Evaluation of the Effect of the Dynamic Behavior and Topology Co-Learning of Neurons and Synapses on the Small-Sample Learning Ability of Spiking Neural Network

Autor: Xu Yang, Yunlin Lei, Mengxing Wang, Jian Cai, Miao Wang, Ziyi Huan, Xialv Lin
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Brain Sciences, Vol 12, Iss 2, p 139 (2022)
Druh dokumentu: article
ISSN: 2076-3425
DOI: 10.3390/brainsci12020139
Popis: Small sample learning ability is one of the most significant characteristics of the human brain. However, its mechanism is yet to be fully unveiled. In recent years, brain-inspired artificial intelligence has become a very hot research domain. Researchers explored brain-inspired technologies or architectures to construct neural networks that could achieve human-alike intelligence. In this work, we presented our effort at evaluation of the effect of dynamic behavior and topology co-learning of neurons and synapses on the small sample learning ability of spiking neural network. Results show that the dynamic behavior and topology co-learning mechanism of neurons and synapses presented in our work could significantly reduce the number of required samples, while maintaining a reasonable performance on the MNIST data-set, resulting in a very lightweight neural network structure.
Databáze: Directory of Open Access Journals
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