Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date: Sep 20, 2019

Abstract

OBJECTIVE: This paper argues that Brain-Inspired Spiking Neural Network (BI-SNN) architectures can learn and reveal deep in time-space functional and structural patterns from spatio-temporal data. These patterns can be represented as deep knowledge, in a partial case in the form of deep spatio-temporal rules. This is a promising direction for building new types of Brain-Computer Interfaces called Brain-Inspired Brain-Computer Interfaces (BI-BCI). A theoretical framework and its experimental validation on deep knowledge extraction and representation using SNN are presented.

Authors

  • Kaushalya Kumarasinghe
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand; Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand. Electronic address: kaushalya.kumarasinghe@aut.ac.nz.
  • Nikola Kasabov
    Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand. Electronic address: nkasabov@aut.ac.nz.
  • Denise Taylor
    Health & Rehabilitation Research Centre, Auckland University of Technology, Auckland, New Zealand.

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