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https://github.com/lacerbi/tics-2020-tutorial
Tutorial on computational modeling and statistical model fitting part of the *Trends in Computational Neuroscience* graduate course of the University of Geneva (2020).
https://github.com/lacerbi/tics-2020-tutorial
computational-neuroscience modeling optimization python tutorial
Last synced: 23 days ago
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Tutorial on computational modeling and statistical model fitting part of the *Trends in Computational Neuroscience* graduate course of the University of Geneva (2020).
- Host: GitHub
- URL: https://github.com/lacerbi/tics-2020-tutorial
- Owner: lacerbi
- License: mit
- Created: 2020-03-31T21:01:51.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-04-22T08:30:37.000Z (over 4 years ago)
- Last Synced: 2024-01-29T07:36:58.425Z (10 months ago)
- Topics: computational-neuroscience, modeling, optimization, python, tutorial
- Language: Jupyter Notebook
- Homepage:
- Size: 6.4 MB
- Stars: 4
- Watchers: 3
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tutorial on computational modeling and statistical model fitting
This tutorial is part the *Trends in Computational Neuroscience* graduate course of the University of Geneva (2020). The course instructor for this part of the course is [Luigi Acerbi](http://luigiacerbi.com/).
## Tutorial instructions
- Download and unzip the tutorial folder somewhere on your computer: [download](https://github.com/lacerbi/tics-2020-tutorial/archive/master.zip).
- To run the tutorial, you will need a standard scientific Python 3.x installation with Jupyter notebook (such as [Anaconda](https://www.anaconda.com/distribution/)).
- You will also need the `CMA-ES` optimization algorithm (see [here](https://github.com/CMA-ES/pycma)). You can install CMA-ES from the command line with `pip install cma`.
- Then open the Jupyter notebook `tics-intro-model-fitting-notebook.ipynb` (you should have *Jupyter notebook* installed as part of Anaconda).
## Additional lecture materials
- Slides of the lectures are available [here](https://github.com/lacerbi/tics-2020-tutorial/blob/master/Acerbi-TICS-2020-slides.pdf).
- Instructions for the second mini-project related to this part of the course are [here](https://github.com/lacerbi/tics-2020-tutorial/blob/master/TICS-Mini-project-2-instructions.pdf).
For any additional question, please email the course instructor at [email protected].
## License
Code and scripts in this repository are released under the terms of the [MIT License](https://github.com/lacerbi/tics-2020-tutorial/blob/master/LICENSE).