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

1. Front Matter

   Pages i-xxxiv

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2. Time-Space and AI. Artificial Neural Networks

   1. Front Matter

      Pages 1-1

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   2. Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI

      • Nikola K. Kasabov

      Pages 3-37

   3. Artificial Neural Networks. Evolving Connectionist Systems

      • Nikola K. Kasabov

      Pages 39-83

3. The Human Brain

   1. Front Matter

      Pages 85-85

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   2. Deep Learning and Deep Knowledge Representation in the Human Brain

      • Nikola K. Kasabov

      Pages 87-123

4. Spiking Neural Networks

   1. Front Matter

      Pages 125-125

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   2. Methods of Spiking Neural Networks

      • Nikola K. Kasabov

      Pages 127-167

   3. Evolving Spiking Neural Networks

      • Nikola K. Kasabov

      Pages 169-199

   4. Brain-Inspired SNN for Deep Learning in Time-Space and Deep Knowledge Representation. NeuCube

      • Nikola K. Kasabov

      Pages 201-243

   5. Evolutionary- and Quantum-Inspired Computation. Applications for SNN Optimisation

      • Nikola K. Kasabov

      Pages 245-287

5. Deep Learning and Deep Knowledge Representation of Brain Data

   1. Front Matter

      Pages 289-289

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   2. Deep Learning and Deep Knowledge Representation of EEG Data

      • Nikola K. Kasabov

      Pages 291-337

   3. Brain Disease Diagnosis and Prognosis Based on EEG Data

      • Nikola K. Kasabov

      Pages 339-359

   4. Deep Learning and Deep Knowledge Representation of fMRI Data

      • Nikola K. Kasabov

      Pages 361-395

   5. Integrating Time-Space and Orientation. A Case Study on fMRI + DTI Brain Data

      • Nikola K. Kasabov

      Pages 397-427

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

   1. Front Matter

      Pages 429-429

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   2. Audio- and Visual Information Processing in the Brain and Its Modelling with Evolving SNN

      • Nikola K. Kasabov

      Pages 431-456

   3. Deep Learning and Modelling of Audio-, Visual-, and Multimodal Audio-Visual Data in Brain-Inspired SNN

      • Nikola K. Kasabov

      Pages 457-477

   4. Brain-Computer Interfaces Using Brain-Inspired SNN

      • Nikola K. Kasabov

      Pages 479-502

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

• Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand

  Nikola K. Kasabov

Nikola Kirilov Kasabov is Professor of neural networks and knowledge engineering and Director of the Knowledge Engineering and Discovery Research Institute (KEDRI) at the Auckland University of Technology (AUT), New Zealand. Born in  Bulgaria, he has worked previously at the TU Sofia, University of Essex and University of Otago. He is fellow of IEEE, Fellow of the Royal Society (Academy) of New Zealand (RSNZ), Distinguished Fellow of the Royal Academy of Engineering UK and Visiting Professor at several universities, including: Shanghai Jia-Tong University; ETH and University of Zurich; RGU Scotland UK; University of Trento; University of Kaiserslautern; Universities of Twente and Maastricht. Prof Kasabov originated methods and systems for intelligent information processing, including: evolving connectionist systems, hybrid neuro-fuzzy systems, evolving- and brain –inspired spiking neural network architectures, quantum-inspired methods, methods for personalised modelling in bio andneuroinformatics, published in more than 600 works. He is Past President of the International Neural Network Society (INNS) and the current President of the Asia-Pacific Neural Network Society (APNNS). Prof Kasabov has received the INNS Ada Lovelace and Gabor Awards, APNNS Outstanding Achievements Award, RSNZ Medal, AUT Medal, Honourable Fellowship of the  Bulgarian and the Greek Computer Societies, Pavlikeni Honourable Citizenship and other awards. He has been the editor of the Springer Handbook of Bio-/Neuro-informatics published by Springer in 2014 and of the related book series Springer Series on Bio- and Neurosystems.

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