Partial Information Decomposition Reveals the Structure of Neural Representations. - David A. Ehrlich; Andreas C. Schneider; Michael Wibral; Viola Priesemann; Abdullah Makkeh (CoRR, 2022)

Infomorphic networks: Locally learning neural networks derived from partial information decomposition. - Marcel Graetz; Abdullah Makkeh; Andreas C. Schneider; David A. Ehrlich; Viola Priesemann; Michael Wibral (CoRR, 2023)

A Measure of the Complexity of Neural Representations based on Partial Information Decomposition. - David A. Ehrlich; Andreas C. Schneider; Viola Priesemann; Michael Wibral; Abdullah Makkeh (Trans. Mach. Learn. Res., 2023)

Telegram graph data of COVID-19 related channels. - Sebastian Bernd Mohr; Andreas C. Schneider; Jasper Benke; Viola Priesemann (GRO.data, 2023)

Telegram graph data of COVID-19 related channels. - Sebastian Bernd Mohr; Andreas C. Schneider; Viola Priesemann (GRO.data, 2023)

Telegram graph data of COVID-19 related channels. - Sebastian Bernd Mohr; Andreas C. Schneider; Süheda Yildirim; Jasper Benke; Viola Priesemann (GRO.data, 2023)

A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions. - Abdullah Makkeh; Marcel Graetz; Andreas C. Schneider; David A. Ehrlich; Viola Priesemann; Michael Wibral (GRO.data, 2024)

What should a neuron aim for? Designing local objective functions based on information theory. - Andreas Christian Schneider; Valentin Neuhaus; David Alexander Ehrlich; Abdullah Makkeh; Alexander S. Ecker; Viola Priesemann; Michael Wibral (ICLR, 2025)