The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory.
C++/Python Information theoretic analyses tools
Code to reproduce the results of the papers "What should a neuron aim for? Designing local objective functions based on information theory" and "Redundancy Maximization as a Principle of Associative Memory Learning in Hopfield Networks"
Safe Rust estimator core for categorical shared-exclusions PID (SxPID), KSG mutual information, and default-off experimental continuous PID. Optional Python bindings. v0.9.0 source-review prerelease.
A Python Package for the Analysis of Deep Neural Networks using Information Theory
Bertschinger-Rauh-Olbrich-Jost-Ay (BROJA) bivariate Partial Information Decomposition
Partial Information Decomposition (PID) analysis of activation functions in deep neural networks — analysis code for PIDeepnets (PID-based deep networks).
Auditable experiment semantics for intervention-grounded diagnosis of embodied policies
A non-negative Partial Information Decomposition
Deciphering microbial interactions with functional redundancy in human microbial community with partial information decomposition.
A non-negative Partial Information Decomposition
The toolbox realizes partial information rate decomposition (PIRD) for a network of random processes mapped by multivariate time series, for the case of one target process and two or three source processes.
Information decomposition in decision-making RNNs: How context integration demand shapes redundant vs synergistic coding. NeuroAI & ML course project, Technical University of Munich, SS2026.
Modified simulation code for context-switching neuronal plasticity model (Agnes et al., 2020). Used to generate data for information-theoretic investigation of neuronal plasticity.
ReScience C publication — three-way information-theoretic analysis of transfer functions; a replication of Smyth, Phillips & Kay (1996).
Quantifying Redundancies and Synergies with Measures of Inequality
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