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Advanced Models of Neural NetworksNo...

Rigatos, Gerasimos G.

Advanced Models of Neural NetworksNonlinear Dynamics and Stochasticity in Biological Neurons /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced Models of Neural Networksby Gerasimos G. Rigatos.
Reminder of title:	Nonlinear Dynamics and Stochasticity in Biological Neurons /
Author:	Rigatos, Gerasimos G.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg :2015.
Description:	xxiii, 275 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science)Mathematical models.
Online resource:	http://dx.doi.org/10.1007/978-3-662-43764-3
ISBN:	9783662437643 (electronic bk.)

Advanced Models of Neural NetworksNonlinear Dynamics and Stochasticity in Biological Neurons /
Rigatos, Gerasimos G.

Advanced Models of Neural NetworksNonlinear Dynamics and Stochasticity in Biological Neurons /[electronic resource] :by Gerasimos G. Rigatos. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - xxiii, 275 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9783662437643 (electronic bk.)

Standard No.: 10.1007/978-3-662-43764-3doiSubjects--Topical Terms:

710533
Neural networks (Computer science)
--Mathematical models.

LC Class. No.: QA76.87

Dewey Class. No.: 006.3

Advanced Models of Neural NetworksNonlinear Dynamics and Stochasticity in Biological Neurons /
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245 1 0 $a Advanced Models of Neural Networks $h [electronic resource] : $b Nonlinear Dynamics and Stochasticity in Biological Neurons / $c by Gerasimos G. Rigatos.
260 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer, $c 2015.
300 $a xxiii, 275 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
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