[5] G. Rigatos,
Advanced Models of Neural Networks: Nonlinear dynamics and stochasticity in biological neurons, Springer, 2014
(monograph). download
Ref. No 5049 Advanced Models of Neural Networks: Nonlinear Dynamics and Stochasticity in Biological Neurons,
Duration: 2013-2014 . The project has provided new findings on the modelling of neural dynamics in terms of electrical circuits and
has analysed related stability properties with the use of dynamical systems theory. It was shown that such electric circuit models
are characterized by attractors and fixed points while in certain cases they exhibit bifurcations and chaotic dynamics. Moreover,
solutions of the neural dynamics in the form of travelling waves equations have been derived. The dynamics of interconnected neurons
have been analysed with the use of forced oscillator and coupled oscillator models. The project has demonstrated that by applying
nonlinear control to biosystems one can modify their dynamics and can achieve their functioning according to specific performance
objectives. For instance one can note the use of automatic control methods in protein and hormonal synthesis control. Besides,
controlled drug infusion has been proposed for more efficient pharmaceutical treatment of several diseases.
