Spiking neural networks for deep learning and knowledge representation: Editorial

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

One of the biggest challenges that scientists are facing is understanding
complex dynamic processes. Such processes can be
measured as multidimensional and multimodal data in different
spatial and temporal dimensions, at different scales of time and
space. A fundamental question is: Can essential patterns that
characterise the dynamics (the changes) of these processes be
revealed through machine learning from data and can these patterns
be represented as knowledge representation, comprehensible
by humans? If such patterns can be learned and interpreted as
new knowledge, then our ability to explain phenomena in nature,
understand mechanisms of human cognition, and predict future
events will be significantly improved.
Original languageEnglish
Pages (from-to)341-342
Number of pages2
JournalNeural Networks
Volume119
Early online date23 Aug 2019
DOIs
Publication statusE-pub ahead of print - 23 Aug 2019

Keywords

  • spiking neural networks
  • knowledge representaion
  • machine learning

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