From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control
Tianwei Wang1,2,3 • Yun Chen1,2,3 • He Cui1,2,3
1 Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
2 Shanghai Center for Brain and Brain-inspired Intelligence Technology, Shanghai 200031, China 3 University of Chinese Academy of Sciences, Beijing 100049, China
Abstract
In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preference for kinetics and kinematics, a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution. In this review, we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view. Here, we aim to reconcile the above perspectives, and evaluate their theoretical impact, future direction, and potential applications in brain-machine interfaces.
Keywords
Dimensionality reduction; Neural network; Machine learning; Population decoding; Brain-machine interface
