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Updated: Aug 26, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
Bifurcations of a Fractional-Order Four-Neuron Recurrent Neural Network with Multiple Delays
Yu Fei1, Rongli Li1, Xiaofang Meng1
1School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, China.
Abstract:
This paper investigates the bifurcation issue of fractional-order four-neuron recurrent neural network with multiple delays. First, the stability and Hopf bifurcation of the system are studied by analyzing the associated characteristic equations. It is shown that the dynamics of delayed fractional-order neural networks not only depend heavily on the communication delay but also significantly affects the applications with different delays. Second, we numerically demonstrate the effect of the order on the Hopf bifurcation. Two numerical examples illustrate the validity of the theoretical results at the end.
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