ArticleAdvances in neural information processing systems2022
Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses.
Article in Advances in neural information processing systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Article
- Fuzzing the brain: automated stress testing for the safety of ML-driven neurostimulation.Journal of neural engineering · 2026Article
- Deep Learning-Based Control of Electrically Evoked Activity in Human Visual Cortex.bioRxiv : the preprint server for biology · 2025Article
- Evaluating Deep Human-in-the-Loop Optimization for Retinal Implants Using Sighted Participants.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Article
- Aligning Visual Prosthetic Development With Implantee Needs.Translational vision science & technology · 2024Article
- Aligning visual prosthetic development with implantee needs.medRxiv : the preprint server for health sciences · 2024Article
- Predicting the Temporal Dynamics of Prosthetic Vision.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Axonal stimulation affects the linear summation of single-point perception in three Argus II users.Journal of neural engineering · 2024Article
- Article
- An actor-model framework for visual sensory encoding.Nature communications · 2024Article
- Neuromorphic hardware for somatosensory neuroprostheses.Nature communications · 2024Review
- Axonal stimulation affects the linear summation of single-point perception in three Argus II users.medRxiv : the preprint server for health sciences · 2023Article
- Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses.Advances in neural information processing systems · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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-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.
Identifiers
37719469PMC10504858What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.