ArticleScience advances2023
Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Real-time closed-loop feedback system for mouse mesoscale cortical signal and movement control.eLife · 2026Article
- Chronically implantable μLED arrays for optogenetic cortical surface stimulation in mice.Nature communications · 2026Article
- Bidirectional optimization of firing rate in a mouse neuronal brain-machine interface.Biology letters · 2025Article
- Effective cerebellar neuroprosthetic control after stroke.Cell reports · 2025Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal spatiotemporal properties of the sensorimotor cortical integration that set constraints on the design of neuroprosthetics.
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