Abstract
The recurrent spiking neural networks include complex structures and implicit nonlinear mechanisms, the formulation of efficient supervised learning algorithm is difficult and remains an important problem in the research area. This paper proposes a new supervised multi-spike learning algorithm for recurrent spiking neural networks, which can implement the complex spatiotemporal pattern learning of spike trains. Using information encoded in precisely timed spike trains and their inner product operators, the error function is firstly constructed. Furthermore, the proposed algorithm defines the learning rules of synaptic weights based on inner product of spike trains. The algorithm is successfully applied to learn spike train patterns, and the high learning accuracy and efficiency are shown by the experimental results. In addition, the network structure parameters are analyzed, such as the neuron number and connectivity degree in the recurrent layer of spiking neural networks.
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Acknowledgment
The research is supported by the National Natural Science Foundation of China under Grants nos. 61762080, and the Medium and Small Scale Enterprises Technology Innovation Foundation of Gansu Province under Grant no. 17CX2JA038.
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Lin, X., Shi, G. (2018). A Supervised Multi-spike Learning Algorithm for Recurrent Spiking Neural Networks. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_22
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