Advanced models of neural networks : nonlinear dynamics and stochasticity in biological neurons /
[edited by] Gerasimos Rigatos.
- xiii, 275 páginas: ilustraciones
1.- Modelling biological neurons in terms of electrical circuits -- 2.- Systems theory for the analysis of biological neural dynamics -- 3.- Bifurcations and limit cycles in models of biological systems -- 4.- Oscillatory dynamics in biological neurons -- 5.- Synchronization of circadian neurons and protein -- 6.-Wave dynamics in the transmission of neural siginals -- 7.- Stochastic models of biological neuron dynamics -- 8.- Synchroonization of stochastic neural oscillators usin lyapunov methods -- 9.- Synchronization of chaotic ans stochastic neurons using differental flatness theory -- 10.- Attractors in associative memories with stochastic weights -- 1.- Spectral analysis of neural models with stochastic weights -- 12.- Neural networks based on the eigenstates of the quantum harminic oscilators -- 13.- Quantum control and manipulation of systems and processes at moleculas scale.-
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.