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

Nikola Kasabov, Lei Zhou, Maryam Doborjeh, Zohreh Doborjeh, Jie Yang

Research output: Contribution to journalArticle

14 Citations (Scopus)
5 Downloads (Pure)

Abstract

This paper argues that, the third generation of neural networks-the spiking neural networks (SNNs), can be used to model dynamic, spatio-temporal, cognitive brain processes measured as functional magnetic resonance imaging (fMRI) data. This 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 3-D SNN reservoir; classification of cognitive states; and connectivity visualization and analysis for the purpose of understanding cognitive dynamics. The method is illustrated on two case studies of cognitive data modeling from a benchmark fMRI data set of seeing a picture versus reading a sentence.
Original languageEnglish
Pages (from-to)293-303
Number of pages11
JournalIEEE Transactions on Cognitive and Developmental Systems
Volume9
Issue number4
DOIs
Publication statusPublished - 7 Dec 2017

Keywords

  • brain functional connectivity
  • benchmark fMRI data set
  • cognitive data
  • cognitive dynamics
  • deep unsupervised learning
  • spike sequences
  • NeuCube
  • dynamic cogntive processes

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