Advances in Neural Computation, Machine Learning, and Cognitive Research V

Beschreibung

This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXIII International Conference on Neuroinformatics, held on October 18-22, 2021, Moscow, Russia.
This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXIII International Conference on Neuroinformatics, held on October 18-22, 2021, Moscow, Russia.
Reports on advanced theories and applications of artificial neural networks Focuses on problems in neuroscience, systems biophysics, cognitive research and adaptive control Merges topics in neurobiology, machine learning and evolutionary programming

Autor*in

Boris Kryzhanovsky

Themen in »Advances in Neural Computation, Machine Learning, and Cognitive Research V«

Human Brain Models Neural Field Models Socially Intelligent Agents Hopfield Neural Network Multi-agent Learning Evolutionary Autonomous Agents Hybrid Superintelligent System EEG Pattern Analysis Convolutional Neural Network Architectures Compartmental Spiking Neuron Model Deep Learning Models Biologically-plausible Learning Algorithm Speech Perception Image Segmentation with Machine Learning Autonomous Vehicle Control

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