ArticleFrontiers in computational neuroscience2015
Signal Processing in Periodically Forced Gradient Frequency Neural Networks.
Article in Frontiers in computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 39 citations in OpenAlex.
- Deep oscillatory neural network.Scientific reports · 2025Article
- Musical neurodynamics.Nature reviews. Neuroscience · 2025Review
- Dynamical mechanisms of how an RNN keeps a beat, uncovered with a low-dimensional reduced model.Scientific reports · 2024Article
- Neural dynamics of predictive timing and motor engagement in music listening.Science advances · 2024Article
- Hebbian learning with elasticity explains how the spontaneous motor tempo affects music performance synchronization.PLoS computational biology · 2023Review
- Dynamic models for musical rhythm perception and coordination.Frontiers in computational neuroscience · 2023Review
- Neural Entrainment to Musical Pulse in Naturalistic Music Is Preserved in Aging: Implications for Music-Based Interventions.Brain sciences · 2022Article
- The relationship between entrainment dynamics and reading fluency assessed by sensorimotor perturbation.Experimental brain research · 2022Article
- A Dynamical, Radically Embodied, and Ecological Theory of Rhythm Development.Frontiers in psychology · 2022Review
- Modeling the tonotopic map using a two-dimensional array of neural oscillators.Frontiers in computational neuroscience · 2022Article
- Multifrequency Hebbian plasticity in coupled neural oscillators.Biological cybernetics · 2021Article
- A Complex-Valued Oscillatory Neural Network for Storage and Retrieval of Multidimensional Aperiodic Signals.Frontiers in computational neuroscience · 2021Article
- Delayed feedback embedded in perception-action coordination cycles results in anticipation behavior during synchronized rhythmic action: A dynamical systems approach.PLoS computational biology · 2019Article
- The uncoupling limit of identical Hopf bifurcations with an application to perceptual bistability.Journal of mathematical neuroscience · 2019Article
- A Dynamical Model of Pitch Memory Provides an Improved Basis for Implied Harmony Estimation.Frontiers in psychology · 2017Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.