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
2016-06-01
发表期刊NEURAL NETWORKS
卷号78页码:1-14
文章类型Article
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.
关键词Spatio/spectro Temporal Data Evolving Connectionist Systems Evolving Spiking Neural Networks Computational Neurogenetic Systems Evolving Spatio-temporal Data Machines Neucube
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1016/j.neunet.2015.09.011
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
项目资助者Knowledge Engineering and Discovery Research Institute (KEDRI)
WOS研究方向Computer Science ; Neurosciences & Neurology
WOS类目Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号WOS:000376232800001
引用统计
被引频次:22[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12223
专题复杂系统管理与控制国家重点实验室_先进机器人
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.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Kasabov, Nikola]的文章
[Scott, Nathan Matthew]的文章
[Tu, Enmei]的文章
百度学术
百度学术中相似的文章
[Kasabov, Nikola]的文章
[Scott, Nathan Matthew]的文章
[Tu, Enmei]的文章
必应学术
必应学术中相似的文章
[Kasabov, Nikola]的文章
[Scott, Nathan Matthew]的文章
[Tu, Enmei]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。