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Topology identification and dynamical pattern recognition for Hindmarsh-Rose neuron model via deterministic learning.

Danfeng Chen1, Junsheng Li1, Wei Zeng2

  • 1School of Mechatronic Engineering and Automation, Foshan University, Foshan, 528225 People's Republic of China.

Cognitive Neurodynamics
|January 27, 2023
PubMed
Summary

This study explores the Hindmarsh-Rose neuron model, revealing complex dynamics crucial for understanding neurological disorders like Parkinson's and epilepsy. The research introduces a novel method for identifying neural network dynamics, aiding in disease diagnosis and treatment.

Keywords:
Deterministic learningHindmarsh–Rose neural networkNeuronal synchronizationPattern recognitionTopology identification

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Area of Science:

  • Computational Neuroscience
  • Nonlinear Dynamics
  • Artificial Intelligence

Background:

  • Neuronal synchronization is critical for brain function and implicated in neurological disorders such as Parkinson's disease and epilepsy.
  • Understanding the complex nonlinear dynamics of neuronal systems is essential for advancing neurobiological research and clinical applications.

Purpose of the Study:

  • To numerically investigate the complex nonlinear behavior of the Hindmarsh-Rose neuron system.
  • To develop a method for identifying the nonlinear dynamics and topologies of neural networks in unknown environments.
  • To construct a fast dynamical pattern recognition method based on system synchronization.

Main Methods:

  • Numerical simulation of the Hindmarsh-Rose neuron system, analyzing time responses, bifurcation diagrams, and Lyapunov exponents.
  • Application of a deterministic learning algorithm for identifying unknown system dynamics and network topologies.
  • Development of a pattern recognition method utilizing system synchronization and time-invariant representations.

Main Results:

  • The Hindmarsh-Rose system exhibits diverse and complex dynamic behaviors influenced by parameter variations.
  • Accurate local identification of unknown dynamics and topologies of the Hindmarsh-Rose system was achieved using the deterministic learning algorithm.
  • A fast dynamical pattern recognition method was successfully constructed based on system synchronization.

Conclusions:

  • The study provides insights into the neurobiological mechanisms underlying neurological diseases.
  • The developed methods offer potential for early diagnosis, classification, and treatment of conditions like Parkinson's and epilepsy.
  • This research supports biological experiments and clinical applications in neuroscience.