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

  • Nikola K. Kasabov

Part of the Springer Series on Bio- and Neurosystems book series (SSBN, volume 7)

Table of contents

  1. Front Matter
    Pages i-xxxiv
  2. Time-Space and AI. Artificial Neural Networks

  3. The Human Brain

    1. Front Matter
      Pages 85-85
  4. Spiking Neural Networks

  5. Deep Learning and Deep Knowledge Representation of Brain Data

  6. SNN for Audio-Visual Data and Brain-Computer Interfaces

  7. SNN in Bio- and Neuroinformatics

  8. Deep in Time-Space Learning and Deep Knowledge Representation of Multisensory Streaming Data

  9. Future Development in BI-SNN and BI-AI

  10. Back Matter
    Pages 715-738

About this book


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. The 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.


Deep knowledge representation Integration of human intelligence and artificial intelligence Deep learning of Time-Space data Spike-time learning Evolving spatio-temporal processes Interactions in Time-Space Evolving connectionist systems (ECOS) Transductive inference methods Knowledge-based ANN Evolving Fuzzy Neural Networks Supervised learning in ANN Convolutional ANN Training multilayer perceptron Evolving self-organizing maps Takagi-Sugeno fuzzy inference Neural Representation of Information Time-space in the brain Spike-Driven Synaptic Plasticity Reservoir architectures Quantum-inspired computation

Authors and affiliations

  • Nikola K. Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research Institute (KEDRI)Auckland University of TechnologyAucklandNew Zealand

Bibliographic information