Design methodology and selected applications of evolving spatio- temporal data machines in the NeuCube neuromorphic framework

Nikola Kasabov, Nathan Scott, Enmei Tu, Stefan Marks, Neelava Sengupta, Elisa Capecci, Muhaini Othman, Maryam Doborjeh, Norhanifah Murli, Reggio Hartono, Josafath Espinosa-Ramos , Lei Zhou, Fahad Alvi, Grace Wang, Denise Taylor, Valery Feigin, Sergei Gulyaev, Mahmoud Mahmoud, Zeng-Guang Hou, Jie Yang

Research output: Contribution to journalArticle

59 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalNeural Networks
Volume78
Issue number(2016)
Early online date17 Oct 2015
DOIs
Publication statusPublished - 30 Jun 2016

Keywords

  • Spatio/spectro temporal data
  • Evovling connectionist systems
  • Evolving spiking neural networks
  • Computaionla neurogenetic systems
  • Evolving spatio-temporal data machines
  • NeuCube

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