Semi-Supervised Learning Combining Backpropagation and STDP: STDP Enhances Learning by Backpropagation with a Small Amount of Labeled Data in a Spiking Neural Network

A semi-supervised learning method for spiking neural networks is proposed. The proposed method consists of supervised learning by backpropagation and subsequent unsupervised learning by spike-timing-dependent plasticity (STDP), which is a biologically plausible learning rule. Numerical experiments s...

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Bibliographic Details
Published in:Journal of the Physical Society of Japan 2021-07, Vol.90 (7), p.74802
Main Authors: Furuya, Kotaro, Ohkubo, Jun
Format: Article
Language:English
Subjects:
Back propagation
Back propagation networks
Neural networks
Semi-supervised learning
Spiking
Teaching methods
Unsupervised learning
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Summary:A semi-supervised learning method for spiking neural networks is proposed. The proposed method consists of supervised learning by backpropagation and subsequent unsupervised learning by spike-timing-dependent plasticity (STDP), which is a biologically plausible learning rule. Numerical experiments show that the proposed method improves the accuracy without additional labeling when a small amount of labeled data is used. This feature has not been achieved by existing semi-supervised learning methods of discriminative models. It is possible to implement the proposed learning method for event-driven systems. Hence, it would be highly efficient in real-time problems if it were implemented on neuromorphic hardware. The results suggest that STDP plays an important role other than self-organization when applied after supervised learning, which differs from the previous method of using STDP as pre-training interpreted as self-organization.
ISSN:0031-9015
1347-4073
DOI:10.7566/JPSJ.90.074802