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

• Published in: IEEE transactions on cognitive and developmental systems 2017-12, Vol.9 (4), p.293-303
• Main Authors: Kasabov, Nikola, Lei Zhou, Doborjeh, Maryam Gholami, Doborjeh, Zohreh Gholami, Jie Yang
• Format: Article
• Language: English
• Subjects: Brain functional connectivity; Brain modeling; classification; Classification algorithms; Data models; deep learning in spiking neural networks; Encoding; Functional magnetic resonance imaging; functional magnetic resonance imaging (fMRI) data; Heuristic algorithms; NeuCube; Neural networks; neuromorphic cognitive systems; Neuromorphics; Neurons; perceptual dynamics; spiking neural networks (SNNs)
• ISSN: 2379-8920; 2379-8939
• DOI: 10.1109/TCDS.2016.2636291

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.