Inverse problems in spin models

Abstract

Several recent experiments in biology study systems composed of several interacting elements, for example neuron networks. Normally, measurements describe only the collective behavior of the system, even if in most cases we would like to characterize how its different parts interact. The goal of this thesis is to extract information about the microscopic interactions as a function of their collective behavior for two different cases. First, we will study a system described by a generalized Ising model. We find explicit formulas for the couplings as a function of the correlations and magnetizations. In the following, we will study a system described by a Hopfield model. In this case, we find not only explicit formula for inferring the patterns, but also an analytical result that allows one to estimate how much data is necessary for a good inference.

PhD thesis defended the 16th September 2010 at the Laboratoire de Physique Théorique of École Normale Supérieure in Paris, France

References in corpus (5)

• Identification of direct residue contacts in protein-protein interaction by message passing
• The Ising Model for Neural Data: Model Quality and Approximate Methods for Extracting Functional Connectivity
• Small-correlation expansions for the inverse Ising problem
• Ising models for networks of real neurons
• Faster solutions of the inverse pairwise Ising problem