Computational Neuroscience 2401-CS-MF-CN-s2

The aim of this subject is to familiarize students with practical/technical
and theoretical/methodological aspects of contemporary computational
neuroscience (CN).

Problem based approach to learning will be prioritized with particular focus
on "understanding by making things done".
We will focus on data acquisition, management and analysis.
Course participants will be required
to read recent scientific publications in the field of CN,
participate in practical sessions of fMRI
and/or EEG data acquisition and perform data analysis project.

Three main areas will be considered.
1. Tools, frameworks and environments for data management:
- GNU/Linux and principles of Unix design and use philosophy;
- shell, bash, zsh;
- ssh, rsync, git, datalad;
- virtualization and containerization (Docker, Singularity);
- using remote servers for data storage and scientific computing (i.e.,
high performance computing, HPC);
2. Tools, frameworks and environments for data analysis:
- R, Python;
- Anaconda;
- Jupyter Notebooks;
- org-mode;
- Pandas, NumPy, NiBabel, NiPyPe, MNE, FSL;
- Data visualization and exploration
- Neuroimaging data quaity assurance;
- Literate programming and reproducible research;
3. Tools, techniques and protocols for data acquisition:
- behavioral data (e.g., PsychoPy);
- neuroimaging, neurophysiological, electrophysiological
(i.a., fMRI, EEG, TMS);
- four practical sessions in data acquisition using EEG and/or fMRI.