Abstract
In this paper we propose SPAN, a LIF spiking neuron that is capable of learning input-output spike pattern association using a novel learning algorithm. The main idea of SPAN is transforming the spike trains into analog signals where computing the error can be done easily. As demonstrated in an experimental analysis, the proposed method is both simple and efficient achieving reliable training results even in the context of noise.
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Mohemmed, A., Schliebs, S., Kasabov, N. (2011). SPAN: A Neuron for Precise-Time Spike Pattern Association. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_83
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DOI: https://doi.org/10.1007/978-3-642-24958-7_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24957-0
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