Supervised associative learning in spiking neural network

In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and sp...

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Main Authors: Yusoff, Nooraini, Grüning, André
Other Authors: Diamantaras, Konstantinos
Format: Book Section
Published: Springer 2010
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Online Access:http://repo.uum.edu.my/12487/
http://dx.doi.org/10.1007/978-3-642-15819-3_30
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spelling my.uum.repo.124872014-10-26T02:45:33Z http://repo.uum.edu.my/12487/ Supervised associative learning in spiking neural network Yusoff, Nooraini Grüning, André QA76 Computer software In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations. Springer Diamantaras, Konstantinos Duch, Wlodek Iliadis, Lazaros S. 2010 Book Section PeerReviewed Yusoff, Nooraini and Grüning, André (2010) Supervised associative learning in spiking neural network. In: Artificial Neural Networks – ICANN 2010. Lecture Notes in Computer Science, 6352 (6352). Springer, pp. 224-229. ISBN 978-3-642-15818-6 http://dx.doi.org/10.1007/978-3-642-15819-3_30 doi:10.1007/978-3-642-15819-3_30
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
topic QA76 Computer software
spellingShingle QA76 Computer software
Yusoff, Nooraini
Grüning, André
Supervised associative learning in spiking neural network
description In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations.
author2 Diamantaras, Konstantinos
author_facet Diamantaras, Konstantinos
Yusoff, Nooraini
Grüning, André
format Book Section
author Yusoff, Nooraini
Grüning, André
author_sort Yusoff, Nooraini
title Supervised associative learning in spiking neural network
title_short Supervised associative learning in spiking neural network
title_full Supervised associative learning in spiking neural network
title_fullStr Supervised associative learning in spiking neural network
title_full_unstemmed Supervised associative learning in spiking neural network
title_sort supervised associative learning in spiking neural network
publisher Springer
publishDate 2010
url http://repo.uum.edu.my/12487/
http://dx.doi.org/10.1007/978-3-642-15819-3_30
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score 13.144533