Coupling Heterogeneous Neural Networks With Memristors
This paper proposes a novel approach for cascading Hopfield and Hindmarsh-Rose neural networks using memristive synapses. The model integrates two distinct neural networks, each consisting of two identical neurons. First, we design a memristor and analyze its memristive characteristics. We then deve...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10918951/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850275804886335488 |
|---|---|
| author | Li Zhang Rongli Jiang Yike Ma Xiangkai Pu Zhongyi Li |
| author_facet | Li Zhang Rongli Jiang Yike Ma Xiangkai Pu Zhongyi Li |
| author_sort | Li Zhang |
| collection | DOAJ |
| description | This paper proposes a novel approach for cascading Hopfield and Hindmarsh-Rose neural networks using memristive synapses. The model integrates two distinct neural networks, each consisting of two identical neurons. First, we design a memristor and analyze its memristive characteristics. We then develop the Hopfield and Hindmarsh-Rose networks with dual neurons, utilizing memristors to couple these sub-networks, forming a new four-neuron neural network. The equilibrium points and system stability are thoroughly analyzed. The dynamic behavior of the network is examined through bifurcation diagrams, two-parameter bifurcation plots, Lyapunov exponents, and phase diagrams, revealing phenomena such as chaos, periodicity, period-doubling bifurcations, and alternating-period chaos. To validate the numerical simulations, we implement the phase diagram on an STM32 hardware platform. Finally, we propose a DNA-based image encryption algorithm leveraging chaotic sequences generated by the network. The performance analysis of encrypted images demonstrates that the algorithm offers strong robustness during transmission and effective resistance against attacks, ensuring privacy and security. |
| format | Article |
| id | doaj-art-bb617067fa1a490a93c2975d65a52c92 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bb617067fa1a490a93c2975d65a52c922025-08-20T01:50:33ZengIEEEIEEE Access2169-35362025-01-0113538355384810.1109/ACCESS.2025.354997810918951Coupling Heterogeneous Neural Networks With MemristorsLi Zhang0https://orcid.org/0000-0003-1367-4031Rongli Jiang1https://orcid.org/0009-0002-4622-263XYike Ma2Xiangkai Pu3Zhongyi Li4School of Integrated Circuits, Anhui University, Hefei, ChinaSchool of Integrated Circuits, Anhui University, Hefei, ChinaSchool of Integrated Circuits, Anhui University, Hefei, ChinaChina National Building Material (CNBM) Environmental Protection Research Institute (Jiangsu) Company Ltd., Yancheng, ChinaSchool of Integrated Circuits, Anhui University, Hefei, ChinaThis paper proposes a novel approach for cascading Hopfield and Hindmarsh-Rose neural networks using memristive synapses. The model integrates two distinct neural networks, each consisting of two identical neurons. First, we design a memristor and analyze its memristive characteristics. We then develop the Hopfield and Hindmarsh-Rose networks with dual neurons, utilizing memristors to couple these sub-networks, forming a new four-neuron neural network. The equilibrium points and system stability are thoroughly analyzed. The dynamic behavior of the network is examined through bifurcation diagrams, two-parameter bifurcation plots, Lyapunov exponents, and phase diagrams, revealing phenomena such as chaos, periodicity, period-doubling bifurcations, and alternating-period chaos. To validate the numerical simulations, we implement the phase diagram on an STM32 hardware platform. Finally, we propose a DNA-based image encryption algorithm leveraging chaotic sequences generated by the network. The performance analysis of encrypted images demonstrates that the algorithm offers strong robustness during transmission and effective resistance against attacks, ensuring privacy and security.https://ieeexplore.ieee.org/document/10918951/Hopfield neural networkmemristorHindmarsh-Rose neural networkhardware implementationimage encryption |
| spellingShingle | Li Zhang Rongli Jiang Yike Ma Xiangkai Pu Zhongyi Li Coupling Heterogeneous Neural Networks With Memristors IEEE Access Hopfield neural network memristor Hindmarsh-Rose neural network hardware implementation image encryption |
| title | Coupling Heterogeneous Neural Networks With Memristors |
| title_full | Coupling Heterogeneous Neural Networks With Memristors |
| title_fullStr | Coupling Heterogeneous Neural Networks With Memristors |
| title_full_unstemmed | Coupling Heterogeneous Neural Networks With Memristors |
| title_short | Coupling Heterogeneous Neural Networks With Memristors |
| title_sort | coupling heterogeneous neural networks with memristors |
| topic | Hopfield neural network memristor Hindmarsh-Rose neural network hardware implementation image encryption |
| url | https://ieeexplore.ieee.org/document/10918951/ |
| work_keys_str_mv | AT lizhang couplingheterogeneousneuralnetworkswithmemristors AT ronglijiang couplingheterogeneousneuralnetworkswithmemristors AT yikema couplingheterogeneousneuralnetworkswithmemristors AT xiangkaipu couplingheterogeneousneuralnetworkswithmemristors AT zhongyili couplingheterogeneousneuralnetworkswithmemristors |