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Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications
Kasabov, Nikola1; Scott, Nathan Matthew1; Tu, Enmei1; Marks, Stefan2; Sengupta, Neelava1; Capecci, Elisa1; Othman, Muhaini1,3; Doborjeh, Maryam Gholami1; Murli, Norhanifah1; Hartono, Reggio1; Espinosa-Ramos, Josafath Israel1,6; Zhou, Lei1; Alvi, Fahad Bashir1; Wang, Grace4; Taylor, Denise5; Feigin, Valery7; Gulyaev, Sergei8; Mahmoud, Mahmoud8; Hou, Zeng-Guang9; Yang, Jie10
Source PublicationNEURAL NETWORKS
2016-06-01
Volume78Pages:1-14
SubtypeArticle
AbstractThe 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. (C) 2015 Elsevier Ltd. All rights reserved.
KeywordSpatio/spectro Temporal Data Evolving Connectionist Systems Evolving Spiking Neural Networks Computational Neurogenetic Systems Evolving Spatio-temporal Data Machines Neucube
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1016/j.neunet.2015.09.011
WOS KeywordSPIKING NEURAL-NETWORKS ; SYNAPTIC PLASTICITY ; FIRING RATES ; CLASSIFICATION ; ARCHITECTURE ; PREDICTION ; KNOWLEDGE ; PATTERNS ; STROKE ; SYSTEM
Indexed BySCI
Language英语
Funding OrganizationKnowledge Engineering and Discovery Research Institute (KEDRI)
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000376232800001
Citation statistics
Cited Times:33[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12223
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, Auckland, New Zealand
2.Auckland Univ Techol, CoLab, Auckland, New Zealand
3.Univ Tun Hussein Onn Malaysia, Johor Baharu, Malaysia
4.Auckland Univ Technol, Gambling & Addict Res Ctr, Auckland, New Zealand
5.Auckland Univ Technol, Hlth & Rehabil Res Ctr, Auckland, New Zealand
6.Inst Politecn Nacl, Ctr Invest Computac, Mexico City 07738, DF, Mexico
7.Auckland Univ Technol, Natl Inst Stroke & Appl Neurosci, Auckland, New Zealand
8.Auckland Univ Technol, Inst Radio Astron & Space Res, Auckland, New Zealand
9.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
10.Shanghai Jiao Tong Univ, Shanghai 200030, Peoples R China
Recommended Citation
GB/T 7714
Kasabov, Nikola,Scott, Nathan Matthew,Tu, Enmei,et al. Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications[J]. NEURAL NETWORKS,2016,78:1-14.
APA Kasabov, Nikola.,Scott, Nathan Matthew.,Tu, Enmei.,Marks, Stefan.,Sengupta, Neelava.,...&Yang, Jie.(2016).Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications.NEURAL NETWORKS,78,1-14.
MLA Kasabov, Nikola,et al."Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications".NEURAL NETWORKS 78(2016):1-14.
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