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

Article

Kasabov, Nikola, Scott, Nathan Matthew, Tu, Enmei, Marks, Stefan, Sengupta, Neelava, Capecci, Elisa, Othman, Muhaini, Doborjeh, Maryam Gholami, Murli, Norhanifah, Hartono, Reggio, Espinosa-Ramos, Josafath Israel, Zhou, Lei, Alvi, Fahad Bashir, Wang, Grace, Taylor, Denise, Feigin, Valery, Gulyaev, Sergei, Mahmoud, Mahmoud, Hou, 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. https://doi.org/10.1016/j.neunet.2015.09.011
Article Title

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

ERA Journal ID18090
Article CategoryArticle
AuthorsKasabov, Nikola (Author), Scott, Nathan Matthew (Author), Tu, Enmei (Author), Marks, Stefan (Author), Sengupta, Neelava (Author), Capecci, Elisa (Author), Othman, Muhaini (Author), Doborjeh, Maryam Gholami (Author), Murli, Norhanifah (Author), Hartono, Reggio (Author), Espinosa-Ramos, Josafath Israel (Author), Zhou, Lei (Author), Alvi, Fahad Bashir (Author), Wang, Grace (Author), Taylor, Denise (Author), Feigin, Valery (Author), Gulyaev, Sergei (Author), Mahmoud, Mahmoud (Author), Hou, Zeng-Guang (Author) and Yang, Jie (Author)
Journal TitleNeural Networks
Journal Citation78, pp. 1-14
Number of Pages14
Year2016
Place of PublicationUnited Kingdom
ISSN0893-6080
1879-2782
Digital Object Identifier (DOI)https://doi.org/10.1016/j.neunet.2015.09.011
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0893608015001860
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.

KeywordsComputational neurogenetic systems; Evolving connectionist systems; Evolving spatio-temporal data machines; Evolving spiking neural networks; NeuCube; Spatio/spectro temporal data
ANZSRC Field of Research 2020520499. Cognitive and computational psychology not elsewhere classified
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Byline AffiliationsAuckland University of Technology, New Zealand
Chinese Academy of Sciences, China
Shanghai Jiao Tong University, China
Institution of OriginUniversity of Southern Queensland
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