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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Discrete bidirectional memristive neural network-based hyperchaotic system and its FPGA implementation.

Chunhua Wang¹, Zhibo Gong², Quanli Deng²

¹College of Computer Sicence and Electronic Engineering, Hunan University, Changsha, 410082, China; Greater Bay Area Institute for Innovation, Hunan University, Guangzhou, 511300, China.

Neural Networks : the Official Journal of the International Neural Network Society

|March 29, 2026

Summary

This summary is machine-generated.

This study introduces a novel discrete bidirectional memristive neural network (DBMNN) for hyperchaotic systems. The DBMNN exhibits complex dynamics, including hyperchaos, and is successfully implemented on an FPGA platform.

Keywords:

Discrete bidirectional neural network Discrete locally active memristor Dynamical behaviour FPGA implementation Hyperchaotic

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Area of Science:

Neuroscience
Chaos Theory
Computer Engineering

Background:

Memristors are crucial for biomimetic neural networks due to their unique properties.
Discrete-time memristive neural networks (DMNN) show promise in chaotic dynamics research.
Existing DMNN chaotic systems lack bidirectional feedback structures.

Purpose of the Study:

To propose a novel hyperchaotic system using a discrete bidirectional memristive neural network (DBMNN).
To implement the proposed DBMNN on a Field-Programmable Gate Array (FPGA) platform.
To analyze the complex chaotic behaviors of the DBMNN.

Main Methods:

A discrete locally active memristor was integrated into a discrete bidirectional neural network (DBNN).
Dynamical behaviors were analyzed using equilibrium point analysis, phase diagrams, Lyapunov exponents, bifurcation diagrams, and bi-parameter dynamic maps.
The DBMNN circuit was implemented on an FPGA for experimental validation.

Main Results:

The DBMNN demonstrated complex chaotic characteristics, including hyperchaos with four positive Lyapunov exponents.
The system exhibited heterogeneous coexisting attractors, transient chaos, and chaotic attractor jumping.
Amplitude control phenomena were observed over a wide range.

Conclusions:

The proposed DBMNN successfully generates hyperchaotic behavior.
FPGA implementation validated the numerical simulations and confirmed the system's dynamics.
This work contributes a novel bidirectional memristive neural network for chaotic system research.