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Desynchronization in synchronous multi-coupled chaotic neurons by mix-adaptive feedback control.

Yong Zhao¹, Zhaosheng Feng

¹School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo, 454000, People's Republic of China.

Journal of Biological Dynamics

|October 27, 2012

Summary

This study introduces an adaptive feedback method to desynchronize coupled chaotic neurons. The technique effectively breaks synchronization in neural models, demonstrating robustness against parameter variations.

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Area of Science:

Neuroscience
Chaos Theory
Dynamical Systems

Background:

Coupled chaotic neurons exhibit complex dynamics and synchronization phenomena.
Controlling synchronization in neural networks is crucial for understanding brain function and developing neuromorphic systems.

Purpose of the Study:

To propose an adaptive feedback scheme for desynchronizing synchronous multi-coupled chaotic neurons.
To analyze the effectiveness and robustness of the proposed desynchronization method.

Main Methods:

Utilizing the invariance principle of differential equations.
Developing a mix-adaptive feedback control strategy.
Performing numerical simulations on the Hindmarsh-Rose neural model with self-coupling.

Main Results:

The proposed adaptive feedback scheme effectively achieves desynchronization in coupled chaotic neurons.
Feedback strengths were observed to converge to a fixed value in finite time.
The desynchronization method demonstrated robustness against small parameter mismatches in three coupled neurons.

Conclusions:

The invariance principle provides a theoretical basis for adaptive feedback control of chaotic neural systems.
The mix-adaptive feedback scheme is a viable method for controlling synchronization in neural networks.
The findings have implications for understanding and manipulating neural network dynamics.