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Random Constraint Satisfaction Problems (CSPs) are ubiquitous in computer science and everyday life, including examples ranging from Sudokus to optimal digital board design. A CSP involves many discrete variables interacting through random constraints. When the number of competing conditions gets large, the optimization of a CSP instance can become extraordinarily hard. The Survey Propagation algorithm, based on the iterative exchange of simple probabilistic messages along the edges of a factor graph, succeeds to optimize even very hard random instances, whereas more standard...

This book illustrates the thrust of the scientific community to use machine learning concepts for tackling a complex problem: given time series of neuronal spontaneous activity, which is the underlying connectivity between the neurons in the network? The contributing authors also develop tools for the advancement of neuroscience through machine learning techniques, with a focus on the major open problems in neuroscience.
While the techniques have been developed for a specific application, they address the more general problem of network reconstruction from observational time series, a...