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

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• 作者：Nikola Kasabov^a ; ^{nkasabov@aut.ac.nz" class="auth_mail" title="E-mail the corresponding author} ; Nathan Matthew Scott^a ; ^{nascott@aut.ac.nz" class="auth_mail" title="E-mail the corresponding author} ; Enmei Tu^a ; Stefan Marks^b ; Neelava Sengupta^a ; Elisa Capecci^a ; Muhaini Othman^a ; ^c ; Maryam Gholami Doborjeh^a ; Norhanifah Murli^a ; Reggio Hartono^a ; Josafath Israel Espinosa-Ramos^a ; ^f ; Lei Zhou^a ; ^j ; Fahad Bashir Alvi^a ; Grace Wang^d ; Denise Taylor^e ; Valery Feigin^g ; Sergei Gulyaev^h ; Mahmoud Mahmoud^h ; Zeng-Guang Houⁱ ; Jie Yang^j
• 关键词：Spatio/spectro temporal data ; Evolving connectionist systems ; Evolving spiking neural networks ; Computational neurogenetic systems ; Evolving spatio-temporal data machines ; NeuCube
• 刊名：Neural Networks
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：78
• 期：Complete
• 页码：1-14
• 全文大小：3327 K

文摘

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.