Learning a neural response metric for retinal prosthesis

Nishal P. Shah, Sasidhar Madugula, E. J. Chichilnisky, Yoram Singer, Jonathon Shlens

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations


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 typical patterns of neural activity in the retina. Therefore, an electrical stimulus must be selected so as to produce a neural response as close as possible to the desired response. This requires a technique for computing the distance between a desired response and an achievable response that is meaningful in terms of the visual signal being conveyed. We propose a method to learn 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 visual inputs. Using data from electrical stimulation experiments, we demonstrate that the learned metric could produce improvements in the performance of a retinal prosthesis.

Original languageEnglish (US)
StatePublished - 2018
Event6th International Conference on Learning Representations, ICLR 2018 - Vancouver, Canada
Duration: Apr 30 2018May 3 2018


Conference6th International Conference on Learning Representations, ICLR 2018

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Education
  • Computer Science Applications
  • Linguistics and Language


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