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Advanced Models of Neural Networks

Nonlinear Dynamics and Stochasticity in Biological Neurons

  • Book
  • © 2015

Overview

Authors:
  1. Gerasimos G. Rigatos

    1. Unit of Industrial Automation, Industrial Systems Institute, Rion Patras, Greece

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  • Suitable for researchers engaged with neural networks and dynamical systems theory

  • Introduces advanced models of neural networks

  • Includes several chapters suitable for related postgraduate courses in engineering, computer science, mathematics, physics and biology

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. Modelling Biological Neurons in Terms of Electrical Circuits

    • Gerasimos G. Rigatos
    Pages 1-26

  3. Systems Theory for the Analysis of Biological Neuron Dynamics

    • Gerasimos G. Rigatos
    Pages 27-52

  4. Bifurcations and Limit Cycles in Models of Biological Systems

    • Gerasimos G. Rigatos
    Pages 53-74

  5. Oscillatory Dynamics in Biological Neurons

    • Gerasimos G. Rigatos
    Pages 75-106

  6. Synchronization of Circadian Neurons and Protein Synthesis Control

    • Gerasimos G. Rigatos
    Pages 107-130

  7. Wave Dynamics in the Transmission of Neural Signals

    • Gerasimos G. Rigatos
    Pages 131-147

  8. Stochastic Models of Biological Neuron Dynamics

    • Gerasimos G. Rigatos
    Pages 149-168

  9. Synchronization of Stochastic Neural Oscillators Using Lyapunov Methods

    • Gerasimos G. Rigatos
    Pages 169-180

  10. Synchronization of Chaotic and Stochastic Neurons Using Differential Flatness Theory

    • Gerasimos G. Rigatos
    Pages 181-190

  11. Attractors in Associative Memories with Stochastic Weights

    • Gerasimos G. Rigatos
    Pages 191-206

  12. Spectral Analysis of Neural Models with Stochastic Weights

    • Gerasimos G. Rigatos
    Pages 207-219

  13. Neural Networks Based on the Eigenstates of the Quantum Harmonic Oscillator

    • Gerasimos G. Rigatos
    Pages 221-249

  14. Quantum Control and Manipulation of Systems and Processes at Molecular Scale

    • Gerasimos G. Rigatos
    Pages 251-262

  15. Back Matter

    Pages 263-275
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Keywords

About this book

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.

Authors and Affiliations

  • Unit of Industrial Automation, Industrial Systems Institute, Rion Patras, Greece

    Gerasimos G. Rigatos

About the author

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.

Bibliographic Information

  • Book Title: Advanced Models of Neural Networks

  • Book Subtitle: Nonlinear Dynamics and Stochasticity in Biological Neurons

  • Authors: Gerasimos G. Rigatos

  • DOI: https://doi.org/10.1007/978-3-662-43764-3

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2015

  • Hardcover ISBN: 978-3-662-43763-6Published: 09 September 2014

  • Softcover ISBN: 978-3-662-51557-0Published: 23 August 2016

  • eBook ISBN: 978-3-662-43764-3Published: 27 August 2014

  • Edition Number: 1

  • Number of Pages: XXIII, 275

  • Number of Illustrations: 44 b/w illustrations, 91 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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