Abstract
Spiking neural networks are shown to be suitable tools for the processing of spatio-temporal information. However, due to their intricately discontinuous and implicit nonlinear mechanisms, the formulation of efficient supervised learning algorithms for spiking neural networks is difficult, which has become an important problem in the research area. This paper presents a new supervised, multi-spike learning algorithm for spiking neurons, which can implement the complex spatio-temporal pattern learning of spike trains. The proposed algorithm firstly defines nonlinear inner products operators to mathematically describe and manipulate spike trains, and then derive the learning rule from the common Widrow-Hoff rule with the nonlinear inner products of spike trains. The algorithm is successfully applied to learn sequences of spikes. The experimental results show that the proposed algorithm is effective for solving complex spatio-temporal pattern learning problems.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Nos. 61165002 and 61363058), the Natural Science Foundation of Gansu Province of China (No. 1506RJZA127), and Scientific Research Project of Universities of Gansu Province (No. 2015A-013).
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Wang, X., Lin, X., Zhao, J., Ma, H. (2016). Supervised Learning Algorithm for Spiking Neurons Based on Nonlinear Inner Products of Spike Trains. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9772. Springer, Cham. https://doi.org/10.1007/978-3-319-42294-7_8
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DOI: https://doi.org/10.1007/978-3-319-42294-7_8
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