Memristive FHN spiking neuron model and brain-inspired threshold logic computing

The FitzHugh-Nagumo (FHN) spiking model has rich dynamics behaviors and can imitate the firing pro-cess of a neuron. The memristor is a nonvolatile and resistance tunable device, which gradually becomes a potential candidate for performing dynamic behaviors and implementing neuromorphic computation in the nervous system. In this paper, the memristive FitzHugh-Nagumo (MFHN) spiking model is pre-sented. Firstly, we introduce a memristor to the FHN spiking model to build the MFHN spiking model and analyze the phase plane trajectory of the MFHN model. Secondly, we couple two MFHN models with a memristor and experimentally simulate the dynamic behaviors of two coupled neurons. The syn-chronous and asynchronous phenomena of two coupled neurons are discussed. Thirdly, the feasibility of the MFHN spiking model is demonstrated by the realization of binary logical operations and the imple-mentation of binary adders. The comparison between the MFHN binary adder and the FHN binary adder is conducted. The simulation results illustrate that the proposed model efficiently performs rich, dynamic behaviors and higher firing frequency. The coupled MFHN models show the effectiveness of a memristor acting as an electric coupling synapse. The threshold logic computation can be completed efficiently by the MFHN spiking model. The MFHN binary adders reproduce the computing functions and behaviors of the biological neuron. (c) 2022 Published by Elsevier B.V.

Automated summary

In this paper, the memristive FitzHugh-Nagumo (MFHN) spiking model is introduced, which utilizes a memristor to achieve rich dynamic behaviors and successfully simulates the synchronous and asynchronous phenomena of two coupled neurons. The experimental results demonstrate the efficiency of the MFHN model in completing threshold logic computation and reproducing the computing functions and behaviors of biological neurons.