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2018 | OriginalPaper | Chapter

A Supervised Multi-spike Learning Algorithm for Recurrent Spiking Neural Networks

Authors : Xianghong Lin, Guoyong Shi

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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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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Ghosh-Dastidar, S., Adeli, H.: Spiking neural networks. Int. J. Neural Syst. 19(4), 295–308 (2009)CrossRef

Adnanshiltagh, N.: Recurrent spiking neural networks the third generation in identification of systems. Int. J. Comput. Appl. 88(1), 40–43 (2014)

Lin, X., Wang, X., Zhang, N., et al.: Supervised learning algorithms for spiking neural networks: a review. Acta Electron. Sin. 43(3), 577–586 (2015)

Woo, J., Botzheim, J., Kubota, N.: Emotional empathy model for robot partners using recurrent spiking neural network model with Hebbian-LMS learning. Malays. J. Comput. Sci. 30(4), 258–285 (2017)CrossRef

Allen, J.N., Abdel-Aty-Zohdy, H.S., Ewing, R.L.: Plasticity recurrent spiking neural networks for olfactory pattern recognition. In: Midwest Symposium on Circuits and Systems, pp. 1741–1744. IEEE (2005)

Shen, J., Lin, K., Wang, Y., et al.: Character recognition from trajectory by recurrent spiking neural networks. In: The 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2900–2903. IEEE (2017)

Smith, A.W., Zipser, D.: Learning sequential structure with the real-time recurrent learning algorithm. Int. J. Neural Syst. 1(2), 125–131 (2011)CrossRef

Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)CrossRef

Selvaratnam, K., Kuroe, Y., Mori, T.: Learning methods of recurrent spiking neural networks-transient and oscillatory spike trains. Trans. Inst. Syst. Control Inf. Eng. 13(3), 95–104 (2000)

10.

Kuroe Y., Ueyama T.: Learning methods of recurrent spiking neural networks based on adjoint equations approach. In: International Joint Conference on Neural Networks, pp. 1–8. IEEE (2010)

11.

Tiňo, P., Mills, A.J.S.: Learning beyond finite memory in recurrent networks of spiking neurons. Neural Comput. 18(3), 591–613 (2006)MathSciNetCrossRef

12.

Brodeur, S., Rouat, J.: Regulation toward self-organized criticality in a recurrent spiking neural reservoir. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012. LNCS, vol. 7552, pp. 547–554. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33269-2_69CrossRef

13.

Bourdoukan, R., Deneve, S.: Enforcing balance allows local supervised learning in spiking recurrent networks. In: International Conference on Neural Information Processing Systems, pp. 982–990. MIT Press (2015)

14.

Diehl, P.U., Zarrella, G., Cassidy, A., et al.: Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware. In: IEEE International Conference on Rebooting Computing, pp. 1–8. IEEE (2016)

15.

Gilra, A., Gerstner, W.: Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network. Elife 6, e28295 (2017)CrossRef

16.

Paiva, A.R., Park, I., Príncipe, J.C.: A reproducing kernel Hilbert space framework for spike train signal processing. Neural Comput. 21(2), 424–449 (2009)MathSciNetCrossRef

17.

Carnell, A., Richardson, D.: Linear algebra for time series of spikes. In: Proceedings of European Symposium on Artificial Neural Networks, pp. 363–368. DBLP (2005)

18.

Sporea, I., Grüning, A.: Supervised learning in multilayer spiking neural networks. Neural Comput. 25(2), 473–509 (2013)MathSciNetCrossRef

19.

Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)CrossRef

Title: A Supervised Multi-spike Learning Algorithm for Recurrent Spiking Neural Networks
Authors: Xianghong Lin
Guoyong Shi
Publisher: Springer International Publishing
Book: Artificial Neural Networks and Machine Learning – ICANN 2018
Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-01418-6_22

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