Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture

Gholami Doborjeh, Z, Kasabov, N, Gholami Doborjeh, M and Sumich, A ORCID: https://orcid.org/0000-0003-4333-8442, 2018. Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture. Scientific Reports, 8 (1): 8912. ISSN 2045-2322

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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.

Item Type:	Journal article
Publication Title:	Scientific Reports
Creators:	Gholami Doborjeh, Z., Kasabov, N., Gholami Doborjeh, M. and Sumich, A.
Publisher:	Nature Publishing Group
Date:	11 June 2018
Volume:	8
Number:	1
ISSN:	2045-2322
Identifiers:	Number Type 10.1038/s41598-018-27169-8 DOI
Divisions:	Schools > School of Social Sciences
Record created by:	Linda Sullivan
Date Added:	13 Jun 2018 07:57
Last Modified:	31 Jul 2018 11:32
URI:	https://irep.ntu.ac.uk/id/eprint/33847

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