Software

JIDT: Java Information Dynamics Toolkit
JIDT is a flagship toolkit for information theory in complex systems, originally designed by Joe Lizier. It’s writen in super-duper-portable Java, which means you can simply download it and run it from Python, Matlab, or R – without installation. Damn that’s convenient.
JIDT: Java Information Dynamics Toolkit
EntRate: Entropy rate estimators for neuroscience
This library implements various estimators of entropy rate on continuous and discrete data. Work in progress, suggestions welcome!
Paper
EntRate: Entropy rate estimators for neuroscience
FastDMF: Fast Dynamic Mean Field model simulator
This library implements an extended version of Deco’s DMF simulator of brain dynamics using a multi-threaded C++ backend (via Eigen), and provides both Python and Matlab interfaces. This code is around 5-10x faster and consumes around 1000x less memory than previous implementations.
Paper Paper
FastDMF: Fast Dynamic Mean Field model simulator
SynDisc: Synergistic information via data disclosure
This Python package implements the synergy measure (and corresponding information decomposition) in our 2020 J Physics A paper. In essence, synergy is defined as the maximum information transmittable through a ‘synergistic channel’, and the optimisation is done with linear programming on the vertices of a probability polytope.
Paper
SynDisc: Synergistic information via data disclosure
IDTxl: Information Dynamics Toolkit xl
IDTxl is a dedicated Python package for the estimation of directed information transfer networks from data. It is extremely data-efficient: it can accurately estimate networks between hundreds of nodes with as few as 1000 time points.
Paper
IDTxl: Information Dynamics Toolkit xl