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Multi-scroll hidden attractor in memristive HR neuron model under electromagnetic radiation and its applications
Sen Zhang1, Jiahao Zheng2, Xiaoping Wang2
1Institute of Artificial Intelligence, School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China.
Chaos (Woodbury, N.Y.)
|March 23, 2021
Summary
This study introduces a novel memristive Hindmarsh-Rose neuron model that generates controllable multi-scroll hidden attractors influenced by electromagnetic radiation. This model shows promise for secure chaos-based applications like image encryption.
Area of Science:
- Computational Neuroscience
- Nonlinear Dynamics
- Memristor Applications
Background:
- The Hindmarsh-Rose (HR) neuron model is a foundational model for studying neuronal spiking dynamics.
- Existing memristor-based HR models have limitations in generating complex attractor structures.
- External stimuli, like electromagnetic radiation, can influence neuronal behavior, but their integration into complex models is underexplored.
Purpose of the Study:
- To propose a novel no-equilibrium Hindmarsh-Rose (HR) neuron model incorporating a memristive electromagnetic radiation effect.
- To investigate the generation of multi-scroll hidden attractors with controllable topological structures.
- To explore the model's potential for engineering applications, including pseudo-random number generation and image encryption.
Main Methods:
- Numerical simulations including phase portraits, bifurcation diagrams, Lyapunov exponents, and two-parameter diagrams were used to analyze complex dynamics.
- Hardware circuit experiments were conducted to validate theoretical analyses and numerical simulations.
- The memristor's internal parameters and external electromagnetic radiation intensity were systematically varied to control attractor properties.
Main Results:
- The proposed memristive HR neuron model successfully generates multi-scroll hidden attractors.
- The number and parity of scrolls are controllable by adjusting memristor parameters and electromagnetic radiation intensity.
- Hardware experiments confirmed the model's dynamic behavior, validating the numerical findings.
Conclusions:
- The novel memristive HR neuron model offers a unique platform for generating complex, controllable hidden attractors.
- The model demonstrates excellent randomness and high security, making it suitable for chaos-based real-world applications.
- This research bridges theoretical neuroscience with practical engineering applications through memristive dynamics.
