HapticV1, PubMed, Corpus, bibRecord, 001412

2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism.

Identifieur interne : 001412 ( PubMed/Corpus ); précédent : 001411; suivant : 001413

2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism.

Auteurs : Qingxiang Wu ; Thomas Martin Mcginnity ; Liam Maguire ; Ammar Belatreche ; Brendan Glackin

Source :

Neural networks : the official journal of the International Neural Network Society [ 0893-6080 ] ; 2008.

RBID : pubmed:18706787

English descriptors

KwdEn :
- Algorithms, Artificial Intelligence, Electrophysiology, Models, Neurological, Models, Statistical, Neural Networks (Computer), Neuronal Plasticity (physiology).
MESH :
- physiology : Neuronal Plasticity.
- Algorithms, Artificial Intelligence, Electrophysiology, Models, Neurological, Models, Statistical, Neural Networks (Computer).

Abstract

In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.

DOI: 10.1016/j.neunet.2008.05.014
PubMed: 18706787

Links to Exploration step

pubmed:18706787

Le document en format XML

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<author><name sortKey="Wu, Qingxiang" sort="Wu, Qingxiang" uniqKey="Wu Q" first="Qingxiang" last="Wu">Qingxiang Wu</name>
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<author><name sortKey="Mcginnity, Thomas Martin" sort="Mcginnity, Thomas Martin" uniqKey="Mcginnity T" first="Thomas Martin" last="Mcginnity">Thomas Martin Mcginnity</name>
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<author><name sortKey="Maguire, Liam" sort="Maguire, Liam" uniqKey="Maguire L" first="Liam" last="Maguire">Liam Maguire</name>
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<author><name sortKey="Belatreche, Ammar" sort="Belatreche, Ammar" uniqKey="Belatreche A" first="Ammar" last="Belatreche">Ammar Belatreche</name>
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<author><name sortKey="Glackin, Brendan" sort="Glackin, Brendan" uniqKey="Glackin B" first="Brendan" last="Glackin">Brendan Glackin</name>
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<front><div type="abstract" xml:lang="en">In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.</div>
</front>
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<Abstract><AbstractText>In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.</AbstractText>
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2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism.

2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

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