Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses
This addresses the challenge of restoring high-quality vision for people with blindness, with potential applications in other neuroprosthetic technologies, though it appears incremental as it builds on existing forward models.
The paper tackles the problem of generating artificial sensations in sensory neuroprostheses by developing a Hybrid Neural Autoencoder (HNA) to solve the inverse problem of determining the required stimulus for a desired neural response, resulting in high-fidelity stimuli that outperform conventional methods across simulated patients.
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-fidelity patient-specific stimuli representing handwritten digits and segmented images of everyday objects, and significantly outperforms conventional encoding strategies across all simulated patients. Overall this is an important step towards the long-standing challenge of restoring high-quality vision to people living with incurable blindness and may prove a promising solution for a variety of neuroprosthetic technologies.