Traiter le cerveau avec les neurosciences : théorie de champ-moyen, effets de taille finie et capacité de codage des réseaux de neurones stochastiques

摘要

The brain is the most complex system in the known universe. Its nested structure with small-world properties determines its function and behavior. The analysis of its structure requires sophisticated mathematical and statistical techniques. In this thesis we shed new light on neural networks, attacking the problem from different points of view, in the spirit of the Theory of Complexity and in terms of their information processing capabilities. In particular, we quantify the Fisher information of the system, which is a measure of its encoding capability. The first technique developed in this work is the mean-field theory of rate and FitzHugh-Nagumo networks without correlations in the thermodynamic limit, through both mathematical and numerical analysis. The second technique, the Mayer’s cluster expansion, is taken from the physics of plasma, and allows us to determine numerically the finite size effects of rate neurons, as well as the relationship of the Fisher information to the size of the network for independent Brownian motions. The third technique is a perturbative expansion, which allows us to determine the correlation structure of the rate network for a variety of different types of connectivity matrices and for different values of the correlation between the sources of randomness in the system. With this method we can also quantify numerically the Fisher information not only as a function of the network size, but also for different correlation structures of the system. The fourth technique is a slightly different type of perturbative expansion, with which we can study the behavior of completely generic connectivity matrices with random topologies. Moreover this method provides an analytic formula for the Fisher information, which is in qualitative agreement with the other results in this thesis. Finally, the fifth technique is purely numerical, and uses an Expectation-Maximization algorithm and Monte Carlo integration in order to evaluate the Fisher information of the FitzHugh-Nagumo network. In summary, this thesis provides an analysis of the dynamics and the correlation structure of the neural networks, confirms this through numerical simulation and makes two key counterintuitive predictions. The first is the formation of a perfect correlation between the neurons for particular values of the parameters of the system, a phenomenon that we term stochastic synchronization. The second, which is somewhat contrary to received opinion, is the explosion of the Fisher information and therefore of the encoding capability of the network for highly correlated neurons. The techniques developed in this thesis can be used also for a complete quantification of the information processing capabilities of the network in terms of information storage, transmission and modification, but this would need to be performed in the future.