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dc.contributor.authorGholami Doborjeh, Zohrehen_NZ
dc.contributor.authorKasabov, Nikolaen_NZ
dc.contributor.authorGholami Doborjeh, Maryamen_NZ
dc.contributor.authorSumich, Aen_NZ
dc.date.accessioned2018-06-18T23:50:48Z
dc.date.available2018-06-18T23:50:48Z
dc.date.copyright2018-06-11en_NZ
dc.identifier.citationScientific Reports, Volume 8, Article number: 8912 (2018)
dc.identifier.urihttp://hdl.handle.net/10292/11599
dc.description.abstractFamiliarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveals the complexity of peri-perceptual processes of familiarity. The method is applied to data from 20 participants viewing familiar and unfamiliar logos. The results support the potential of SNN models as novel tools in the exploration of peri-perceptual mechanisms that respond differentially to familiar and unfamiliar stimuli. Specifically, the activation pattern of the time-locked response identified by the proposed SNN model at approximately 200 milliseconds post-stimulus suggests greater connectivity and more widespread dynamic spatio-temporal patterns for familiar than unfamiliar logos. The proposed SNN approach can be applied to study other peri-perceptual or perceptual brain processes in cognitive and computational neuroscience.
dc.publisherMacmillan Publishers Limited
dc.relation.urihttps://www.nature.com/articles/s41598-018-27169-8en_NZ
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.subjectComputational models; Computational neuroscience; Electroencephalography – EEG; Human behaviour; Machine learning
dc.titleModelling peri-perceptual brain processes in a deep learning spiking neural network architectureen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1038/s41598-018-27169-8en_NZ
aut.relation.volume8en_NZ
pubs.elements-id338955
aut.relation.journalNature Scientific Reporten_NZ


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