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Author:   Gerasimos G. Rigatos
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of the original 1st ed. 2015
Dimensions:   Width: 15.50cm , Height: 1.60cm , Length: 23.50cm
Weight:   4.569kg
ISBN:  

9783662515570

ISBN 10:   3662515571
Pages:   275
Publication Date:   23 August 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

Reviews

Several chapters deal with standard questions like control, synchronization, and estimation. Rigatos uses a clever linearization technique, and then applies variants of linear control techniques to solve these problems for nonlinear models. ... I recommend this book to those interested in neural nets who won't be put off by the density of the mathematics. (Paul Cull, Computing Reviews, December, 2014)

"""Several chapters deal with standard questions like control, synchronization, and estimation. Rigatos uses a clever linearization technique, and then applies variants of linear control techniques to solve these problems for nonlinear models. ... I recommend this book to those interested in neural nets who won't be put off by the density of the mathematics."" (Paul Cull, Computing Reviews, December, 2014)"

Author Information

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.

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