Tuning Bienenstock–Cooper–Munro learning rules in a two-terminal memristor for neuromorphic computing-Reference-Cited by-同舟云学术

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Tuning Bienenstock–Cooper–Munro learning rules in a two-terminal memristor for neuromorphic computing

Published:2023 Issue:23 Volume:25 Page:15920-15928
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Li Zeyang¹,Liu Peilin¹,Yang Guanghong²,Jia Caihong¹^ORCID,Zhang Weifeng¹³

Affiliation:

1. School of Future Technology, Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng 475004, China

2. Key Lab for Special Functional Materials of Ministry of Education, School of Materials, Henan University, Kaifeng 475004, P. R. China

3. Institute of Quantum Materials and Physics, Henan Academy of Sciences, Zhengzhou 450046, China

Abstract

The non-monotonous BCM learning rule with the enhanced depression effect is achieved by applying presynaptic pulses and changing series resistance.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Henan Province

Key Scientific Research Project of Colleges and Universities in Henan Province

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D3CP01134H

Reference46 articles.

1. A Memristor Neural Network Using Synaptic Plasticity and Its Associative Memory

2. Hf0.5Zr0.5O2-based ferroelectric memristor with multilevel storage potential and artificial synaptic plasticity

3. Ferroelectric polymer-based artificial synapse for neuromorphic computing

4. Reconfigurable logic and neuromorphic circuits based on electrically tunable two-dimensional homojunctions

5. One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications

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