title: An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.
abstract: The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.
authors.affiliation: Department of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, Sichuan, China.
authors.forename: Xiurui
authors.initials: X
authors.lastname: Xie
authors.affiliation: Department of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, Sichuan, China.|Department of Physics, Humboldt University, 12489, Berlin, Berlin, Germany.|Potsdam Institute for Climate Impact Research(PIK), 14473 Potsdam, Germany.
authors.forename: Hong
authors.initials: H
authors.lastname: Qu
authors.affiliation: Department of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, Sichuan, China.
authors.forename: Guisong
authors.initials: G
authors.lastname: Liu
authors.affiliation: Department of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, Sichuan, China.
authors.forename: Malu
authors.initials: M
authors.lastname: Zhang
authors.affiliation: Department of Physics, Humboldt University, 12489, Berlin, Berlin, Germany.|Potsdam Institute for Climate Impact Research(PIK), 14473 Potsdam, Germany.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Kurths
mesh_terms: D000465:Algorithms; D000818:Animals; D003071:Cognition / Q000502:physiology*; D006801:Humans; D000069550:Machine Learning; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*