Abstract
Using gradient descent, we propose a new backpropagation learning algorithm for spiking neural networks with multi-layers, multi-synapses between neurons, and multi-spiking neurons. It adjusts synaptic weights, delays, and time constants, and neurons’ thresholds in output and hidden layers. It guarantees convergence to minimum error point, and unlike SpikeProp and its extensions, does not need a one-to-one correspondence between actual and desired spikes in advance. So, it is stably and widely applicable to practical problems.
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References
Bohte, S.M., Kok, J.N., La Poutre, H.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48, 17–37 (2002)
Florian, R.V.: The chronotron: a neuron that learns to fire temporally precise spike patterns. PLoS ONE 7(8), e40233 (2012)
Gerstner, W., Kistler, W.: Spiking Neuron Models. Cambridge University Press, Cambridge (2002)
Ghosh-Dastidar, S., Adeli, H.: A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Netw. 22, 1419–1431 (2009)
Guetig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006)
Matsuda, S.: BPSpike:a backpropagation learning for all parameters in spiking neural networks with multiple layers and multiple spikes. In: IJCNN 2016 (2016)
Mohemmed, A., Schliebs, S., Matsuda, S., Kasabov, N.: Training spiking neural networks to associate spatio-temporal input-output spike patterns. Int. J. Neural Syst. 22(4), 1250012 (2012)
Ponulak, Filip: Supervised learning in spiking neural networks with ReSuMe method, Doctoral Dissertation. Poznan University of Technology, Poznan, Poland (2006)
Schrauwen, B., van Campenhout, J.: Improving spikeProp: enhancements to an error-backpropagation rule for spiking neural networks. In: Proceeduings of 15th ProRISC Workshop (2004)
Yan, X., Zeng, X., Han, L., Yang, J.: A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks. Neural Netw. 43, 99–113 (2013)
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Matsuda, S. (2016). BPSpike II: A New Backpropagation Learning Algorithm for Spiking Neural Networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_7
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DOI: https://doi.org/10.1007/978-3-319-46672-9_7
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