Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer

Research output: Book/ReportBook

Abstract

Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. This monograph book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.
Original languageEnglish
Place of PublicationBerlin
Number of pages738
Volume1
Edition1
ISBN (Electronic)978-3-662-57715-8
DOIs
Publication statusPublished - 12 Aug 2018

Publication series

NameSpringer Series on Bio-and Neurosystems
PublisherSpringer-Nature
Volume7

Keywords

  • spatio-temporal data
  • evovlving connectionist systems
  • spiking neural networks
  • brauin data modelling
  • braion-computer interfaces
  • neuroinformatics
  • bioinformatics
  • personalised modelling
  • quantum inspired evolutionary optimisation

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