Advanced Models of Neural Networks

Nonlinear Dynamics and Stochasticity in Biological Neurons
Softcover Nachdruck of the original 1. Auflage 2015
ISBN: 978-3-662-51557-0
Verlag: Springer

Buch, Englisch, 275 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 4569 g

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory.

It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

Zielgruppe

Research

Autoren/Hrsg.

Rigatos, Gerasimos G.

Fachgebiete

• Mathematik | Informatik Mathematik Geometrie Dynamische Systeme
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Technische Wissenschaften Technik Allgemein Modellierung & Simulation

Inhaltsverzeichnis

Modelling Biological Neurons in Terms of Electrical Circuits.- Systems Theory for the Analysis of Biological Neuron Dynamics.- Bifurcations and Limit Cycles in Models of Biological Systems.- Oscillatory Dynamics in Biological Neurons.- Synchronization of Circadian Neurons and Protein Synthesis Control.- Wave Dynamics in the Transmission of Neural Signals.- Stochastic Models of Biological Neuron Dynamics.- Synchronization of Stochastic Neural Oscillators Using Lyapunov Methods.- Synchronization of Chaotic and Stochastic Neurons Using Differential Flatness Theory.- Attractors in Associative Memories with Stochastic Weights.- Spectral Analysis of Neural Models with Stochastic Weights.- Neural Networks Based on the Eigenstates of the Quantum Harmonic Oscillator.- Quantum Control and Manipulation of Systems and Processes at Molecular Scale.- References.- Index.

Über Autor(innen)

Dr. Gerasimos Rigatos received his Ph.D. from the Dept. of Electrical and Computer Engineering of the National Technical University of Athens, Greece. He had a postdoctoral position at IRISA, Rennes, France, he was an invited professor at the Université Paris XI (Institut d'Eléctronique Fondamentale) and a lecturer in the Dept. of Engineering of Harper-Adams University College, UK. He is now a researcher in the Unit of Industrial Automation, Industrial Systems Institute, Patras, Greece. His research interests include computational intelligence, adaptive systems, mechatronics, robotics and control, optimization and fault diagnosis.