Courses
Learn to build modular experiments with Python and PsychoPy using software engineering best practices like automated testing, data validation, and continuous integration.
Compact one-day course covering data analysis with Numpy and Pandas, visualization with Matplotlib, and statistical tests using real neuroscience data.
Tools for reproducible computational research: VSCode and Jupyter for interactive coding, Conda and Pixi for environment management, and Git and GitHub for version control and collaboration.
Explore database management with SQL, DuckDB, HDF5, and JSON to seamlessly integrate and analyze complex neuroscience datasets.
Analyze neural spiking data with Pandas, Seaborn, and Elephant, from spike sorting with SpikeInterface to advanced statistical inference methods.
Detailed introduction to programming with Python including data analysis with Numpy and Pandas, visualization with Matplotlib, and statistical tests using real neuroscience data.
Analyze calcium imaging data from TIFF stacks to neuronal activity using trace extraction, spike inference, and tools like CaImAn and Suite2P.
Introduction to local field potential (LFPs) analysis and signal processing using Numpy, Xarray, Scipy, and specialized tools like Elephant and Neo.
Learn to create reproducible research with data science notebooks, hvPlot visualizations, and automated multi-notebook pipelines using PyDoIt and Papermill.
Learn C++ programming with Arduino microcontrollers for neuroscience experiments, from sensors to efficient real-world code and version control.
Build robust, reproducible analysis pipelines with Snakemake, Conda, and Git for scalable computational neuroscience projects.