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

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Table of Contents

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    Book Overview
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    Chapter 1 Evolving Processes in Time - Space . Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI
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    Chapter 2 Artificial Neural Networks. Evolving Connectionist Systems
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    Chapter 3 Deep Learning and Deep Knowledge Representation in the Human Brain
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    Chapter 4 Methods of Spiking Neural Networks
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    Chapter 5 Evolving Spiking Neural Networks
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    Chapter 6 Brain-Inspired SNN for Deep Learning in Time-Space and Deep Knowledge Representation. NeuCube
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    Chapter 7 Evolutionary- and Quantum-Inspired Computation. Applications for SNN Optimisation
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    Chapter 8 Deep Learning and Deep Knowledge Representation of EEG Data
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    Chapter 9 Brain Disease Diagnosis and Prognosis Based on EEG Data
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    Chapter 10 Deep Learning and Deep Knowledge Representation of fMRI Data
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    Chapter 11 Integrating Time-Space and Orientation. A Case Study on fMRI + DTI Brain Data
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    Chapter 12 Audio- and Visual Information Processing in the Brain and Its Modelling with Evolving SNN
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    Chapter 13 Deep Learning and Modelling of Audio-, Visual-, and Multimodal Audio-Visual Data in Brain-Inspired SNN
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    Chapter 14 Brain-Computer Interfaces Using Brain-Inspired SNN
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    Chapter 15 Computational Modelling and Pattern Recognition in Bioinformatics
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    Chapter 16 Computational Neuro-genetic Modelling
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    Chapter 17 A Computational Framework for Personalised Modelling. Applications in Bioinformatics
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    Chapter 18 Personalised Modelling for Integrated Static and Dynamic Data. Applications in Neuroinformatics
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    Chapter 19 Deep Learning of Multisensory Streaming Data for Predictive Modelling with Applications in Finance, Ecology, Transport and Environment
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    Chapter 20 From von Neumann Machines to Neuromorphic Platforms
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    Chapter 21 From Claude Shannon’s Information Entropy to Spike-Time Data Compression Theory
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    Chapter 22 From Brain-Inspired AI to a Symbiosis of Human Intelligence and Artificial Intelligence
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Title
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
Published by
Springer Berlin Heidelberg, October 2018
DOI 10.1007/978-3-662-57715-8
ISBNs
978-3-66-257713-4, 978-3-66-257715-8
Authors

Kasabov, Nikola K.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 95 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 24 25%
Student > Ph. D. Student 19 20%
Researcher 16 17%
Student > Doctoral Student 6 6%
Other 4 4%
Other 17 18%
Unknown 9 9%
Readers by discipline Count As %
Engineering 27 28%
Computer Science 20 21%
Neuroscience 4 4%
Medicine and Dentistry 3 3%
Psychology 3 3%
Other 21 22%
Unknown 17 18%