Retinal prostheses for treating incurable blindness are designed to electrically stimulate surviving retinal neurons,  causing them to send artificial visual signals to the brain. However, electrical stimulation generally cannot precisely reproduce  normal patterns of neural activity in the retina. Therefore, an electrical stimulus must be selected that produces a neural response as close as possible to the desired response. This requires a technique for computing a distance between the desired response and the achievable response that is meaningful in terms of the visual signal being conveyed. Here we propose a method to learn such a metric on neural responses, directly from recorded light responses of a population of retinal ganglion cells (RGCs) in the primate retina. The learned metric produces a measure of similarity of RGC population responses that accurately reflects the similarity of the visual input. Using data from electrical stimulation experiments, we demonstrate that this metric may improve the performance of a prosthesis.
Using triplets to learn a metric for comparing neural responses and improve the performance of a prosthesis. Authors develop new spike train distance metrics, including neural networks and quadratic metrics. These metrics are shown to outperform the naive Hamming distance metric, and implicitly captures some structure in neural code. With the application of improving neural prosthesis in mind, the authors propose to learn a metric between neural responses by either optimizing a quadratic form or a deep neural network .