The convergence analysis of SpikeProp algorithm with smoothing L 1∕2 regularization

Unlike the first and the second generation artificial neural networks, spiking neural networks (SNNs) model the human brain by incorporating not only synaptic state but also a temporal component into their operating model. However, their intrinsic properties require expensive computation during trai...

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Bibliographic Details
Published in:Neural networks 2018-07, Vol.103, p.19-28
Main Authors: Zhao, Junhong, Zurada, Jacek M, Yang, Jie, Wu, Wei
Format: Article
Language:English
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Summary:Unlike the first and the second generation artificial neural networks, spiking neural networks (SNNs) model the human brain by incorporating not only synaptic state but also a temporal component into their operating model. However, their intrinsic properties require expensive computation during training. This paper presents a novel algorithm to SpikeProp for SNN by introducing smoothing L regularization term into the error function. This algorithm makes the network structure sparse, with some smaller weights that can be eventually removed. Meanwhile, the convergence of this algorithm is proved under some reasonable conditions. The proposed algorithms have been tested for the convergence speed, the convergence rate and the generalization on the classical XOR-problem, Iris problem and Wisconsin Breast Cancer classification.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2018.03.007