27 citations · 103 across the 10 of their papers we have counts for
11 papers
Phase Transitions of the Additive Uniform Noise Channel with Peak Amplitude and Cost Constraint
Jonas Stapmanns, Catarina Dias, Luke Eilers +2
Under which condition is quantization optimal? We address this question in the context of the additive uniform noise channel under peak amplitude and cost constraints. We compute a…
Diagrammatics of free energies with fixed variance for high-dimensional data
Tobias Kühn
Systems with many interacting stochastic constituents are fully characterized by their free energy. Computing this quantity is therefore the objective of various approaches, notabl…
Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivity
Tobias Kühn, Rémi Monasson
Continuous attractor neural networks (CANN) form an appealing conceptual model for the storage of information in the brain. However a drawback of CANN is that they require finely t…
Diagrammatics for the Inverse Problem in Spin Systems and Simple Liquids
Tobias Kühn, Frédéric van Wijland
Modeling complex systems, like neural networks, simple liquids or flocks of birds, often works in reverse to textbook approaches: given data for which averages and correlations are…
Gell-Mann-Low criticality in neural networks
Lorenzo Tiberi, Jonas Stapmanns, Tobias Kühn +3
Criticality is deeply related to optimal computational capacity. The lack of a renormalized theory of critical brain dynamics, however, so far limits insights into this form of bio…
Large Deviations Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions
Alexander van Meegen, Tobias Kühn, Moritz Helias
We here unify the field theoretical approach to neuronal networks with large deviations theory. For a prototypical random recurrent network model with continuous-valued units, we s…
Transient chaotic dimensionality expansion by recurrent networks
Christian Keup, Tobias Kühn, David Dahmen +1
Neurons in the brain communicate with spikes, which are discrete events in time and value. Functional network models often employ rate units that are continuously coupled by analog…
Self-consistent formulations for stochastic nonlinear neuronal dynamics
Jonas Stapmanns, Tobias Kühn, David Dahmen +3
Neural dynamics is often investigated with tools from bifurcation theory. However, many neuron models are stochastic, mimicking fluctuations in the input from unknown parts of the…
Expansion of the effective action around non-Gaussian theories
Tobias Kühn, Moritz Helias
This paper derives the Feynman rules for the diagrammatic perturbation expansion of the effective action around an arbitrary solvable problem. The perturbation expansion around a G…
mlr Tutorial
Julia Schiffner, Bernd Bischl, Michel Lang +9
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.
Locking of correlated neural activity to ongoing oscillations
Tobias Kühn, Moritz Helias
Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of ne…