Spike train statistics and Gibbs distributions

This paper is based on a lecture given in the LACONEU summer school, Valparaiso, January 2012. We introduce Gibbs distribution in a general setting, including non stationary dynamics, and present then three examples of such Gibbs distributions, in the context of neural networks spike train statistics: (i) Maximum entropy model with spatio-temporal constraints; (ii) Generalized Linear Models; (iii) Conductance based Inte- grate and Fire model with chemical synapses and gap junctions.Comment: 23 pages, submitte

An asymptotic shape theorem for random linear growth models

In this note, we generalize the asymptotic shape theorem proved in [Des14a] for a class of random growth models whose growth is at least and at most linear. In this way, we obtain asymptotic shape theorems conjectured for several models: the contact process in a randomly evolving environment [SW08], the oriented percolation with hostile immigration [GM12b] and the bounded modified contact process [DS00]