Department of Physiology

Software, Prototype Demos & Datasets

  • Lu.i - An Educational Neuron Printed Circuit Board
    Lu.i is an electronic neuron circuit mimicking and illustrating the basic dynamics of real, biological neurons.
    Sebastian Billaudelle, Yannik Stradmann, and Julian Göltz
    GitHub, 2022

  • The DELAUNAY Dataset
    A dataset of abstract art for psychophysical and machine learning research
    Camille Gontier, Jakob Jordan, Mihai A. Petrovici
    GitHub, 2022

  • Learning cortical representations through perturbed and adversarial dreaming
    This repository contains the code to reproduce the results of the eLife publication "Learning cortical representations through perturbed and adversarial dreaming"
    Nicolas Deperrois, Mihai A. Petrovici, Walter Senn, Jakob Jordan
    GitHub, 2022 - More info on EBRAINS Knowledge Graph

  • Latent Equilibrium
    This repository contains the code to reproduce the results of the NeurIPS 2021 submission "Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons"
    Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici
    GitHub, 2021

  • gridspeccer
    A software to make plotting in Matplotlib easier
    Julian Göltz and Oliver Breitwieser (developed in the NeuroTMA Lab)
    GitHub, 2021

  • HAL-CGP
    A software for Cartesian Genetic Programming (CGP) in pure Python
    Jakob Jordan, Henrik Mettler and Maximilian Schmidt (developed in the NeuroTMA Lab)
    GitHub, 2021 - More info on EBRAINS Knowledge Graph

  • Dendritic opinion pooling
    Library for the simulation of rate-based neuron models with conductance-based synapses in feedforward architectures
    Jakob Jordan, João Sacramento, Willem A.M. Wybo, Mihai A. Petrovici, Walter Senn
    GitHub, 2021 - More info on EBRAINS Knowledge Graph

  • sbs: Spike-based Sampling
    A helper library for stochastic LIF sampling in PyNN-supported neural simulators
    Oliver Breitwieser, Andreas Baumbach, Agnes Korcsak-Gorzo, Johann Klähn, Max Brixner and Mihai A. Petrovici
    Zenodo, 2020

  • The Yin-Yang Dataset
    A deep learning dataset for research on biologically plausible error-backpropagation and deep learning in spiking neural networks
    Laura Kriener, Julian Göltz, Mihai A. Petrovici
    GitHub, 2020 - also published in arXiv

  • Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate
    A demonstration of five experiments performed on the BrainScaleS-2 neuromorphic system
    Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber, Benjamin Cramer, Andreas Baumbach, Dominik Dold, Julian Goltz, Akos F. Kungl, Timo C. Wunderlich, Andreas Hartel, Eric Muller, Oliver Breitwieser, Christian Mauch, Mitja Kleider, Andreas Grübl, David Stöckel, Christian Pehle, Arthur Heimbrecht, Philipp Spilger, Vitali Karasenko, Walter Senn, Mihai A. Petrovici, Johannes Schemmel and Karlheinz Meier
    IEEE International Symposium on Circuits and Systems (ISCAS), 2020

  • Fast sampling with neuromorphic hardware
    A demonstration of the first implementation of neural sampling in the mixed-signal, low-power and highly accelerated Spikey neuromorphic hardware.
    Mihai A. Petrovici, David Stöckel, Ilja Bytschok, Johannes Bill, Thomas Pfeil, Johannes Schemmel, Karlheinz Meier
    Advances in Neural Information Processing Systems (NeurIPS) 28, 2015