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Multiscroll hidden attractor in memristive autapse neuron model and its memristor-based scroll control and
Zhiqiang Wan1, Yi-Fei Pu1, Qiang Lai2
1College of Computer Science, Sichuan University, Chengdu 610065, China.
Summary
This study introduces a novel memristive autapse HR (MAHR) neuron model with a multiscroll hidden attractor. The model simplifies complex mathematical forms, enabling easier research and application of neurodynamic systems.
Area of Science:
- Neuroscience
- Complex Systems
- Nonlinear Dynamics
Background:
- Current memristor models for multiscroll attractors use complex polynomial functions, limiting research and applications.
- There is a need for simpler, more applicable memristor models in neurodynamics.
Purpose of the Study:
- To develop a novel memristor model and a memristive autapse HR (MAHR) neuron model with a multiscroll hidden attractor.
- To simplify the mathematical complexity associated with generating multiscroll attractors.
- To demonstrate the physical feasibility and practical applications of the proposed model.
Main Methods:
- Devised a unique memristor model without relying on polynomial or nested composite functions.
- Developed a memristive autapse HR (MAHR) neuron model incorporating the new memristor.
- Utilized simulation time to regulate the quantity of scrolls in the hidden attractor.
- Incorporated a simple control factor to enhance the MAHR neuron model.
- Performed numerical analysis to study parameter influence on scroll quantity.
- Conducted microcontroller-based hardware experiments for feasibility validation.
- Proposed an image encryption scheme to explore real-world applicability.
Main Results:
- Introduced a novel MAHR neuron model with a multiscroll hidden attractor.
- Demonstrated that the quantity of scrolls can be regulated by simulation time.
- Showcased that scroll quantity in the improved MAHR model is adjustable via a single memristor parameter or initial condition.
- Confirmed the physical feasibility of the improved MAHR neuron model through hardware experiments.
- Proposed a functional image encryption scheme based on the MAHR model.
Conclusions:
- The novel MAHR neuron model offers a simplified approach to generating multiscroll hidden attractors.
- The model's parameters provide convenient control over attractor complexity.
- Hardware validation confirms the model's physical feasibility.
- The proposed image encryption scheme highlights the model's potential for real-world applications in secure communication.
