Abstract
In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations.
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References
Bloom, F., Nelson, C.A., Lazerson, A.: Brain, Mind, and Behaviour, 3rd ed. Educational Broadcasting Corporation, US (2001)
Crick, F.: The recent excitement about neural networks. Nature 337(1989), 129–132 (1989)
Izhikevich, E.M.: Simple Model of Spiking Neurons. IEEE Trans. Neural Networks 14(6), 1569–1572 (2003)
Izhikevich, E.M.: Which model to use for cortical spiking neurons. IEEE Trans. Neural Networks 15(5), 1063–1070 (2004)
Kasinski, A., Ponulak, F.: Comparison of Supervised Learning Methods for Spike Time Coding in Spiking Neural Networks. Int. J. Appl. Math. Comput. Sci. 16(1), 101–113 (2006)
Dayan, P., Abbot, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT, Cambridge (2005)
Brunel, N., Lavigne, F.: Semantic Priming in a Cortical Network Model. Journal of Cognitive Neuroscience 21(12), 2300–2319 (2009)
Mongillo, G., Amit, D.J., Brunel, N.: Retrospective and prospective persistent activity induced by Hebbian learning in a recurrent cortical network, vol. 18, pp. 2011–2024 (2003)
Paugam-Moisy, H., Martinez, R., Bengio, S.: Delay learning and polychnization for reservoir computing. Neurocomputing 71(7-9), 1143–1158 (2008)
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Yusoff, N., Grüning, A. (2010). Supervised Associative Learning in Spiking Neural Network. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_30
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DOI: https://doi.org/10.1007/978-3-642-15819-3_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15818-6
Online ISBN: 978-3-642-15819-3
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