000			02901nam a22003735i 4500
650		7	_aRedes neuronales artificiales _2embne _9678664
999			_c103489 _d103489 _x1
001			103489
003			DE-He213
005			20230221040153.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			140827s2015 gw | s |||| 0|eng d
020			_a9783662437643
024	7		_a10.1007/978-3-662-43764-3 _2doi
040			_bspa _dES-MaUEC
050		4	_aQA76.87 _b2015 EB
100	1		_aRigatos, Gerasimos G. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/57974040/
245	1	0	_aAdvanced Models of Neural Networks : _bNonlinear Dynamics and Stochasticity in Biological Neurons _cby Gerasimos G. Rigatos.
264		1	_aBerlin, Heidelberg _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XXIII, 275 páginas 135 ilustraciones, 91 ilustraciones a color.)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aEngineering (Springer-11647)
505	0		_aModelling 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	3		_aThis 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.
988			_aEBSPRINGER_2018
776	0	8	_iEdición impresa: _z9783662437636
776	0	8	_iEdición impresa: _z9783662437650
776	0	8	_iEdición impresa: _z9783662515570
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-662-43764-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _eIG _zSI