An memristor-based synapse implementation using BCM learning rule

Yongchuang Huang; Junxiu Liu; Jim Harkin; Liam McDaid; Yuling Luo

doi:10.1016/j.neucom.2020.10.106

An memristor-based synapse implementation using BCM learning rule

Yongchuang Huang, Junxiu Liu, Jim Harkin, Liam McDaid, Yuling Luo

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23 Citations (Scopus)

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Abstract

A novel memristive synapse model based on the HP memristor is proposed in this paper, which can address the problem of synaptic weight infinite modulations. The sliding threshold mechanism of the Bienenstock-Cooper-Munro rule (BCM) is used to redefine the memristance (i.e. synaptic weight) adjustment process of the memristive synapse model. Based on the proposed memristor-based synapse and Leaky Integrate-and-Fire neurons, a spiking neural network (SNN) hardware fragment is constructed, where spike trains with different frequencies are used to evaluate the stability performance of the proposed SNN hardware. Results show that compared to other approaches, the network is stable under different stimuli due to the characteristics of the memristor-based synapse model, and prove that the proposed synapse model is able to mimic biological synaptic behaviour and the problem of synaptic weight infinite modulations is addressed.

Original language	English
Pages (from-to)	336-342
Number of pages	7
Journal	Neurocomputing
Volume	423
Early online date	16 Nov 2020
DOIs	https://doi.org/10.1016/j.neucom.2020.10.106
Publication status	Published (in print/issue) - 29 Jan 2021

Bibliographical note

Funding Information:
This research is supported by the National Natural Science Foundation of China under Grant 61976063 and the funding of Overseas 100 Talents Program of Guangxi Higher Education.

Publisher Copyright:
© 2020 Elsevier B.V.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

BCM theory
Learning rule
Memristor
Spiking neural networks

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.neucom.2020.10.106

Journal Pre-ProofAuthor Accepted Manuscript, 470 KBLicence: CC BY-NC-ND

Cite this

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title = "An memristor-based synapse implementation using BCM learning rule",

abstract = "A novel memristive synapse model based on the HP memristor is proposed in this paper, which can address the problem of synaptic weight infinite modulations. The sliding threshold mechanism of the Bienenstock-Cooper-Munro rule (BCM) is used to redefine the memristance (i.e. synaptic weight) adjustment process of the memristive synapse model. Based on the proposed memristor-based synapse and Leaky Integrate-and-Fire neurons, a spiking neural network (SNN) hardware fragment is constructed, where spike trains with different frequencies are used to evaluate the stability performance of the proposed SNN hardware. Results show that compared to other approaches, the network is stable under different stimuli due to the characteristics of the memristor-based synapse model, and prove that the proposed synapse model is able to mimic biological synaptic behaviour and the problem of synaptic weight infinite modulations is addressed.",

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author = "Yongchuang Huang and Junxiu Liu and Jim Harkin and Liam McDaid and Yuling Luo",

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AU - Harkin, Jim

AU - McDaid, Liam

AU - Luo, Yuling

N1 - Funding Information: This research is supported by the National Natural Science Foundation of China under Grant 61976063 and the funding of Overseas 100 Talents Program of Guangxi Higher Education. Publisher Copyright: © 2020 Elsevier B.V. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

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N2 - A novel memristive synapse model based on the HP memristor is proposed in this paper, which can address the problem of synaptic weight infinite modulations. The sliding threshold mechanism of the Bienenstock-Cooper-Munro rule (BCM) is used to redefine the memristance (i.e. synaptic weight) adjustment process of the memristive synapse model. Based on the proposed memristor-based synapse and Leaky Integrate-and-Fire neurons, a spiking neural network (SNN) hardware fragment is constructed, where spike trains with different frequencies are used to evaluate the stability performance of the proposed SNN hardware. Results show that compared to other approaches, the network is stable under different stimuli due to the characteristics of the memristor-based synapse model, and prove that the proposed synapse model is able to mimic biological synaptic behaviour and the problem of synaptic weight infinite modulations is addressed.

AB - A novel memristive synapse model based on the HP memristor is proposed in this paper, which can address the problem of synaptic weight infinite modulations. The sliding threshold mechanism of the Bienenstock-Cooper-Munro rule (BCM) is used to redefine the memristance (i.e. synaptic weight) adjustment process of the memristive synapse model. Based on the proposed memristor-based synapse and Leaky Integrate-and-Fire neurons, a spiking neural network (SNN) hardware fragment is constructed, where spike trains with different frequencies are used to evaluate the stability performance of the proposed SNN hardware. Results show that compared to other approaches, the network is stable under different stimuli due to the characteristics of the memristor-based synapse model, and prove that the proposed synapse model is able to mimic biological synaptic behaviour and the problem of synaptic weight infinite modulations is addressed.

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An memristor-based synapse implementation using BCM learning rule

Abstract

Bibliographical note

Keywords

UN SDGs

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