Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)

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Unsupervised Learning of Digit Recognition Through Spike-Timing-Dependent Plasticity Based on Memristors

Authors
Yu Wang1, Yu Yan1, Yi Liu1, Yanzhong Zhang1, Yanji Wang1, Hao Zhang2, *, Tong Yi1, *
1College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
2Suzhou Laboratory, Suzhou, China
*Corresponding author. Email: zhangh@szlab.ac.cn
*Corresponding author. Email: tongyi@njupt.edu.cn
Corresponding Authors
Hao Zhang, Tong Yi
Available Online 22 September 2023.
DOI
10.2991/978-94-6463-242-2_27How to use a DOI?
Keywords
Spiking neural network (SNN); Spike-timing-dependent plasticity (STDP); Artificial synaptic and neuron; Memristor
Abstract

Neuromorphic computing based on spiking neural networks (SNNs) is a promising alternative in the field of intelligent computing, especially when traditional Von Neumann architectures is facing several choke point. Memristors, as the fourth-generation fundamental circuit element, play a crucial role in neuromorphic computing systems and are commonly employed as neural and synaptic devices. Due to their spike-based operation, memristive spiking neural networks (MSNNs) are considered to be superior and biologically plausible compared to alternative systems in terms of effectiveness. Here, the spike-timing-dependent plasticity (STDP) learning characteristic is reaped from our manufactured equipment. Utilizing memristor-based leaky integrate-and-fire (LIF) neurons and synapses, unsupervised learning of spiking neural networks with 784 × 324 × 324 architectures are constructed.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
22 September 2023
ISBN
10.2991/978-94-6463-242-2_27
ISSN
2589-4900
DOI
10.2991/978-94-6463-242-2_27How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yu Wang
AU  - Yu Yan
AU  - Yi Liu
AU  - Yanzhong Zhang
AU  - Yanji Wang
AU  - Hao Zhang
AU  - Tong Yi
PY  - 2023
DA  - 2023/09/22
TI  - Unsupervised Learning of Digit Recognition Through Spike-Timing-Dependent Plasticity Based on Memristors
BT  - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
PB  - Atlantis Press
SP  - 221
EP  - 226
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-242-2_27
DO  - 10.2991/978-94-6463-242-2_27
ID  - Wang2023
ER  -