Abstract
Training spiking neurons to output desired spike train is a fundamental research in spiking neural networks. The current article proposes a novel and efficient supervised learning algorithm for spiking neurons. We divide the running time of spiking neurons into two classes: desired output time and not desired output time. Our learning method makes the membrane potential equal to threshold at desired output time, and makes the membrane potential lower than threshold at not desired output time. For efficiency, at not desired output time, we just calculate the membrane potential at some special time points where the spiking neuron is most likely to output a wrong spike. The experimental results show that the learning performance of the proposed method is better than the existing methods in accuracy and efficiency.
This work was supported by National Science Foundation of China under Grant 61273308, 61370073 and the Fundamental Research Funds for Central Universities under Grant ZYGX2012J068.
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Zhang, M., Qu, H., Li, J., Xie, X. (2015). A New Supervised Learning Algorithm for Spiking Neurons. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, K. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-13359-1_14
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DOI: https://doi.org/10.1007/978-3-319-13359-1_14
Publisher Name: Springer, Cham
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