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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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 |
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Universiti Utara Malaysia |
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Malaysia |
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http://repo.uum.edu.my/ |
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QA76 Computer software |
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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 |
| _version_ |
1644280925156016128 |
| score |
13.250246 |