A learning-based approach to artificial sensory feedback leads to optimal integration
Open accessAbstract
Proprioception-the sense of the body's position in space-is important to natural movement planning and execution and will likewise be necessary for successful motor prostheses and brain-machine interfaces (BMIs). Here we demonstrate that monkeys were able to learn to use an initially unfamiliar multichannel intracortical microstimulation signal, which provided continuous information about hand position relative to an unseen target, to complete accurate reaches. Furthermore, monkeys combined this artificial signal with vision to form an optimal, minimum-variance estimate of relative hand position. These results demonstrate that a learning-based approach can be used to provide a rich artificial sensory feedback signal, suggesting a new strategy for restoring proprioception to patients using BMIs, as well as a powerful new tool for studying the adaptive mechanisms of sensory integration.
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In contrast, ICMS feedback in a relevant area of cortex, but in an unintuitive way, requires considerable learning time (thousands of trials) to use effectively.
“In contrast, ICMS feedback in a relevant area of cortex, but in an unintuitive way, requires considerable learning time (thousands of trials) to use effectively (33).”
From A brain-computer interface that evokes tactile sensations improves robotic arm control (2021) · cites this paper as [33]
Brain machine interfaces can be used to give the brain access to new datastreams, and to investigate how those datastreams are incorporated into task performance, and whether such incorporation is governed by optimality principles.
“Likewise, brain machine interfaces can be used to give the brain access to new datastreams, and to investigate how those datastreams are incorporated into task performance, and whether such incorporation is governed by optimality principles (Dadarlat et al., 2015).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Dadarlat et al., 2015
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References 50 · Cited by 210
- Authors
- Dadarlat, M. C., O'Doherty, J. E., & Sabes, P. N.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Sabes, Philip N
- Venue
- Nature Neuroscience
- Published
- 2014-11-24
- Volume
- 18
- Issue
- 1
- Pages
- 138-144
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC4282864
- OpenAlex
- W1973816804
- Cited by
- 210
- References
- 50
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 13 · 2023: 14 · 2022: 19 · 2021: 25 · 2020: 20 · 2019: 21 · 2018: 17 · 2017: 18 · 2016: 27 · 2015: 12
- FWCI
- 10.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Eye Institute (EY015679, R01EY015679, EY007120, T32EY007120)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- intracortical microstimulation, somatosensory cortex, brain-computer interface, sensory integration, multisensory integration, visual-proprioceptive integration, hand position estimation, reaching movements
- MeSH terms
- Animals, Behavior, Animal, Conditioning, Operant, Learning, Macaca mulatta, Male, Photic Stimulation, Psychomotor Performance, Sensation, Somatosensory Cortex, Visual Perception, Feedback, Psychological, Brain-Computer Interfaces
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