期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：2
页码：1-11
DOI：10.1088/1755-1315/252/2/022046
出版社：IOP Publishing
摘要：In this paper, an unsupervised learning algorithm of spiking neural network (SNN) by using biologically plausible mechanisms is presented for achieving handwritten digital recognition. Firstly, the spike-timing-dependent plasticity (STDP) model was established based on pre- and postsynaptic trace learning rule to determine the connection between neurons. Secondly, genetic algorithm (GA) was used to optimize the initial presynaptic weights and axon delay in the neural network. Finally, the Mixed National Institute of Standards and Technology (MNIST) dataset was trained and tested. Experimental results show that the proposed method can effectively improve the recognition rate of handwritten numbers and realize unsupervised learning of handwritten digital recognition. The accuracy in the MNIST benchmark test is increased by using this improved unsupervised SNN learning scheme. The computational complexity is greatly reduced; therefore, the calculation speed is increased. Simulation result is better than the implementation of the previous unsupervised SNNs.
