ReviewFrontiers in computational neuroscience2023
Dynamic models for musical rhythm perception and coordination.
Review in Frontiers in computational neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Modeling the rhythmic complexity of professional drumming with an oscillation-driven reservoir computer.Cognitive neurodynamics · 2026Article
- Cortico-striatal network architecture shapes rhythmic biases in music.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Duration, but not intensity cues, facilitate synchronization to complex rhythms.Attention, perception & psychophysics · 2026Article
- Music as a scientific metaphor for mind and brain.Neuroscience and biobehavioral reviews · 2026Review
- Universal and Cross-Cultural Variations in Audio-Motor Synchronization Between French and Indian Participants.Annals of the New York Academy of Sciences · 2026Article
- Timing-induced illusory percepts of pitch.Scientific reports · 2026Article
- The Emergence of a Universal Rhythmic Feature: Simple Models Can Produce Categorical Rhythms.Annals of the New York Academy of Sciences · 2026Article
- Novel Approach to Inter-Onset-Interval Ratio Uncovers Music-Like Rhythmic Patterns in Budgerigar (Melopsittacus undulatus) Warble Song.Annals of the New York Academy of Sciences · 2026Article
- The Influence of Dancesport on College Students' Rhythm Perception Ability.Brain sciences · 2026Article
- Rhythm Processing Across Development: Origins, Links to Language Processing, and Perspectives for Intervention.Annals of the New York Academy of Sciences · 2026Review
- Effects of delay and amplification of auditory feedback for walking: anticipation, variability, and frequency adaptation.bioRxiv : the preprint server for biology · 2026Article
- Temporal codes and recurrent timing nets for rhythmic expectancy.Frontiers in computational neuroscience · 2026Article
- Computational modeling of rhythmic expectations: Perspectives, pitfalls, and prospects.PLoS computational biology · 2025Review
- New insights into multistability and complex resonances driven by subthreshold periodic signals in a neuronal model.Cognitive neurodynamics · 2025Article
- Deep oscillatory neural network.Scientific reports · 2025Article
- Impact of Neural network-quantified musical groove on cyclists' joint coordination and muscle synergy: a repeated measures study.Journal of neuroengineering and rehabilitation · 2025Article
- Neural encoding of auditory rhythm beyond cortical auditory areas before the age of term.iScience · 2025Article
- Tempo-dependent selective enhancement of neural responses at the beat frequency can be mimicked by both an oscillator and an evoked model.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Revealing rhythm categorization in human brain activity.Science advances · 2025Article
- Pitch biases sensorimotor synchronization to auditory rhythms.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context of music, humans have the ability to perceive, generate, and anticipate rhythmic structures, for example, "the beat." Experimental and behavioral studies offer clues about the biophysical and neural mechanisms that underlie our rhythmic abilities, and about different brain areas that are involved but many open questions remain. In this paper, we review several theoretical and computational approaches, each centered at different levels of description, that address specific aspects of musical rhythmic generation, perception, attention, perception-action coordination, and learning. We survey methods and results from applications of dynamical systems theory, neuro-mechanistic modeling, and Bayesian inference. Some frameworks rely on synchronization of intrinsic brain rhythms that span the relevant frequency range; some formulations involve real-time adaptation schemes for error-correction to align the phase and frequency of a dedicated circuit; others involve learning and dynamically adjusting expectations to make rhythm tracking predictions. Each of the approaches, while initially designed to answer specific questions, offers the possibility of being integrated into a larger framework that provides insights into our ability to perceive and generate rhythmic patterns.
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