Identification of Direct and Network-Mediated Activation of Retinal Ganglion Cells from Visually Evoked Potentials Using Machine Learning

Objective. To preserve the encoding of visual information in prosthetic vision as close to natural as possible, subretinal photovoltaic implants, which replace the lost photoreceptors, strive to stimulate the second-order retinal neurons, the bipolar cells, while avoiding direct activation of the downstream retinal ganglion cells. To assess the range of such selective subretinal activation, we implanted the devices in rodent models of retinal degeneration and measured the stimulation thresholds based on the visually evoked potentials. After assessment of the bipolar cell-mediated thresholds, direct activation of retinal ganglion cells was measured following intraocular injection of synaptic blockers. Since these chemicals are toxic to the retina, this procedure can only be done once in each animal. Approach. We developed a machine-learning model that identifies the stimulation pathway directly from the recorded visually evoked potentials, eliminating the need for synaptic blockers. The model was trained on recordings from rats implanted with PRIMA subretinal arrays and evaluated on two additional implant architectures, a second rat species, and a different anesthesia protocol. Main Results. The classifier achieved a balanced accuracy of 92% in cross-validation on the training data. Generalization to all unseen experimental conditions yielded an average balanced accuracy of 91%. Integrated Gradients analysis showed that combined bipolar and ganglion cell responses were driven by the early P1 component, while bipolar cell responses relied on later waveform components, consistent with thalamocortical processing dynamics. Significance. The described computational alternative to pharmacological blockers should improve the experimental throughput, allow multiple recordings over the lifetime of the same animal, and might be applicable to optimization of the stimulation settings in patients.

Kiessling, L., Kochnev Goldstein, A., Ly, K., Palanker, D.

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