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

✍ Scribed by Nikola K. Kasabov

Publisher: Springer Berlin Heidelberg
Year: 2019
Tongue: English
Leaves: 742
Series: Springer Series on Bio- and Neurosystems 7
Edition: 1st ed.
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xxxiv
Front Matter ....Pages 1-1
Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI (Nikola K. Kasabov)....Pages 3-37
Artificial Neural Networks. Evolving Connectionist Systems (Nikola K. Kasabov)....Pages 39-83
Front Matter ....Pages 85-85
Deep Learning and Deep Knowledge Representation in the Human Brain (Nikola K. Kasabov)....Pages 87-123
Front Matter ....Pages 125-125
Methods of Spiking Neural Networks (Nikola K. Kasabov)....Pages 127-167
Evolving Spiking Neural Networks (Nikola K. Kasabov)....Pages 169-199
Brain-Inspired SNN for Deep Learning in Time-Space and Deep Knowledge Representation. NeuCube (Nikola K. Kasabov)....Pages 201-243
Evolutionary- and Quantum-Inspired Computation. Applications for SNN Optimisation (Nikola K. Kasabov)....Pages 245-287
Front Matter ....Pages 289-289
Deep Learning and Deep Knowledge Representation of EEG Data (Nikola K. Kasabov)....Pages 291-337
Brain Disease Diagnosis and Prognosis Based on EEG Data (Nikola K. Kasabov)....Pages 339-359
Deep Learning and Deep Knowledge Representation of fMRI Data (Nikola K. Kasabov)....Pages 361-395
Integrating Time-Space and Orientation. A Case Study on fMRI + DTI Brain Data (Nikola K. Kasabov)....Pages 397-427
Front Matter ....Pages 429-429
Audio- and Visual Information Processing in the Brain and Its Modelling with Evolving SNN (Nikola K. Kasabov)....Pages 431-456
Deep Learning and Modelling of Audio-, Visual-, and Multimodal Audio-Visual Data in Brain-Inspired SNN (Nikola K. Kasabov)....Pages 457-477
Brain-Computer Interfaces Using Brain-Inspired SNN (Nikola K. Kasabov)....Pages 479-502
Front Matter ....Pages 503-503
Computational Modelling and Pattern Recognition in Bioinformatics (Nikola K. Kasabov)....Pages 505-543
Computational Neuro-genetic Modelling (Nikola K. Kasabov)....Pages 545-561
A Computational Framework for Personalised Modelling. Applications in Bioinformatics (Nikola K. Kasabov)....Pages 563-591
Personalised Modelling for Integrated Static and Dynamic Data. Applications in Neuroinformatics (Nikola K. Kasabov)....Pages 593-615
Front Matter ....Pages 617-617
Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment (Nikola K. Kasabov)....Pages 619-658
Front Matter ....Pages 659-659
From von Neumann Machines to Neuromorphic Platforms (Nikola K. Kasabov)....Pages 661-677
From Claude Shannon’s Information Entropy to Spike-Time Data Compression Theory (Nikola K. Kasabov)....Pages 679-699
From Brain-Inspired AI to a Symbiosis of Human Intelligence and Artificial Intelligence (Nikola K. Kasabov)....Pages 701-714
Back Matter ....Pages 715-738

✦ Subjects

Engineering; Computational Intelligence; Computational Biology/Bioinformatics; Neurosciences; Robotics and Automation; Pattern Recognition

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