Kristina Lisa Klinkner

TL;DR: Two approaches to automatically filter the changing configurations of spatial dynamical systems and extract coherent structures are presented, a modification of the local Lyapunov exponent approach suitable to cellular automata and other discrete spatial systems and a more traditional approach based on an order parameter and free energy is compared.

TL;DR: In this paper, causal state models (CSMs), the minimal hidden Markov models or stochastic automata capable of generating statistically identical time series, are used to quantify both the generalizable structure and the idiosyncratic randomness of the spike train.

TL;DR: The methods presented here complement more traditional spike train analyses by describing not only spiking probability and spike train entropy, but also the complexity of a spike train's structure.

TL;DR: This paper lays out the problem of discovering functional communities, and describes an approach to doing so that combines recent work on measuring information sharing across stochastic networks with an existing and successful community-discovery algorithm for weighted networks.

TL;DR: In this article, the authors lay out the problem of discovering functional communities and describe an approach to doing so, which combines recent work on measuring information sharing across stochastic networks with an existing and successful community-discovery algorithm for weighted networks.