Transfer Learning of Fuzzy Spatio-Temporal Rules in a Brain-Inspired Spiking Neural Network Architecture: A Case Study on Spatio-Temporal Brain Data

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

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
Pages (from-to)4542-4552
Number of pages11
JournalIEEE Transactions on Fuzzy Systems
Volume31
Issue number12
DOIs
Publication statusPublished - Dec 1 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1993-2012 IEEE.

ASJC Scopus Subject Areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

Keywords

  • EEG data
  • explainable AI
  • fuzzy spatio-temporal rules
  • neucube
  • spatio-temporal learning
  • spiking neural networks
  • transfer learning

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