Statistics of spike trains: A dynamical systems perspective
Abstract
We review recent results dealing with the analysis of spike train statistics in neural networks models, using methods from dynamical systems (thermodynamic formalism). We discuss why Gibbs distributions are natural candidates and present some consequences at the theoretical and algorithmic level.