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Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications

Kasabov, Nikola and Scott, Nathan Matthew and Tu, Enmei and Marks, Stefan and Sengupta, Neelava and Capecci, Elisa and Othman, Muhaini and Doborjeh, Maryam Gholami and Murli, Norhanifah and Hartono, Reggio and Espinosa-Ramos, Josafath Israel and Zhou, Lei and Alvi, Fahad Bashir and Wang, Grace and Taylor, Denise and Feigin, Valery and Gulyaev, Sergei and Mahmoud, Mahmoud and Houi, Zeng-Guang and Yang, Jie (2016) Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Networks, 78. pp. 1-14. ISSN 08936080

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Abstract

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this is presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM.

Item Type: Article
Uncontrolled Keywords: Spatio/spectro temporal data; evolving connectionist systems; evolving spiking neural networks; computational neurogenetic systems; evolving spatio-temporal data machines; NeuCube
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 Aug 2018 03:24
Last Modified: 13 Aug 2018 03:24
URI: http://eprints.uthm.edu.my/id/eprint/8988
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