Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

The Izhikevich (IZH) spiking neuron model can display spiking and bursting behaviors of neurons. Based on the switching property and bio-plausibility of the memristor, the memristive Izhikevich (MIZH) spiking neuron model is built. Firstly, the MIZH spiking model is introduced and used to generate 2...

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Published inFrontiers in neuroscience Vol. 16; p. 885322
Main Authors Fang, Xiaoyan, Duan, Shukai, Wang, Lidan
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 03.05.2022
Subjects
associative memory
IZH
memristor
MIZH
networks
Neuroscience
spiking neuron
spiking neuron
networks
associative memory
IZH
MIZH
memristor
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Summary:The Izhikevich (IZH) spiking neuron model can display spiking and bursting behaviors of neurons. Based on the switching property and bio-plausibility of the memristor, the memristive Izhikevich (MIZH) spiking neuron model is built. Firstly, the MIZH spiking model is introduced and used to generate 23 spiking patterns. We compare the 23 spiking patterns produced by the IZH and MIZH spiking models. Secondly, the MIZH spiking model actively reproduces various neuronal behaviors, including the excitatory cortical neurons, the inhibitory cortical neurons, and other cortical neurons. Finally, the collective dynamic activities of the MIZH neuronal network are performed, and the MIZH oscillatory network is constructed. Experimental results illustrate that the constructed MIZH spiking neuron model performs high firing frequency and good frequency adaptation. The model can easily simulate various spiking and bursting patterns of distinct neurons in the brain. The MIZH neuronal network realizes the synchronous and asynchronous collective behaviors. The MIZH oscillatory network can memorize and retrieve the information patterns correctly and efficiently with high retrieval accuracy.
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This article was submitted to Neuromorphic Engineering, a section of the journal Frontiers in Neuroscience
Reviewed by: Zhongrui Wang, The University of Hong Kong, Hong Kong SAR, China; Argha Mondal, Indian Institute of Technology Dhanbad, India
Edited by: Rajendra Bishnoi, Delft University of Technology, Netherlands
ISSN:1662-4548
1662-453X
1662-453X
DOI:10.3389/fnins.2022.885322