Abstract
Usual spiking neural network with a hidden layer whose input and output are all spike times is very powerful for performing classification on real-world data. In this paper, we investigate the performance of a modified one-layer spiking neural network that involves both the spike time and derivative of the state function at firing time. It is shown by numerical experiments that a modified one-layer spiking neural network using same or fewer encoding neurons is almost as good as a usual spiking neural network with a hidden layer for solving some benchmark problems.
Research funded by National Natural Science Foundation of China (11171367) and the Fundamental Research Funds for the Central Universities of China.
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Yang, W., Yang, J., Wu, W. (2012). A Modified One-Layer Spiking Neural Network Involves Derivative of the State Function at Firing Time. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_18
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DOI: https://doi.org/10.1007/978-3-642-31346-2_18
Publisher Name: Springer, Berlin, Heidelberg
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