Studying on Improved Spiking Neural Network in Handwritten Digital Recognition

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 le...

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Bibliographic Details
Published inIOP conference series. Earth and environmental science Vol. 252; no. 2; pp. 22046 - 22055
Main Authors Jia, Lan, Miao, Hongxia, Qi, Bensheng
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 09.07.2019
Subjects
Algorithms
Firing pattern
Genetic algorithms
Handwriting recognition
Learning
Machine learning
Neural networks
Spiking
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Summary: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.
ISSN:1755-1307
1755-1315
1755-1315
DOI:10.1088/1755-1315/252/2/022046