Electromechanical memcapacitor model offering biologically plausible spiking

Zixi Zhang, Yuriy V. Pershin and Ivar Martin

Chaos, Solitons & Fractals, 2024, vol. 181, issue C

Abstract: In this article, we introduce a new nanoscale electromechanical device – a leaky memcapacitor – and show that it may be useful for the hardware implementation of spiking neurons. The leaky memcapacitor is a movable-plate capacitor that becomes quite conductive when the plates come close to each other. The equivalent circuit of the leaky memcapacitor involves a memcapacitive and memristive system connected in parallel. In the leaky memcapacitor, resistance and capacitance depend on the same internal state variable, which is the displacement of the movable plate. We have performed a comprehensive analysis showing that several types of spiking observed in biological neurons can be implemented with the leaky memcapacitor. Significant attention is paid to the dynamic properties of the model. As in leaky memcapacitors the capacitive, leaking resistive, and reset functionalities are implemented naturally within the same device structure, their use will simplify the creation of spiking neural networks.

Keywords: Memcapacitor; Neuron; Artificial neural networks; Spiking neurons; Nonlinear dynamics; Memristor (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:181:y:2024:i:c:s0960077924001528

DOI: 10.1016/j.chaos.2024.114601

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