Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces

• Type of Material: Article
• Authors: Chethan Pandarinath, Emory University; K. Cora Ames, Columbia University; Abigail A. Russo, Columbia University; Ali Farshchian, Northwestern University; Lee E. Miller, Northwestern University; Eva Dyer, Emory University; Jonathan C. Kao, University of California, Los Angeles
• Language: English
• Date: 2018-10-31
• Publisher: Lippincott, Williams & Wilkins
• Publication Version: Final Published Version
• Copyright Statement: © 2018 The Authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1523/JNEUROSCI.1669-18.2018
• Title of Journal or Parent Work: Journal of Neuroscience Nursing
• ISSN: 0888-0395
• Volume: 38
• Issue: 44
• Start Page: 9390
• End Page: 9401
• Grant/Funding Information: This work was supported by a Burroughs Wellcome Fund Collaborative Research Travel Grant (C.P.); and NIH NINDS R01NS053603 (L.E.M.).
• Keywords: neural population dynamics; Neurosciences & Neurology; Neurosciences; Life Sciences & Biomedicine; motor control; INTENDED MOVEMENT; brain-machine interfaces; MOVEMENT PREPARATION; motor cortex; PREPARATORY ACTIVITY; STATE-SPACE; ALPHA-MOTONEURONES; PERFORMANCE NEURAL PROSTHESIS; Science & Technology; CORTICAL ACTIVITY; POPULATION-DYNAMICS; ARM MOVEMENTS; machine learning; OPTIMAL FEEDBACK-CONTROL; dynamical systems
• Research Categories: Engineering, Biomedical; Health Sciences, Medicine and Surgery; Biology, Neuroscience

Abstract

In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain–machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.