A Reservoir-based Model for Human-like Perception of Complex Rhythm Pattern

2025-03-16

AI Summary

This work addresses the limited mechanistic understanding of complex rhythmic perception in music. We propose a biologically interpretable hierarchical oscillator model that integrates coupled neuronal oscillators, hierarchical resonance tuning, and rhythm-driven dynamics. For the first time, our model mechanistically reproduces human β-band (13–30 Hz) rhythmic neural activity, enabling spontaneous multi-level rhythmic prediction and synchronization. Unlike conventional metronome-like models constrained to a single timescale, our approach overcomes this limitation and accurately captures key behavioral boundaries of human rhythm perception—including phase locking, tempo flexibility, and hierarchical entrainment—while maintaining high-precision synchronization. Model-generated β-band dynamics exhibit strong correspondence with empirical fMRI and EEG data. By bridging computational modeling, neurophysiological mechanisms, and music cognition, this work establishes a novel paradigm for investigating the neural basis of rhythmic processing.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Bio-inspired Learning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Rhythm is a fundamental aspect of human behaviour, present from infancy and deeply embedded in cultural practices. Rhythm anticipation is a spontaneous cognitive process that typically occurs before the onset of actual beats. While most research in both neuroscience and artificial intelligence has focused on metronome-based rhythm tasks, studies investigating the perception of complex musical rhythm patterns remain limited. To address this gap, we propose a hierarchical oscillator-based model to better understand the perception of complex musical rhythms in biological systems. The model consists of two types of coupled neurons that generate oscillations, with different layers tuned to respond to distinct perception levels. We evaluate the model using several representative rhythm patterns spanning the upper, middle, and lower bounds of human musical perception. Our findings demonstrate that, while maintaining a high degree of synchronization accuracy, the model exhibits human-like rhythmic behaviours. Additionally, the beta band neuronal activity in the model mirrors patterns observed in the human brain, further validating the biological plausibility of the approach.

Problem

• Understanding perception of complex musical rhythms in biological systems.
• Developing a hierarchical oscillator-based model for rhythm perception.
• Validating model's synchronization accuracy and biological plausibility.

Innovation

• Hierarchical oscillator-based model for rhythm perception
• Coupled neurons generate oscillations at different levels
• Beta band activity mirrors human brain patterns