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Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex

Sakellaridi, Sofia and Christopoulos, Vassilios N. and Aflalo, Tyson and Pejsa, Kelsie W. and Rosario, Emily R. and Ouellette, Debra and Pouratian, Nader and Andersen, Richard A. (2019) Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex. Neuron, 102 (3). pp. 694-705. ISSN 0896-6273. PMCID PMC6922088. doi:10.1016/j.neuron.2019.02.012.

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Although animal studies provided significant insights in understanding the neural basis of learning and adaptation, they often cannot dissociate between different learning mechanisms due to the lack of verbal communication. To overcome this limitation, we examined the mechanisms of learning and its limits in a human intracortical brain-machine interface (BMI) paradigm. A tetraplegic participant controlled a 2D computer cursor by modulating single-neuron activity in the anterior intraparietal area (AIP). By perturbing the neuron-to-movement mapping, the participant learned to modulate the activity of the recorded neurons to solve the perturbations by adopting a target re-aiming strategy. However, when no cognitive strategies were adequate to produce correct responses, AIP failed to adapt to perturbations. These findings suggest that learning is constrained by the pre-existing neuronal structure, although it is possible that AIP needs more training time to learn to generate novel activity patterns when cognitive re-adaptation fails to solve the perturbations.

Item Type:Article
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URLURL TypeDescription CentralArticle
Andersen, Richard A.0000-0002-7947-0472
Additional Information:© 2019 Elsevier. Received 17 May 2018, Revised 5 November 2018, Accepted 6 February 2019, Available online 7 March 2019. Data and Software Availability: Data and MATLAB analysis scripts available upon request from Sofia Sakellaridi ( Additional Resources: This study was conducted as part of NIH clinical trial NCT01958086. This work was supported by the National Institute of Health (5R01EY01554512), the Tianqiao and Chrissy Chen Brain-Machine Interface Center at Caltech, the Boswell Foundation, and the Swartz Foundation. The authors would also like to thank subject N.S. for participating in the studies and Viktor Scherbatyuk for technical assistance. Author Contributions: S.S., V.N.C., T.A., and R.A.A. designed the study. S.S., V.N.C., and T.A. developed the experimental tasks. S.S. and V.N.C. collected the data. S.S., V.N.C., and T.A. analyzed the results. S.S., V.N.C., T.A., and R.A.A. interpreted results and wrote the paper. E.R.R. provided experimental facilities, administrative assistance, and coordination with Casa Colina Hospital and Centers for Healthcare. K.W.P. provided administrative assistance. D.O. provided onsite assistance during experimental sessions. N.P. performed the surgery implanting the recording arrays in subject N.S. The authors declare no competing interests.
Group:Tianqiao and Chrissy Chen Institute for Neuroscience
Funding AgencyGrant Number
Tianqiao and Chrissy Chen Institute for NeuroscienceUNSPECIFIED
James G. Boswell FoundationUNSPECIFIED
Swartz FoundationUNSPECIFIED
Subject Keywords:brain-machine interface; intrinsic-variable learning; individual-neuron learning; anterior intraparietal cortex; posterior parietal cortex; spinal cord injury
Issue or Number:3
PubMed Central ID:PMC6922088
Record Number:CaltechAUTHORS:20190307-092211759
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Official Citation:Sofia Sakellaridi, Vassilios N. Christopoulos, Tyson Aflalo, Kelsie W. Pejsa, Emily R. Rosario, Debra Ouellette, Nader Pouratian, Richard A. Andersen, Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex, Neuron, Volume 102, Issue 3, 2019, Pages 694-705.e3, ISSN 0896-6273,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93612
Deposited By: George Porter
Deposited On:08 Mar 2019 15:35
Last Modified:15 Feb 2022 22:50

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