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@mackelab

mackelab

Machine Learning in Science at University of Tübingen, Germany
README.md

Machine Learning in Science

Our goal is to accelerate scientific discovery using machine learning and artificial intelligence: We develop computational methods that help scientists interpret empirical data and use them to gain scientific insights.

We closely collaborate with experimental researchers from various disciplines. We are particularly interested in applications in the neurosciences: We build data-driven mechanistic models of neuronal functions in order to understand how neuronal networks in the brain process sensory information and control intelligent behaviour.

We are part of the Excellence Cluster Machine Learning Tübingen and the Tübingen AI Center. You can find out more about us on our lab website.

In addition to the repositories in this organization, (former) lab members have also developed the following toolboxes:

  • sbi, a toolbox for simulation-based inference,
  • DECODE, a deep learning tool for single molecule localization microscopy,
  • sbibm, a benchmark for simulation-based inference,
  • flyvis, a connectome constrained deep mechanistic network (DMN) model.

Pinned

  1. mnle-for-ddms mnle-for-ddms Public

    Research code for Mixed Neural Likelihood Estimation (MNLE, Boelts et al. 2022)

    Jupyter Notebook 17 5

  2. phase-limit-cycle-RNNs phase-limit-cycle-RNNs Public

    Code for "Trained recurrent neural networks develop phase-locked limit cycles in a working memory task" - Matthijs Pals (@matthijspals) , Jakob Macke and Omri Barak.

    Python 5 2

  3. neural_timeseries_diffusion neural_timeseries_diffusion Public

    This repository contains research code for the preprint "Generating realistic neurophysiological time series with denoising diffusion probabilistic models".

    Python 48 5

  4. sbi-ice sbi-ice Public

    Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers

    Python 1

  5. STG_energy STG_energy Public

    Repo for STG energy paper. Michael (@michaeldeistler) and Pedro (@ppjgoncalves).

    Jupyter Notebook 1

  6. labproject labproject Public

    Labproject about comparing distributions metrics by @mackelab

    Python

Repositories

Showing 10 of 73 repositories
  • labproject Public

    Labproject about comparing distributions metrics by @mackelab

    Python 0 MIT 0 13 0 Updated Jun 6, 2024
  • epiphyte Public

    Python toolkit for working with high-dimensional neural data recorded during naturalistic, continuous stimuli @a-darcher @rachrapp

    Jupyter Notebook 4 1 0 0 Updated Jun 5, 2024
  • preprocessing-ice-data Public

    Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers

    Python 0 AGPL-3.0 0 0 0 Updated Jun 3, 2024
  • sbi-ice Public

    Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers

    Python 1 AGPL-3.0 0 3 0 Updated Jun 3, 2024
  • simulation_based_model_inference Public

    Repository for the paper "Simultaneous identification of models and parameters of scientific simulators" -- Cornelius: @coschroeder

    0 MIT 0 0 0 Updated May 24, 2024
  • phase-limit-cycle-RNNs Public

    Code for "Trained recurrent neural networks develop phase-locked limit cycles in a working memory task" - Matthijs Pals (@Matthijspals) , Jakob Macke and Omri Barak.

    Python 5 Apache-2.0 2 0 0 Updated May 22, 2024
  • amortized-decision-making Public

    Repository for the paper "Amortized Bayesian Decision Making for simulation-based models" - Mila @milagorecki and Michael @michaeldeistler

    Jupyter Notebook 3 0 0 0 Updated May 21, 2024
  • neural_timeseries_diffusion Public

    This repository contains research code for the preprint "Generating realistic neurophysiological time series with denoising diffusion probabilistic models".

    Python 48 AGPL-3.0 5 0 0 Updated May 15, 2024
  • grassmann_binary_distribution Public

    Implementation of binary distribution in the Grassmann formalism, including conditional version. Main contributor: Cornelius Schröder (@coschroeder)

    Jupyter Notebook 2 MIT 0 0 0 Updated May 13, 2024
  • simformer Public

    Combination of transformers and diffusion models for flexible all-in-one simulation-based inference

    Jupyter Notebook 20 MIT 0 0 0 Updated May 12, 2024
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