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Neuron. 2017 Feb 22;93(4):955-970.e5. doi: 10.1016/j.neuron.2017.01.016. Epub 2017 Feb 9.

Emergence of Coordinated Neural Dynamics Underlies Neuroprosthetic Learning and Skillful Control.

Author information

1
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA; Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Avenida de Brasília, Doca de Pedrouços, Lisbon 1400-038, Portugal.
2
Neurology and Rehabilitation Services, San Francisco VA Medical Center, San Francisco, CA 94121, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA.
3
Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Avenida de Brasília, Doca de Pedrouços, Lisbon 1400-038, Portugal; Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10032, USA. Electronic address: rui.costa@neuro.fchampalimaud.org.
4
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA; UC Berkeley/UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA. Electronic address: jcarmena@berkeley.edu.

Abstract

During motor learning, movements and underlying neural activity initially exhibit large trial-to-trial variability that decreases over learning. However, it is unclear how task-relevant neural populations coordinate to explore and consolidate activity patterns. Exploration and consolidation could happen for each neuron independently, across the population jointly, or both. We disambiguated among these possibilities by investigating how subjects learned de novo to control a brain-machine interface using neurons from motor cortex. We decomposed population activity into the sum of private and shared signals, which produce uncorrelated and correlated neural variance, respectively, and examined how these signals' evolution causally shapes behavior. We found that initially large trial-to-trial movement and private neural variability reduce over learning. Concomitantly, task-relevant shared variance increases, consolidating a manifold containing consistent neural trajectories that generate refined control. These results suggest that motor cortex acquires skillful control by leveraging both independent and coordinated variance to explore and consolidate neural patterns.

KEYWORDS:

brain-machine interface; dimensionality reduction; motor learning; neural variability; neuroprosthetic learning

PMID:
28190641
DOI:
10.1016/j.neuron.2017.01.016
[Indexed for MEDLINE]
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