On the Applicability of Spiking Neural Network Models to Solve the Task of Recognizing Gender Hidden in Texts

Two approaches to utilize spiking neural networks, applicable for implementing in neuromorphic hardware with ultra-low power consumption, in the task of recognizing gender of a text author are analyzed. The first one is to obtain synaptic weights for the spiking network by training a formal network....

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Bibliographic Details
Published inProcedia computer science Vol. 101; pp. 187 - 196
Main Authors Sboev, Alexander, Litvinova, Tatiana, Vlasov, Danila, Serenko, Alexey, Moloshnikov, Ivan
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2016
Subjects
artificial neural networks
spike-timing-dependent plasticity
spiking neural networks
supervised learning
spiking neural networks
artificial neural networks
spike-timing-dependent plasticity
supervised learning
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Summary:Two approaches to utilize spiking neural networks, applicable for implementing in neuromorphic hardware with ultra-low power consumption, in the task of recognizing gender of a text author are analyzed. The first one is to obtain synaptic weights for the spiking network by training a formal network. We show the results obtained with this approach. The second one is a creation of a supervised learning algorithm for spiking networks that would be based on biologically plausible plasticity rules. We discuss possible ways to construct such algorithms.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2016.11.023