Learning population of spiking neural networks with perturbation of conductances
Piotr Suszyński , Paweł Wawrzyński
AbstractIn this paper a method is presented for learning of spiking neural networks. It is based on perturbation of synaptic conductances. While this approach is known to be model-free, it is also known to be slow, because it applies improvement direction estimates with large variance. Two ideas are analysed to alleviate this problem: First, learning of many networks at the same time instead of one. Second, autocorrelation of perturbations in time. In the experimental study the method is validated on three learning tasks in which information is conveyed with frequency and spike timing. Index terms-Spiking neural networks, learning.
|Publication size in sheets||0.5|
|Book||Alippi C, Bu L, Zhao D (eds.): Proceedings of the International Joint Conference on Neural Networks 2013 Dallas, 2013, NY, USA, IEEE, ISBN 978-1-4673-6129-3, 2628 p.|
|Score|| = 10.0, 09-01-2020, BookChapterMatConfByIndicator|
= 15.0, 09-01-2020, BookChapterMatConfByIndicator
|Publication indicators||= 0; = 0|
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