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Learning-based sliding mode synchronization for fractional-order Hindmarsh-Rose neuronal models with deterministic

Danfeng Chen¹, Junsheng Li¹, Chengzhi Yuan²

¹School of Mechatronic Engineering and Automation, Foshan University, Foshan, China.

Frontiers in Neuroscience

|October 13, 2023

Summary

This study introduces a novel learning-based sliding mode control for fractional-order Hindmarsh-Rose neuronal models. It achieves fast and adaptable synchronization in unknown environments by learning and reusing neural dynamics.

Keywords:

deterministic learning fractional-order Hindmarsh-Rose system sliding mode control synchronization control system identification

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Area of Science:

Computational Neuroscience
Control Theory
Artificial Intelligence

Background:

Neural synchronization is crucial for neuronal signal processing and organism function.
Existing synchronization methods for neural models suffer from parameter dependency and limited adaptability.
Fractional-order Hindmarsh-Rose (FOHR) neuronal models present unique synchronization challenges in dynamic environments.

Purpose of the Study:

To address limitations in current neural synchronization techniques.
To develop a robust and adaptable control strategy for FOHR neuronal models.
To investigate synchronization in unknown dynamic environments.

Main Methods:

A learning-based sliding mode control algorithm utilizing the deterministic learning (DL) mechanism.
Accurate identification and storage of unknown FOHR system dynamics using constant weight neural networks.
Design of model-based and relearning-based controllers for efficient synchronization tasks.

Main Results:

Fast synchronization achieved by rapidly recalling learned neuronal dynamics, reducing online computation.
Continuous improvement in synchronization speed and accuracy through reusable, stored control experiences.
Demonstrated adaptability to new synchronization tasks without retraining control parameters.

Conclusions:

The proposed DL-based sliding mode control offers an effective solution for FOHR neuronal synchronization.
The method overcomes parameter dependency and enhances adaptability in dynamic environments.
This approach provides a foundation for advancing neural control and synchronization research.