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Signal Processing in Periodically Forced Gradient Frequency Neural Networks

By Ji Chul eKim and Edward W Large


Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active nonlinear 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

Topics: Auditory Perception, synchronization, neural networks, Signal processing, Nonlinear oscillation, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Publisher: Frontiers Media S.A.
Year: 2015
DOI identifier: 10.3389/fncom.2015.00152
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