Work

A Machine Learning Framework to Optimize Optic Nerve Electrical Stimulation for Vision Restoration

Year: 2021

Type: article

Abstract: Optic nerve electrical stimulation is a promising technique to restore vision in blind subjects. Machine learning methods can be used to select effective stimulation protocols, but they require a model of the stimulated system to generate enough training data. Here, we use a convolutional neural network (CNN) as a model of the ventral visual stream. A genetic algorithm drives the activation of the units in a layer of the CNN representing a cortical region toward a desired pattern, by refining the activation imposed at a layer representing the optic nerve. To simulate the pattern of activation elicited by the sites of an electrode array, a simple point-source model was introduced and its optimization process was investigated for static and dynamic scenes. Psychophysical data confirm that our stimulation evolution framework produces results compatible with natural vision. Machine learning approaches could become a very powerful tool to optimize and personalize neuroprosthetic systems.

Source: Patterns

DOI: 10.1016/j.patter.2021.100286

Authors: Simone Romeni, Davide Zoccolan, Silvestro Micera

Institutions: École Polytechnique Fédérale de Lausanne, Scuola Internazionale Superiore di Studi Avanzati, Scuola Superiore Sant'Anna

Language: English

Topic: Neuroscience and Neural Engineering

Subfield: Cellular and Molecular Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $8,900

Funders: Fondation Bertarelli, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung