Model of rhythmic ball bouncing using a visually controlled neural oscillator
Abstract
The present paper investigates the sensory-driven modulations of central pattern generator dynamics that can be expected to reproduce human behavior during rhythmic hybrid tasks. We propose a theoretical model of human sensorimotor behavior able to account for the observed data from the ball-bouncing task. The novel control architecture is composed of a Matsuoka neural oscillator coupled with the environment through visual sensory feedback. The architecture’s ability to reproduce human-like performance during the ball-bouncing task in the presence of perturbations is quantified by comparison of simulated and recorded trials. The results suggest that human visual control of the task is achieved online. The adaptive behavior is made possible by a parametric and state control of the limit cycle emerging from the interaction of the rhythmic pattern generator, the musculoskeletal system, and the environment.
Keywords
Ball bouncing
Behavioral modeling
Neural oscillators
Perception-action couplings
Visual control
perception
humans
male
female
adult
synchronization
visual perception
dynamics
task
visual control
movement
feedback
physiological
active control
coordination
tracking
models
neurological
central pattern generators
hand
sensorimotor cortex
behavioral modeling
ball bouncing
information-movement couplings
neural oscillators
discrete
passive stability
Domains
Neurons and Cognition [q-bio.NC]| Origin | Files produced by the author(s) |
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| Licence |
