Computational Neuroscience

- Understanding of basic concepts in high school level mathematics, biology, chemistry, and physics.

- Understanding of basic concepts in statistics and statistical inference.

- Basic programming skills in Python (or strong willingness to catch up).

przedmiot fakultatywny

- In class tutorial and laboratory: 30 hours.

- Self study, literature review, homework preparation: 60 hours.

- Final (data analysis) project preparation: 30 hours.

Student knows and understands:

- principles of scientific process in the context of computational neuroscience (aims and methods of their achevement);

- mathematical underpinnings of selected methods of data analysis (incl. data dimensionality reduction such as PCA and ICA);

- principles of literate programming and reproducible research;

- contemporary research problems considered in computational neuroscience;

- rules for disseminating the results of scientific research including free and open reproducible research;

Student is able to:

- use tools and online services that enable sharing of large datasets (neuroimaging, neurophysiological, electrophysiological);

- acquire, manage, share and publish datasets using standard GNU tools such as scp, rsync, as well as, more advanced code and data versioning tools such as git and datalad;

- use Python language and specialized packages/modules to perform analysis on neuroimaging data;

- use frameworks such as Anaconda and Jupyter to setup independent computational environments for specific tasks;

- select data acquisition and analysis methods appropriate for given research goals and questions;

- design scientific computing plan taking into account available time, computational power, data storage capacity and RAM;

- design specific research protocol taking into account specific research goals, as well as, strengths and weaknesses of specific neuroimaging methods;

Student is able to appreciate:

- the need to conduct reproducible research and publish

research results, as well as, raw data and code used for their analysis.

- consequences of computational neuroscience development

and potential research results in broader context of

contemporary society, philosophy and civilization.

see below

- pokaz

- opis
- pogadanka
- wykład problemowy

- ćwiczeniowa
- doświadczeń
- giełda pomysłów
- klasyczna metoda problemowa
- laboratoryjna
- obserwacji
- projektu
- seminaryjna
- studium przypadku

- metody oparte na współpracy

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.

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.

- In class activity; 30%;

- Homeworks: 30%

- Final project 40%;