Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses.

ABSTRACT: Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capabilities. However, sensations elicited by current devices often appear artificial and distorted. Although current models can predict the neural or perceptual response to an electrical stimulus, an optimal stimulation strategy solves the inverse problem: what is the required stimulus to produce a desired response? Here, we frame this as an end-to-end optimization problem, where a deep neural network stimulus encoder is trained to invert a known and fixed forward model that approximates the underlying biological system. As a proof of concept, we demonstrate the effectiveness of this hybrid neural autoencoder (HNA) in visual neuroprostheses. We find that HNA produces high-fidel

SUBMITTER: Granley J 

PROVIDER: S-EPMC10504858 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature