Staff View: Learning anticipation through priming in spatio-temporal neural networks

Learning anticipation through priming in spatio-temporal neural networks

In this paper, we propose a reward-based learning model inspired by the findings from a behavioural study and biologically realistic properties of spatio-temporal neural networks.The model simulates the cognitive priming effect in stimulus-stimulus-response association.Synaptic plasticity is depende...

Full description

Saved in:

Bibliographic Details
Main Authors:	Yusoff, Nooraini, Grüning, André
Other Authors:	Tingwen, Huang
Format:	Book Section
Published:	Springer 2012
Subjects:	QA76 Computer software
Online Access:	http://repo.uum.edu.my/12488/ http://dx.doi.org/10.1007/978-3-642-34475-6_21
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.uum.repo.12488
record_format	eprints
spelling	my.uum.repo.124882014-10-26T02:29:22Z http://repo.uum.edu.my/12488/ Learning anticipation through priming in spatio-temporal neural networks Yusoff, Nooraini Grüning, André QA76 Computer software In this paper, we propose a reward-based learning model inspired by the findings from a behavioural study and biologically realistic properties of spatio-temporal neural networks.The model simulates the cognitive priming effect in stimulus-stimulus-response association.Synaptic plasticity is dependent on a global reward signal that enhances the synaptic changes derived from spike-timing dependent plasticity (STDP) process.We show that by priming a network with a cue stimulus can facilitate the response to a later stimulus.The network can be trained to associate a stimulus pair (with an inter-stimulus interval) to a response, as well as to recognise the temporal sequence of the stimulus presentation. Springer Tingwen, Huang Zhigang, Zeng Chuangdong, Li Chi, Sing Leung 2012 Book Section PeerReviewed Yusoff, Nooraini and Grüning, André (2012) Learning anticipation through priming in spatio-temporal neural networks. In: Neural Information Processing. Lecture Notes in Computer Science, 7663 (7663). Springer, pp. 168-175. ISBN 978-3-642-34474-9 http://dx.doi.org/10.1007/978-3-642-34475-6_21 doi:10.1007/978-3-642-34475-6_21
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 Learning anticipation through priming in spatio-temporal neural networks
description	In this paper, we propose a reward-based learning model inspired by the findings from a behavioural study and biologically realistic properties of spatio-temporal neural networks.The model simulates the cognitive priming effect in stimulus-stimulus-response association.Synaptic plasticity is dependent on a global reward signal that enhances the synaptic changes derived from spike-timing dependent plasticity (STDP) process.We show that by priming a network with a cue stimulus can facilitate the response to a later stimulus.The network can be trained to associate a stimulus pair (with an inter-stimulus interval) to a response, as well as to recognise the temporal sequence of the stimulus presentation.
author2	Tingwen, Huang
author_facet	Tingwen, Huang Yusoff, Nooraini Grüning, André
format	Book Section
author	Yusoff, Nooraini Grüning, André
author_sort	Yusoff, Nooraini
title	Learning anticipation through priming in spatio-temporal neural networks
title_short	Learning anticipation through priming in spatio-temporal neural networks
title_full	Learning anticipation through priming in spatio-temporal neural networks
title_fullStr	Learning anticipation through priming in spatio-temporal neural networks
title_full_unstemmed	Learning anticipation through priming in spatio-temporal neural networks
title_sort	learning anticipation through priming in spatio-temporal neural networks
publisher	Springer
publishDate	2012
url	http://repo.uum.edu.my/12488/ http://dx.doi.org/10.1007/978-3-642-34475-6_21
_version_	1644280925445423104
score	13.145126

Learning anticipation through priming in spatio-temporal neural networks

Similar Items