Theoretical and empirical advances in understanding musical rhythm, beat and metre

Snyder, Joel S.; Gordon, Reyna L.; Hannon, Erin E.

doi:10.1038/s44159-024-00315-y

Cited by 2 publications

References 224 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Dynamical mechanisms of how an RNN keeps a beat, uncovered with a low-dimensional reduced model

Zemlianova,

Bose,

Rinzel

2024

Sci Rep

View full text Add to dashboard Cite

Despite music’s omnipresence, the specific neural mechanisms responsible for perceiving and anticipating temporal patterns in music are unknown. To study potential mechanisms for keeping time in rhythmic contexts, we train a biologically constrained RNN, with excitatory (E) and inhibitory (I) units, on seven different stimulus tempos (2–8 Hz) on a synchronization and continuation task, a standard experimental paradigm. Our trained RNN generates a network oscillator that uses an input current (context parameter) to control oscillation frequency and replicates key features of neural dynamics observed in neural recordings of monkeys performing the same task. We develop a reduced three-variable rate model of the RNN and analyze its dynamic properties. By treating our understanding of the mathematical structure for oscillations in the reduced model as predictive, we confirm that the dynamical mechanisms are found also in the RNN. Our neurally plausible reduced model reveals an E-I circuit with two distinct inhibitory sub-populations, of which one is tightly synchronized with the excitatory units. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-77849-x.

show abstract

Dynamical mechanisms of how an RNN keeps a beat, uncovered with a low-dimensional reduced model

Zemlianova,

Bose,

Rinzel

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

Cortical connectivity supports motoric synchronization to both auditory and visual rhythms in a frontal-temporal network

Lu,

Yu,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

Synchronizing motoric responses to metrical sensory rhythms is key to social activities, e.g., group singing and dancing. It remains elusive, however, whether there is a common neural network for motoric synchronization to metrical rhythms from different sensory modalities. Here, we separate sensorimotor responses from basic sensory responses by combining a metrical sensorimotor synchronization task with frequency-domain magnetoencephalography (MEG) analysis. A common frontal-temporal network, not including visual cortex, is observed during both visual- and auditory-motor synchronization, and the network remains in congenitally deaf participants during visual-motor synchronization, suggesting the network is formed by intrinsic cortical connections instead of auditory experience. Furthermore, activation of the left and right frontal-temporal areas, as well as the ipsilateral white matter connection, separately predict the precision of auditory and visual synchronization. These results reveal a common but lateralized frontal-temporal network for visual- and auditory-motor synchronization, which is generated based on intrinsic cortical connections.

show abstract

Theoretical and empirical advances in understanding musical rhythm, beat and metre

Cited by 2 publications

References 224 publications

Dynamical mechanisms of how an RNN keeps a beat, uncovered with a low-dimensional reduced model

Dynamical mechanisms of how an RNN keeps a beat, uncovered with a low-dimensional reduced model

Cortical connectivity supports motoric synchronization to both auditory and visual rhythms in a frontal-temporal network

Contact Info

Product

Resources

About