From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control.
10.1007/s12264-022-00832-x
- Author:
Tianwei WANG
1
;
Yun CHEN
1
;
He CUI
2
Author Information
1. Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China.
2. Center for Excellence in Brain Science and Intelligent Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, 200031, China. cuihe@ion.ac.cn.
- Publication Type:Review
- Keywords:
Brain-machine interface;
Dimensionality reduction;
Machine learning;
Neural network;
Population decoding
- MeSH:
Biomechanical Phenomena;
Brain-Computer Interfaces;
Motor Cortex/physiology*;
Neurons/physiology*
- From:
Neuroscience Bulletin
2022;38(7):796-808
- CountryChina
- Language:English
-
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.
