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Title: Comparison of supervised learning methods for spike time coding in spiking neural networks

Creator:

Kasiński, Andrzej ; Ponulak, Filip

Date:

2006

Resource Type:

artykuł

Contributor:

Korbicz, Józef - red.

Subtitle:

Soft Computing in Control and Fault Diagnosis

Group publication title:

AMCS, Volume 16 (2006)

Abstract:

In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks(SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems,which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We areconcerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recentlearning methods? In order to answer this question, we discuss various approaches to the learning task considered. Weshortly describe the particular learning algorithms and report the results of experiments. Finally, we discuss the properties,assumptions and limitations of each method. We complete this review with a comprehensive list of pointers to the literature.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:46824

Pages:

101-113

Source:

AMCS, Volume 16, Number 1 (2006) ; click here to follow the link

Language:

eng

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Subject and keywords:

supervised learning spiking neural networks time coding temporal sequences of spikes

Object collections:

Last modified:

Sep 7, 2021

In our library since:

Aug 10, 2018

Number of object content hits:

356

All available object's versions:

https://zbc.uz.zgora.pl/repozytorium/publication/54915

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Comparison of supervised learning methods for spike time coding in spiking neural networks Sep 7, 2021

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