Self-organizing neural network for reproducing human postural mode alternation through deep reinforcement learning.

ABSTRACT: A self-organized phenomenon in postural coordination is essential for understanding the auto-switching mechanism of in-phase and anti-phase postural coordination modes during standing and related supra-postural activities. Previously, a model-based approach was proposed to reproduce such self-organized phenomenon. However, if we set this problem including the process of how we establish the internal predictive model in our central nervous system, the learning process is critical to be considered for establishing a neural network for managing adaptive postural control. Particularly when body characteristics may change due to growth or aging or are initially unknown for infants, a learning capability can improve the hyper-adaptivity of human motor control for maintaining postural stability a

SUBMITTER: Shen K 

PROVIDER: S-EPMC10238493 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature