It is widely recognized that rhythmic oscillatory activity in networks of neurons plays an important role in the brain functionning and a key role in processing neural information. This thesis is devoted to the analysis of this synchronized activity by using tools and methods issued from automatic control and stability theory. Two models are used to describe oscillatory activity of neural networks : Kuramoto model and network of Hindmarsh-Rose neurons. First, we consider Kuramoto model with complete (all-to-all) coupling, which is one of the simplest systems used to model neural network. For this model we construct an auxiliary linear system that preserves information on the natural frequencies and interconnection gains of the original Kuramoto model. Next, stability properties of this model are analyzed and we show that the solutions of the new linear system converge to a stable periodic limit cycle. Finally, we show that constrained to the limit cycle, dynamics of the linear system coincide with the original Kuramoto model. Second, a model for the network (population) with a better behavior, with respect to the Kuramoto model, from a biological point of view but more complex is considered. Particularly, we consider a network of diffusively coupled neurons where we use a Hindmarsh-Rose model to describe the dynamics of each individual neuron. Based on semi-passivity of individual Hindmarsh-Rose neurons, we analyse stability properties of a heterogeneous network of such neurons and show that network is practically synchronized for sufficient large values of interconnection gains. Moreover, we characterize the limiting synchronized behavior by using an averaging of all neuron dynamics.
Stability analysis of non-linear network scillator, neuronal population application