Kasabov, NZhou, LDoborjeh, MGholami, ZJie Yang2017-07-242017-07-242016-12-072016-12-07IEEE Transactions on Cognitive and Developmental Systems , vol.PP, no.99, pp.1-12379-89202379-8939https://hdl.handle.net/10292/10674The paper argues that, the third generation of neural networks – the spiking neural networks (SNN), can be used to model dynamic, spatio-temporal, cognitive brain processes measured as functional magnetic resonance imaging (fMRI) data. The 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 3D SNN reservoir; classification of cognitive states; connectivity visualization and analysis for the purpose of understanding cognitive dynamics. The method is illustrated on two case studies of cognitive data modelling from a benchmark fMRI data set of seeing a picture versus reading a sentence.Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Neuromorphic cognitive systemsSpiking neural networksfMRI dataNeuCubeDeep learning in spiking neural networksBrain functional connectivityClassificationJournal ArticleOpenAccess10.1109/TCDS.2016.2636291