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

Nikola Kasabov, Lei Zhou, Maryam Gholami Doborjeh, Zohreh Gholami Doborjeh, Jie Yang
2017 IEEE Transactions on Cognitive and Developmental Systems  
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;
more » ... 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.
doi:10.1109/tcds.2016.2636291 fatcat:d65yarmtwvcltkktgawoxm7kvy