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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 16 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 6 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

Repositories

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People

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