Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture
aut.relation.journal | Nature Scientific Report | en_NZ |
aut.relation.volume | 8 | en_NZ |
aut.researcher | Gholami Doborjeh, Zohreh | |
dc.contributor.author | Gholami Doborjeh, Zohreh | en_NZ |
dc.contributor.author | Kasabov, Nikola | en_NZ |
dc.contributor.author | Gholami Doborjeh, Maryam | en_NZ |
dc.contributor.author | Sumich, A | en_NZ |
dc.date.accessioned | 2018-06-18T23:50:48Z | |
dc.date.available | 2018-06-18T23:50:48Z | |
dc.date.copyright | 2018-06-11 | en_NZ |
dc.date.issued | 2018-06-11 | en_NZ |
dc.description.abstract | Familiarity 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.identifier.citation | Scientific Reports, Volume 8, Article number: 8912 (2018) | |
dc.identifier.doi | 10.1038/s41598-018-27169-8 | en_NZ |
dc.identifier.uri | https://hdl.handle.net/10292/11599 | |
dc.publisher | Macmillan Publishers Limited | |
dc.relation.uri | https://www.nature.com/articles/s41598-018-27169-8 | en_NZ |
dc.rights | Open 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.rights.accessrights | OpenAccess | en_NZ |
dc.subject | Computational models; Computational neuroscience; Electroencephalography – EEG; Human behaviour; Machine learning | |
dc.title | Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture | en_NZ |
dc.type | Journal Article | |
pubs.elements-id | 338955 | |
pubs.organisational-data | /AUT | |
pubs.organisational-data | /AUT/Design & Creative Technologies | |
pubs.organisational-data | /AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences | |
pubs.organisational-data | /AUT/PBRF | |
pubs.organisational-data | /AUT/PBRF/PBRF Design and Creative Technologies | |
pubs.organisational-data | /AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS |
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