Firing Patterns and Synchronicity of Locally Active Memristor-Based Neuromorphic Circuits

Neuromorphic circuits with diverse neuronal firing features are the foundation for implementing neuron-based intelligent tasks. The locally active memristor (LAM) is a good candidate for constructing such kind of artificial circuits. However, it is attractive and challenging to explore synchronicity of firing patterns between LAM-based neuromorphic circuits, especially on hardware level. To this end, a neuromorphic circuit is constructed with a newly designed LAM emulator, a capacitor, a DC voltage, and an external current stimulus. Abundant stimulus-relating firing patterns are numerically revealed, whose bifurcation mechanism are theoretically discussed. Besides, using a LAM emulator as the coupling synapse, the interactions between two firing neuromorphic circuits are explored under identical and nonidentical stimulus conditions. The results manifest that the amplitude and phase of the stimulus current, as well as the static potential of the LAM branch, all exert significant influences on the synchronization behaviors. Furthermore, PCB-based analog circuits are implemented to demonstrate the firing patterns of single and coupled neuromorphic circuits from the perspective of physical experiments. This research could provide a viable paradigm to theoretical research and engineering application of memristive neuromorphic circuits.

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Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2025-05-05
DOI
https://doi.org/10.1109/tcsi.2025.3564662
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19

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article

Firing Patterns and Synchronicity of Locally Active Memristor-Based Neuromorphic Circuits

Quan Xu, Huagan Wu, Yuchen Zhang, Mo Chen
3 citations
IEEE Transactions on Circuits and Systems I Regular Papers
Advanced Memory and Neural Computing
2.19
article

Firing Patterns and Synchronicity of Locally Active Memristor-Based Neuromorphic Circuits

Quan Xu, Huagan Wu, Yuchen Zhang, Mo Chen
article en
3 citations

Abstract

Neuromorphic circuits with diverse neuronal firing features are the foundation for implementing neuron-based intelligent tasks. The locally active memristor (LAM) is a good candidate for constructing such kind of artificial circuits. However, it is attractive and challenging to explore synchronicity of firing patterns between LAM-based neuromorphic circuits, especially on hardware level. To this end, a neuromorphic circuit is constructed with a newly designed LAM emulator, a capacitor, a DC voltage, and an external current stimulus. Abundant stimulus-relating firing patterns are numerically revealed, whose bifurcation mechanism are theoretically discussed. Besides, using a LAM emulator as the coupling synapse, the interactions between two firing neuromorphic circuits are explored under identical and nonidentical stimulus conditions. The results manifest that the amplitude and phase of the stimulus current, as well as the static potential of the LAM branch, all exert significant influences on the synchronization behaviors. Furthermore, PCB-based analog circuits are implemented to demonstrate the firing patterns of single and coupled neuromorphic circuits from the perspective of physical experiments. This research could provide a viable paradigm to theoretical research and engineering application of memristive neuromorphic circuits.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 72(11)
Changzhou University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 12%
Advanced Memory and Neural Computing
2.19
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