Spiking and bursting activities in an NLAM-based CNN cell

Huagan Wu, Jinxiang Gu, Ning Wang, Mo Chen and Quan Xu

Chaos, Solitons & Fractals, 2025, vol. 192, issue C

Abstract: Spiking and bursting activities are potential candidates for biologically inspired artificial intelligence applications. This paper proposes an N-type locally active memristor-based (NLAM-based) cellular neural network (CNN) cell to generate spiking and bursting activities. The mathematical model of the NLAM-based CNN cell is deduced, as well as the equilibrium-trajectory and stability are collaborated. Numerical simulations reveal that the NLAM-based CNN cell can generate bursting activity for low-frequency stimulus and spiking activity for high-frequency stimulus, respectively. The amplitude and frequency of the stimuli can be deployed to regulate the spiking and bursting activities. Besides, the fold/Hopf bifurcation sets are numerically simulated, and then the bifurcation mechanism for bursting activity is theoretically deduced. Finally, an analog circuit is hired to capture the numerically simulated spiking and bursting activities. These investigations give foot-stones for exploring biologically inspired artificial intelligence applications.

Keywords: Spiking activity; Bursting activity; Locally active memristor; Cellular neural network; Analog circuit experiment (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:192:y:2025:i:c:s0960077924015212

DOI: 10.1016/j.chaos.2024.115969

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