Emergence of Coordinated Neural Dynamics Underlies Neuroprosthetic Learning and Skillful Control

• Published in: Neuron (Cambridge, Mass.) Vol. 93; no. 4; pp. 955 - 970.e5
• Main Authors: Athalye, Vivek R., Ganguly, Karunesh, Costa, Rui M., Carmena, Jose M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 22.02.2017; Elsevier Limited
• Subjects: Animals; Behavior; Brain-Computer Interfaces; brain-machine interface; dimensionality reduction; Learning - physiology; Models, Neurological; Motor Cortex - physiology; motor learning; Motor Skills - physiology; Movement - physiology; neural variability; Neuronal Plasticity - physiology; Neurons; Neurons - physiology; neuroprosthetic learning; Population; Scholarships & fellowships; Software; Trends; neuroprosthetic learning; dimensionality reduction; neural variability; motor learning; brain-machine interface
• ISSN: 0896-6273; 1097-4199; 1097-4199
• DOI: 10.1016/j.neuron.2017.01.016

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. •In early training, large uncorrelated neural variance produces variable cursor control•Trial-to-trial uncorrelated neural and cursor variability decrease with training•Task-relevant neural covariance increases and consolidates over training•Consistent neural trajectories with task-relevant covariance produce skilled control Athalye et al. ask how task-relevant neural populations coordinate to acquire activity patterns underlying novel skills. As subjects learn to control a brain-machine interface de novo, each neuron initially explores independently, and then coordinated activity patterns are consolidated, driving skillful performance.