From von Neumann architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications

Doborjeh, Maryam and Doborjeh, Zohreh and Gollahalli, Akshay Raj and Kumarasinghe, Kaushalya and Breen, Vivienne and Sengupta, Neelava and Ramos, Josafath Israel Espinosa and Hartono, Reggio and Capecci, Elisa and Kawano, Hideaki and Othman, Muhaini and Lei, Zhou and Jie, Yang and Bose, Pritam and Chenjie, Ge (2018) From von Neumann architecture and Atanasoff’s ABC to Neuromorphic Computation and Kasabov’s NeuCube. Part II: Applications. Practical Issues of Intelligent Innovations. pp. 17-36. ISSN 2198-4182

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Official URL: http://dx.doi.org/10.1007/978-3-319-78437-3_2

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.

Item Type:	Article
Uncontrolled Keywords:	NIL
Subjects:	N Fine Arts > NA Architecture T Technology > T Technology (General)
Divisions:	Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User:	UiTM Student Praktikal
Date Deposited:	09 Jan 2022 04:06
Last Modified:	09 Jan 2022 04:06
URI:	http://eprints.uthm.edu.my/id/eprint/5343

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