Models of Wave Memory

This monograph examines in detail models of neural systems described by delay-differential equations. Each element of the medium (neuron) is an oscillator that generates, in standalone mode, short impulses also known as spikes. The book discusses models of synaptic interaction between neurons, which...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kashchenko, Serguey (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Colección:Lecture Notes in Morphogenesis,
Temas:
Neural networks (Computer science) .
Neurosciences.
Nonlinear Optics.
System theory.
Dynamics.
Nonlinear theories.
Mathematical Models of Cognitive Processes and Neural Networks.
Neuroscience.
Complex Systems.
Applied Dynamical Systems.
Acceso en línea:Texto Completo

MARC

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300 |a XXVIII, 239 p. 34 illus.  |b online resource. 
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490 1 |a Lecture Notes in Morphogenesis,  |x 2195-1942 
505 0 |a Model of single neuron -- Model of the interaction of neurons -- Model of wave propagations in the ring neural structure with chemical synapsis -- Model of self-organization of oscillations in the ring system of homogeneous neural modules -- Model of adaptation of neural ensembles -- Model of the neural system, synchronizing the wave packets -- Model of the neural system with diffusive interaction of elements -- Pseudo correlative dimension of the electroencephalogram and its volume amount of short term memory of the human -- Estimates of differences of evoked potentials. 
520 |a This monograph examines in detail models of neural systems described by delay-differential equations. Each element of the medium (neuron) is an oscillator that generates, in standalone mode, short impulses also known as spikes. The book discusses models of synaptic interaction between neurons, which lead to complex oscillatory modes in the system. In addition, it presents a solution to the problem of choosing the parameters of interaction in order to obtain attractors with predetermined structure. These attractors are represented as images encoded in the form of autowaves (wave memory). The target audience primarily comprises researchers and experts in the field, but it will also be beneficial for graduate students. 
650 0 |a Neural networks (Computer science) . 
650 0 |a Neurosciences. 
650 0 |a Nonlinear Optics. 
650 0 |a System theory. 
650 0 |a Dynamics. 
650 0 |a Nonlinear theories. 
650 1 4 |a Mathematical Models of Cognitive Processes and Neural Networks. 
650 2 4 |a Neuroscience. 
650 2 4 |a Nonlinear Optics. 
650 2 4 |a Complex Systems. 
650 2 4 |a Applied Dynamical Systems. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783319198675 
776 0 8 |i Printed edition:  |z 9783319198651 
776 0 8 |i Printed edition:  |z 9783319368085 
830 0 |a Lecture Notes in Morphogenesis,  |x 2195-1942 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-319-19866-8  |z Texto Completo 
912 |a ZDB-2-SMA 
912 |a ZDB-2-SXMS 
950 |a Mathematics and Statistics (SpringerNature-11649) 
950 |a Mathematics and Statistics (R0) (SpringerNature-43713) 

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