New Algorithms for Encoding, Learning and Classification of fMRI Data in a Dpiking Neural Network Architecture: A Case on Modelling and Understanding of Dynamic Cognitive

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aut.relation.journalIEEE Transactions on Cognitive and Developmental Systemsen_NZ
aut.relation.volume99en_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.contributor.authorZhou, Len_NZ
dc.contributor.authorDoborjeh, Men_NZ
dc.contributor.authorGholami, Zen_NZ
dc.contributor.authorJie Yangen_NZ
dc.date.accessioned2017-07-24T23:02:48Z
dc.date.available2017-07-24T23:02:48Z
dc.date.copyright2016-12-07en_NZ
dc.date.issued2016-12-07en_NZ
dc.description.abstractThe paper argues that, the third generation of neural networks – the spiking neural networks (SNN), can be used to model dynamic, spatio-temporal, cognitive brain processes measured as functional magnetic resonance imaging (fMRI) data. The paper proposes a novel method based on the NeuCube SNN architecture for which the following new algorithms are introduced: fMRI data encoding into spike sequences; deep unsupervised learning of fMRI data in a 3D SNN reservoir; classification of cognitive states; connectivity visualization and analysis for the purpose of understanding cognitive dynamics. The method is illustrated on two case studies of cognitive data modelling from a benchmark fMRI data set of seeing a picture versus reading a sentence.en_NZ
dc.identifier.citationIEEE Transactions on Cognitive and Developmental Systems , vol.PP, no.99, pp.1-1
dc.identifier.doi10.1109/TCDS.2016.2636291en_NZ
dc.identifier.issn2379-8920en_NZ
dc.identifier.issn2379-8939en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/10674
dc.publisherIEEEen_NZ
dc.relation.urihttp://ieeexplore.ieee.org/document/7776755/en_NZ
dc.rightsCopyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectNeuromorphic cognitive systemsen_NZ
dc.subjectSpiking neural networksen_NZ
dc.subjectfMRI dataen_NZ
dc.subjectNeuCubeen_NZ
dc.subjectDeep learning in spiking neural networksen_NZ
dc.subjectBrain functional connectivityen_NZ
dc.subjectClassificationen_NZ
dc.titleNew Algorithms for Encoding, Learning and Classification of fMRI Data in a Dpiking Neural Network Architecture: A Case on Modelling and Understanding of Dynamic Cognitiveen_NZ
dc.typeJournal Article
pubs.elements-id217137
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences
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