Transfer Learning of Fuzzy Spatio-Temporal Rules in the NeuCube Brain-Inspired Spiking Neural Network: A Case Study on EEG Spatio-temporal Data: A Case Study on Spatio-Temporal Brain Data

Nikola Kasabov, Yongyao Tan, Maryam Doborjeh, Enmei Tu, Jie Yang, Wilson Goh, Jimmy Lee

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
159 Downloads (Pure)

Abstract

The article demonstrates for the first time that a brain-inspired spiking neural network (SNN) architecture can be used not only to learn spatio-temporal data, but also to extract fuzzy spatio-temporal rules from such data and to update these rules incrementally in a transfer learning mode. We propose a method, where a SNN model learns incrementally new time-space data related to new classes/tasks/categories, always utilizing some previously learned knowledge, and presents the evolved knowledge as fuzzy spatio-temporal rules. Similarly, to how the brain manifests transfer learning, these SNN models do not need to be restricted in number of layers and neurons in each layer as they adopt self-organizing learning principles. The continuously evolved fuzzy rules from spatio-temporal data are interpretable for a better understanding of the processes that generate the data. The proposed method is based on a brain-inspired SNN architecture NeuCube, which is structured according to a brain three-dimensional structural template. It is illustrated on tasks of incremental and transfer learning and knowledge transfer using spatio-temporal data measuring brain activity, when subjects are performing tasks in space and time. The method is a general one and opens the field to create new types of adaptable and explainable spatio-temporal learning systems across domain areas.

Original languageEnglish
Article numberTFS-2022-1366.R2
Pages (from-to)4542-4552
Number of pages12
JournalIEEE Transactions of Fuzzy Systems
Volume31
Issue number12
Early online date7 Jul 2023
DOIs
Publication statusPublished (in print/issue) - 1 Dec 2023

Bibliographical note

Publisher Copyright:
© 1993-2012 IEEE.

Keywords

  • fuzzy spatio-temporal rules
  • NeuCube
  • spatio-temporal learning
  • transfer learning
  • EEG data
  • spiking neural networks
  • explainable AI
  • Task analysis
  • Neurons
  • Three-dimensional displays
  • Brain modelling
  • Data models
  • Transfer learning
  • Electroencephalography
  • neucube
  • Brain modeling

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