The paper argues that, the third generation of neural networksthe 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;

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... 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. Index Terms-Spiking neural networks, perceptual dynamics, fMRI data, NeuCube, deep learning in spiking neural networks, brain functional connectivity, classification, neuromorphic cognitive systems.