STDP enhances learning by backpropagation in a spiking neural network

• URL: http://arxiv.org/abs/2102.10530v1
• Date: Sun, 21 Feb 2021 06:55:02 GMT
• Title: STDP enhances learning by backpropagation in a spiking neural network
• Authors: Kotaro Furuya and Jun Ohkubo
• Abstract summary: The proposed method improves the accuracy without additional labeling when a small amount of labeled data is used. It is possible to implement the proposed learning method for event-driven systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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