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Biologically inspired temporal sequence learning

We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons.In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the...

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Main Authors: Yusoff, Nooraini, Grüning, André
Format: Article
Language:English
Published: Elsevier Ltd. 2012
Subjects:
QA76 Computer software
Online Access:http://repo.uum.edu.my/12490/1/1-s2.pdf
http://repo.uum.edu.my/12490/
http://dx.doi.org/10.1016/j.proeng.2012.07.179
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spelling my.uum.repo.124902014-10-26T03:10:03Z http://repo.uum.edu.my/12490/ Biologically inspired temporal sequence learning Yusoff, Nooraini Grüning, André QA76 Computer software We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons.In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the weight changes derived from spike-timing dependent plasticity (STDP) function.The dynamic properties of our model can be attributed to the sparse and recurrent connectivity, synaptic transmission delays, background activity and inter-stimulus interval (ISI).We have tested the learning in visual recognition task, and temporal AND and XOR problems.The network can be trained to associate a stimulus pair with its target response and to discriminate the temporal sequence of the stimulus presentation. Elsevier Ltd. 2012 Article PeerReviewed application/pdf en cc_by http://repo.uum.edu.my/12490/1/1-s2.pdf Yusoff, Nooraini and Grüning, André (2012) Biologically inspired temporal sequence learning. Procedia Engineering, 41. pp. 319-325. ISSN 1877-7058 http://dx.doi.org/10.1016/j.proeng.2012.07.179 doi:10.1016/j.proeng.2012.07.179
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/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Yusoff, Nooraini
Grüning, André
Biologically inspired temporal sequence learning
description We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons.In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the weight changes derived from spike-timing dependent plasticity (STDP) function.The dynamic properties of our model can be attributed to the sparse and recurrent connectivity, synaptic transmission delays, background activity and inter-stimulus interval (ISI).We have tested the learning in visual recognition task, and temporal AND and XOR problems.The network can be trained to associate a stimulus pair with its target response and to discriminate the temporal sequence of the stimulus presentation.
format Article
author Yusoff, Nooraini
Grüning, André
author_facet Yusoff, Nooraini
Grüning, André
author_sort Yusoff, Nooraini
title Biologically inspired temporal sequence learning
title_short Biologically inspired temporal sequence learning
title_full Biologically inspired temporal sequence learning
title_fullStr Biologically inspired temporal sequence learning
title_full_unstemmed Biologically inspired temporal sequence learning
title_sort biologically inspired temporal sequence learning
publisher Elsevier Ltd.
publishDate 2012
url http://repo.uum.edu.my/12490/1/1-s2.pdf
http://repo.uum.edu.my/12490/
http://dx.doi.org/10.1016/j.proeng.2012.07.179
_version_ 1644280926024237056
score 13.211869

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