Advanced Models of Neural Networks: Nonlinear Dynamics and Stochasticity in Biological Neurons by Gerasimos G. RigatosAdvanced Models of Neural Networks: Nonlinear Dynamics and Stochasticity in Biological Neurons by Gerasimos G. Rigatos

Advanced Models of Neural Networks: Nonlinear Dynamics and Stochasticity in Biological Neurons

byGerasimos G. Rigatos

Hardcover | September 9, 2014

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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.

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 Engi...
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Title:Advanced Models of Neural Networks: Nonlinear Dynamics and Stochasticity in Biological NeuronsFormat:HardcoverDimensions:275 pagesPublished:September 9, 2014Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3662437635

ISBN - 13:9783662437636

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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.