Preprints
2025
Diagrammatic expansion for the mutual-information rate in the realm of limited statistics
T. Kühn, G. Mahuas, U. Ferrari
arxiv
Papers
2026
Phase Transitions of the Additive Uniform Noise Channel With Peak Amplitude and Cost Constraint
J. Stapmanns, C. Dias, L. Eilers, T. Kühn, J.-P. Pfister
IEEE Transactions on Information Theory
Diagrammatics of free energies with fixed variance for high-dimensional data
T. Kühn
Journal of Physics A
2023
Information content in continuous attractor neural networks is preserved
in the presence of moderate disordered background connectivity
T. Kühn, R. Monasson
Physical Review E, editor’s suggestion, also available at arxiv
Diagrammatics for the inverse problem in spin systems and simple liquids
T. Kühn, F. van Wijland
Journal of Physics A, also available at arxiv
2022
Gell-Mann-Low criticality in neural networks
L. Tiberi1, J. Stapmanns1, T. Kühn, T. Luu, D. Dahmen, M. Helias
Physical Review Letters
2021
Large-deviation approach to random recurrent neuronal networks: parameter inference and fluctuation-induced transitions
A. van Meegen, T. Kühn, M. Helias
Physical Review Letters
Transient chaotic dimensionality expansion by recurrent networks
T. Kühn1, C. Keup1, D. Dahmen, M. Helias
Physical Review X
2020
Self-consistent formulations for stochastic nonlinear neuronal dynamics
J. Stapmanns1, T. Kühn1, D. Dahmen, T. Luu. C. Honerkamp, M. Helias
Physical Review E
2018
Expansion of the effective action around non-Gaussian theories
T. Kühn, M. Helias
Journal of Physics A
2017
Locking of correlated neural activity to ongoing oscillations
T. Kühn, M. Helias
PloS Computational Biology
Thesis
2019/2020
Path integral methods for correlated activity in neuronal networks
T. Kühn
RWTH Aachen
Also see my Google Scholar page.
1 Equal contribution