A Reservoir-based Model for Human-like Perception of Complex Rhythm Pattern
This work addresses the problem of modeling complex rhythm perception for neuroscience and AI researchers, representing an incremental advance in biologically plausible computational models.
The researchers tackled the limited understanding of complex musical rhythm perception by proposing a hierarchical oscillator-based model with coupled neurons tuned to different perception levels. Their model demonstrated human-like rhythmic behaviors with high synchronization accuracy and showed beta band neuronal activity patterns matching those observed in the human brain.
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.