Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture

Zohreh Doborjeh, Nikola Kasabov, Maryam Doborjeh, Alexander Sumich

Research output: Contribution to journalArticle

12 Citations (Scopus)

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 spatiotemporal 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.
Original languageEnglish
Article number8912
Pages (from-to)1-13
Number of pages14
JournalScientific Reports
Volume8
DOIs
Publication statusPublished - 11 Jun 2018

Keywords

  • deep learning
  • spiking neural netqworks
  • per-perceptual brain modelling m
  • neuromarketing

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