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

Nikola K. Kasabov
Springer Berlin
353.09 € · Hardcover

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

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 f...

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

Nikola K. Kasabov
Springer Berlin
353.09 € · Paperback

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

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 f...

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

Nikola K. Kasabov
Springer Berlin
352.03 € · eBook

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

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 f...

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