Textbook : Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, 1st Edition (PDF Download)

A Digital Textbook : Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, 1st Edition
By Nikola K. Kasabov
ISBN 10: 3662577135 | ISBN 13:9783662577134
Publisher: Springer Berlin Heidelberg
Other ISBNs: 9783662577134, 9783662577158
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Part I. Time-Space and AI. Artificial Neural Networks
1. Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI
2. Artificial Neural Networks. Evolving Connectionist Systems
Part II. The Human Brain
3. Deep Learning and Deep Knowledge Representation in the Human Brain
Part III. Spiking Neural Networks
4. Methods of Spiking Neural Networks
5. Evolving Spiking Neural Networks
6. Brain-Inspired SNN for Deep Learning in Time-Space and Deep Knowledge Representation. NeuCube
7. Evolutionary- and Quantum-Inspired Computation. Applications for SNN Optimisation
Part IV. Deep Learning and Deep Knowledge Representation of Brain Data
8. Deep Learning and Deep Knowledge Representation of EEG Data
9. Brain Disease Diagnosis and Prognosis Based on EEG Data
10. Deep Learning and Deep Knowledge Representation of fMRI Data
11. Integrating Time-Space and Orientation. A Case Study on fMRI + DTI Brain Data
Part V. SNN for Audio-Visual Data and Brain-Computer Interfaces
12. Audio- and Visual Information Processing in the Brain and Its Modelling with Evolving SNN
13. Deep Learning and Modelling of Audio-, Visual-, and Multimodal Audio-Visual Data in Brain-Inspired SNN
14. Brain-Computer Interfaces Using Brain-Inspired SNN
Part VI. SNN in Bio- and Neuroinformatics
15. Computational Modelling and Pattern Recognition in Bioinformatics
16. Computational Neuro-genetic Modelling
17. A Computational Framework for Personalised Modelling. Applications in Bioinformatics
18. Personalised Modelling for Integrated Static and Dynamic Data. Applications in Neuroinformatics
Part VII. Deep in Time-Space Learning and Deep Knowledge Representation of Multisensory Streaming Data
19. Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment
Part VIII. Future Development in BI-SNN and BI-AI
20. From von Neumann Machines to Neuromorphic Platforms
21. From Claude Shannon’s Information Entropy to Spike-Time Data Compression Theory
22. From Brain-Inspired AI to a Symbiosis of Human Intelligence and Artificial Intelligence

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