NDLI: Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

Content Provider	Springer Nature Link
Author	Grüning, André Sporea, Ioana
Copyright Year	2012
Abstract	Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can successfully be applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons. We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons. While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understanding of how computations can be done with spike trains.
Starting Page	117
Ending Page	134
Page Count	18
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	36
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2012-05-15
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Spiking neural networks Supervised learning Logical operation Spike trains Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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