Abstract
Spatio/Spector-Temporal Data (SSTD) analyzing is a challenging task, as temporal features may manifest complex interactions that may also change over time. Making use of suitable models that can capture the “hidden” interactions and interrelationship among multivariate data, is vital in SSTD investigation. This chapter describes a number of prominent applications built using the Kasabov’s NeuCube-based Spiking Neural Network (SNN) architecture for mapping, learning, visualization, classification/regression and better understanding and interpretation of SSTD.
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The research is supported by the Knowledge Engineering and Discovery Research Institute of the Auckland University of Technology (www.kedri.aut.ac.nz), New Zealand.
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Gholami Doborjeh, M. et al. (2018). From von Neumann Architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications. In: Sgurev, V., Jotsov, V., Kacprzyk, J. (eds) Practical Issues of Intelligent Innovations. Studies in Systems, Decision and Control, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-78437-3_2
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