Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding
Grüning, A and Sporea, I (2012) Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding Neural Processing Letters, volume.
Available under License : See the attached licence file.
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 be successfully 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.
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Identification Number :||https://doi.org/10.1007/s11063-012-9225-1|
|Related URLs :|
|Additional Information :||Preprint deposited in arXiv on 1 December 2011.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||16 Dec 2011 11:23|
|Last Modified :||01 Apr 2015 13:36|
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