicted RGC activity in one retina showed a mean correlation of 0.55 between decoded and true images, compared to an upper bound of 0.81 using models fitted to light response data. 

Significance
These results suggest that inference of RGC light response properties from intrinsic features of their electrical activity may be a useful approach for high-fidelity sight restoration. The overall strategy of first inferring cell type from electrical features and then exploiting cell type to help infer natural cell function may also prove broadly useful to neural interfaces.

Reproducing high-acuity vision with an epiretinal implant will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without measuring them directly. Here we demonstrate an approach that exploits intrinsic electrical features of primate RGCs. First, ON-parasol and OFF-parasol RGCs were identified with 95% accuracy using electrical features. Then, the somatic electrical footprint, predicted cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. Across five retinas, these models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses, an upper bound. This finding, and linear decoding of images from predicted RGC activity, suggested that the inference approach may be useful for high-fidelity sight restoration.