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

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications
	Kasabov, Nikola 1; Scott, Nathan Matthew 1; Tu, Enmei 1; Marks, Stefan 2; Sengupta, Neelava 1; Capecci, Elisa 1; Othman, Muhaini 1,3; Doborjeh, Maryam Gholami 1; Murli, Norhanifah 1; Hartono, Reggio 1
刊名	NEURAL NETWORKS
	2016-06-01
卷号	78 页码:1-14
关键词	Spatio/spectro temporal data Evolving connectionist systems Evolving spiking neural networks Computational neurogenetic systems Evolving spatio-temporal data machines NeuCube
英文摘要	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. (C) 2015 Elsevier Ltd. All rights reserved.
WOS标题词	Science & Technology ; Technology ; Life Sciences & Biomedicine
类目[WOS]	Computer Science, Artificial Intelligence ; Neurosciences
研究领域[WOS]	Computer Science ; Neurosciences & Neurology
关键词[WOS]	SPIKING NEURAL-NETWORKS ; SYNAPTIC PLASTICITY ; FIRING RATES ; CLASSIFICATION ; ARCHITECTURE ; PREDICTION ; KNOWLEDGE ; PATTERNS ; STROKE ; SYSTEM
收录类别	SCI
语种	英语
WOS记录号	WOS:000376232800001
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/12223]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
作者单位	1.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
推荐引用方式 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.