This is a julia package for advanced Monte Carlo simulations of classical equilibrium and non-equilibrium processes

Train and use a neural network to approximate arbitratry functions

Scientific website of Johannes Zierenberg

Mechanism for source localization from diffusive chemicals despite small size (cf. infotaxis)

A general function approximation using a deep neural network trained on sampled points

Code accompanying our paper "How contact patterns destabilize and modulate epidemic outbreaks"

Personal template to comply with the DFG proposal template

Code that accompanies our Review on methodological advances to make use of subsampled data

phase diagram of a probabilistic integrate and fire model

Deep neural network (Boltzmann generators) with muca

Tools to estimate low-dimensional latent space from high dimensional spiking data

Study correlation functions in reservoir computing

Develop effective sptial models of directed percolation with annealed coupling motivated by DP-like puffs at the onset of turbulent in (active) turbulence

Material for lecture on current approaches to model disease spread within a lecture series on current topic of theoretical physics.

Experiment to measure mutual information of human communication with noisy channel for teaching purposes

generate correlated noise in several languages

Lecture notes (German) including hands-on practice part

Stochastic kinetic Monte Carlo simulations of testing-tracing-isolation strategies for small communities

Julia module for simulation of simple neuron models on different network topologies

julia module for complex networks (hierarchical, modular, etc) based on LightGraphs

Characterize dynamic range of branching network (directed percolation) with and without coalescence

Code used to generate 'Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence' by Zierenberg et al.