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

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Oct 17, 2015

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.

Authors

  • Nikola Kasabov
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand. Electronic address: nkasabov@aut.ac.nz.
  • Nathan Matthew Scott
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand. Electronic address: nascott@aut.ac.nz.
  • Enmei Tu
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand.
  • Stefan Marks
    CoLab, Auckland University of Techology, Auckland, New Zealand.
  • Neelava Sengupta
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand.
  • Elisa Capecci
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand.
  • Muhaini Othman
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand; Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
  • Maryam Gholami Doborjeh
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand.
  • Norhanifah Murli
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand.
  • Reggio Hartono
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand.
  • Josafath Israel Espinosa-Ramos
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand; Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico.
  • Lei Zhou
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Fahad Bashir Alvi
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand.
  • Grace Wang
    Gambling & Addictions Research Centre, Auckland University of Technology, Auckland, New Zealand.
  • Denise Taylor
    Health & Rehabilitation Research Centre, Auckland University of Technology, Auckland, New Zealand.
  • Valery Feigin
    National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand.
  • Sergei Gulyaev
    Institute for Radio Astronomy & Space Research, Auckland University of Technology, Auckland, New Zealand.
  • Mahmoud Mahmoud
  • Zeng-Guang Hou
    State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China.
  • Jie Yang
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.

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