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.
- Download and unzip the tutorial folder somewhere on your computer: download.
- To run the tutorial, you will need a standard scientific Python 3.x installation with Jupyter notebook (such as Anaconda).
- You will also need the
CMA-ESoptimization algorithm (see here). You can install CMA-ES from the command line withpip install cma. - Then open the Jupyter notebook
tics-intro-model-fitting-notebook.ipynb(you should have Jupyter notebook installed as part of Anaconda).
- Slides of the lectures are available here.
- Instructions for the second mini-project related to this part of the course are here.
For any additional question, please email the course instructor at luigi.acerbi@unige.ch.
Code and scripts in this repository are released under the terms of the MIT License.