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Updated: Sep 18, 2025
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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
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Generating Simple Cyclic Memristive Neural Network Circuit With Controllable Multiscroll Attractors and Multivariable
Summary
This study introduces a novel memristive neural network circuit capable of generating complex multiscroll chaotic attractors. The design offers a simpler, more implementable alternative for neuromorphic computing applications.
Area of Science:
- Neuroscience
- Chaos Theory
- Electronic Engineering
Background:
- Memristors exhibit synaptic properties, making them suitable for neuromorphic circuits like neural networks.
- Existing complex dynamic neural networks often suffer from high dimensionality and excessive connections, hindering practical implementation.
Purpose of the Study:
- To design a simple cyclic memristive neural network (SCMNN) circuit capable of generating complex multiscroll chaotic attractors.
- To overcome the implementation challenges of high-dimensional neural network circuits.
Main Methods:
- Incorporating an arctangent function memristor into a simple cyclic neural network (SCNN).
- Utilizing an external stimulus current to generate multiscroll attractors.
- Activating switches in the memristor equivalent circuit to control the number of scrolls.
Main Results:
- The designed SCMNN successfully generates multiscroll chaotic attractors.
- The number of scrolls expands with activated switches and can be controlled via parameter variation.
- The circuit demonstrates anti-interference capabilities and achieved multivariable amplitude control.
- Experimental validation using a microcontroller and oscilloscope confirmed attractor generation.
Conclusions:
- The proposed SCMNN circuit offers a viable and implementable approach for generating complex chaotic dynamics.
- The circuit's ability to produce controllable multiscroll attractors holds potential for advanced neuromorphic applications.
- NIST testing verified the circuit's application value in engineering.
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