ArticleeLife2024
Precise control of neural activity using dynamically optimized electrical stimulation.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Article
- Neural population dynamics of direct electrical stimulation of neocortex.Cell reports · 2026Article
- A Dynamic Virtual Channel Approach to Enhance Retinal Prosthetic Precision.Biomimetics (Basel, Switzerland) · 2026Article
- Leveraging current steering and the biophysics of spike generation for cellular-resolution electrical stimulation of neurons.Cell reports · 2026Article
- Neural population dynamics of direct electrical stimulation of neocortex.bioRxiv : the preprint server for biology · 2025Article
- Deep Learning-Based Control of Electrically Evoked Activity in Human Visual Cortex.bioRxiv : the preprint server for biology · 2025Article
- Restoration of Sight with Electronic Retinal Prostheses.Annual review of vision science · 2025Review
- Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures.bioRxiv : the preprint server for biology · 2025Article
- Visual Prostheses in the Era of Artificial Intelligence Technology.Eye and brain · 2025Review
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Neural implants have the potential to restore lost sensory function by electrically evoking the complex naturalistic activity patterns of neural populations. However, it can be difficult to predict and control evoked neural responses to simultaneous multi-electrode stimulation due to nonlinearity of the responses. We present a solution to this problem and demonstrate its utility in the context of a bidirectional retinal implant for restoring vision. A dynamically optimized stimulation approach encodes incoming visual stimuli into a rapid, greedily chosen, temporally dithered and spatially multiplexed sequence of simple stimulation patterns. Stimuli are selected to optimize the reconstruction of the visual stimulus from the evoked responses. Temporal dithering exploits the slow time scales of downstream neural processing, and spatial multiplexing exploits the independence of responses generated by distant electrodes. The approach was evaluated using an experimental laboratory prototype of a retinal implant: large-scale, high-resolution multi-electrode stimulation and recording of macaque and rat retinal ganglion cells ex vivo. The dynamically optimized stimulation approach substantially enhanced performance compared to existing approaches based on static mapping between visual stimulus intensity and current amplitude. The modular framework enabled parallel extensions to naturalistic viewing conditions, incorporation of perceptual similarity measures, and efficient implementation for an implantable device. A direct closed-loop test of the approach supported its potential use in vision restoration.
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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.