VIB.AI Gonçalves lab

Code & Tools

We believe that open, transparent, and well-documented code is essential to advancing neuroscience. That’s why we actively develop and share the software tools we use in our research. Our lab contributes to the development of scalable and interpretable machine learning methods designed to extract mechanistic insight from neural data.

You can find our code, toolboxes, and documentation on our GitHub:

GitHub
Hackaton

We are proud contributors to the following open-source packages:

  • sbi
    A simulation-based inference toolkit for parameter estimation in complex mechanistic models, using modern deep learning techniques.
     
  • Jaxley
    A differentiable simulator for biophysical neuron models built in JAX, enabling gradient-based learning and optimization in mechanistic neuroscience models.

Our tools are developed with flexibility, reproducibility, and interpretability in mind—and are designed to support researchers working at the interface of computational neuroscience and machine learning.

We welcome contributions and collaborations. If you’re using our tools or would like to get involved, feel free to reach out or open an issue on GitHub.