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  • Entrainment of Weakly Coupled Canonical Oscillators with Applications in Gradient Frequency Neural Networks Using Approximating Analytical Methods
Title: Entrainment of Weakly Coupled Canonical Oscillators with Applications in Gradient Frequency Neural Networks Using Approximating Analytical Methods
Solving phase equations for systems with high degrees of nonlinearities is cumbersome. However, in the case of two coupled canonical oscillators, that is, a reduced model of translated Wilson–Cowan neuronal dynamics, under slowly varying amplitude and rotating wave approximations, we suggested a convenient way to find their average relative phase evolution. This approach enabled us to find an explicit solution for the average relative phase of the two coupled canonical oscillators based on the original neuronal model parameters, and importantly, to find their phase-locking constraint. This methodology is straightforward to implement in any Wilson–Cowan-type coupled oscillators with applications in gradient frequency neural networks (GFNNs).  more » « less
Award ID(s):
1735225
PAR ID:
10280760
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Mathematics
Volume:
8
Issue:
8
ISSN:
2227-7390
Page Range / eLocation ID:
1312
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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