A machine learning framework that encodes images like a retina
A major challenge to developing better neural prostheses is sensory encoding: transforming information captured from the environment by sensors into neural signals that can be interpreted by the nervous system. But because the number of electrodes in a prosthesis is limited, this environmental input must be reduced in some way, while still preserving the quality of the data that is transmitted to the brain.
Demetri Psaltis (Optics Lab) and Christophe Moser (Laboratory of Applied Photonics Devices) collaborated with Diego Ghezzi of the Hôpital ophtalmique Jules-Gonin – Fondation Asile des Aveugles (previously Medtronic Chair in Neuroengineering at EPFL) to apply machine ...












