A Dynamic Systems Approach to Modelling Human-Machine Rhythm Interaction

TL;DR

This paper presents a computational reservoir model inspired by biological neural processes that accurately simulates human rhythmic perception, synchronization, and adaptation, advancing understanding of rhythm cognition in humans and machines.

Contribution

It introduces a novel reservoir computing framework mimicking cerebellar functions, enabling dynamic rhythm interaction modeling with continuous learning capabilities.

Findings

Model accurately perceives and adapts to human rhythmic patterns

Incorporates fine-tuning and delay-feedback for precise rhythm prediction

Enhances understanding of neural mechanisms underlying rhythm synchronization

Abstract

In exploring the simulation of human rhythmic perception and synchronization capabilities, this study introduces a computational model inspired by the physical and biological processes underlying rhythm processing. Utilizing a reservoir computing framework that simulates the function of cerebellum, the model features a dual-neuron classification and incorporates parameters to modulate information transfer, reflecting biological neural network characteristics. Our findings demonstrate the model's ability to accurately perceive and adapt to rhythmic patterns within the human perceptible range, exhibiting behavior closely aligned with human rhythm interaction. By incorporating fine-tuning mechanisms and delay-feedback, the model enables continuous learning and precise rhythm prediction. The introduction of customized settings further enhances its capacity to stimulate diverse human rhythmic behaviors, underscoring the potential of this architecture in temporal cognitive task modeling and the study of rhythm synchronization and prediction in artificial and biological systems. Therefore, our model is capable of transparently modelling cognitive theories that elucidate the dynamic processes by which the brain generates rhythm-related behavior.