Emergent coordination with a brain–machine interface: implications for the neural basis of motor learning | Zendy

Madhur Mangalam | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Emergent coordination with a brain–machine interface: implications for the neural basis of motor learning

Author(s) -

Madhur Mangalam

Publication year - 2018

Publication title -

journal of neurophysiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.302

H-Index - 245

eISSN - 1522-1598

pISSN - 0022-3077

DOI - 10.1152/jn.00361.2018

Subject(s) - motor learning , motor coordination , grasp , psychology , neuroscience , motor skill , brain–computer interface , neural system , covariance , cognitive science , artificial neural network , neural activity , interface (matter) , cognitive psychology , computer science , artificial intelligence , mathematics , electroencephalography , statistics , programming language , bubble , maximum bubble pressure method , parallel computing

How patterns of covariance in motor output and neural activity emerge over the course of learning is a topic of ongoing investigation. Vaidya et al. (Vaidya M, Balasubramanian K, Southerland J, Badreldin I, Eleryan A, Shattuck K, Gururangan S, Slutzky M, Osborne L, Fagg A, Oweiss K, Hatsopoulos NG. J Neurophysiol 119: 1291–1304, 2018) investigate the emergence of patterns of covariance in the motor output and neural activity in chronically amputated macaques learning reach-to-grasp movements with a brain–machine interface. The authors’ findings have implications for uncovering general principles of how neural coordination unfolds while learning a different motor behavior.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research