Related Experiment Video
Updated: Jan 10, 2026
08:07
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
8.3K
Discrete Memristive Hopfield Neural Network with Grid-Polyhedral Hyperchaos for FPGA-Based Pseudorandom Number
Han Bao1, Ruimin Wang1, Ning Wang1
1Wang Zheng School of Microelectronics, Changzhou University, Changzhou, 213159, China.
Summary
This study introduces a novel discrete two-memristor Hopfield neural network (DTM-HNN) for generating tunable hyperchaotic attractors. The developed hardware pseudorandom number generator (PRNG) offers high throughput and security for practical applications.
Area of Science:
- Neuroscience
- Chaos Theory
- Hardware Security
Background:
- Hopfield neural networks (HNNs) are foundational models in computational neuroscience and associative memory.
- Hyperchaotic systems offer complex dynamics valuable for secure communication and random number generation.
- Memristors, as emerging non-volatile memory devices, provide unique properties for hardware implementations of neural networks.
Purpose of the Study:
- To propose and analyze a discrete two-memristor-based Hopfield neural network (DTM-HNN).
- To investigate the generation of grid-polyhedral and homogeneous coexisting hyperchaotic attractors.
- To develop and evaluate a hardware pseudorandom number generator (PRNG) based on the DTM-HNN.
Main Methods:
- Integration of two memristors with piecewise-linear state functions into a two-neuron HNN.
- Fixed-point and Jacobian analyses to understand attractor behavior.
- Numerical simulations using diagonal distance and spectral entropy for quantitative characterization.
- Design and implementation of a digital hardware device for real-time observation and parameter configuration.
Main Results:
- The DTM-HNN successfully generates tunable grid-polyhedral and homogeneous coexisting hyperchaotic attractors.
- Memristor parameters and scaling factors allow flexible regulation of attractor structure and scale.
- Rich dynamic behaviors, including state transitions, were observed and quantitatively analyzed.
- A hardware PRNG based on dynamic state switching demonstrated high throughput and passed NIST randomness tests.
Conclusions:
- The DTM-HNN provides a flexible platform for generating complex hyperchaotic dynamics.
- The developed hardware PRNG exhibits practical utility in security applications due to its performance and key sensitivity.
- This work bridges theoretical models of HNNs with practical hardware implementations for advanced functionalities.
