Computational Neuroscience
Informacje ogólne
Kod przedmiotu: | 2401-CS-CN-s2 |
Kod Erasmus / ISCED: |
(brak danych)
/
(0610) Information and Communication Technologies (ICTs)
|
Nazwa przedmiotu: | Computational Neuroscience |
Jednostka: | Katedra Kognitywistyki |
Grupy: |
Cognitive Science s2 - I i II rok przedmioty do wyboru |
Punkty ECTS i inne: |
4.00
|
Język prowadzenia: | angielski |
Wymagania wstępne: | - 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). |
Rodzaj przedmiotu: | przedmiot fakultatywny |
Całkowity nakład pracy studenta: | - In class tutorial and laboratory: 30 hours. - Self study, literature review, homework preparation: 60 hours. - Final (data analysis) project preparation: 30 hours. |
Efekty uczenia się - wiedza: | 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; |
Efekty uczenia się - umiejętności: | 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; |
Efekty uczenia się - kompetencje społeczne: | 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. |
Metody dydaktyczne: | see below |
Metody dydaktyczne eksponujące: | - pokaz |
Metody dydaktyczne podające: | - opis |
Metody dydaktyczne poszukujące: | - ćwiczeniowa |
Metody dydaktyczne w kształceniu online: | - metody oparte na współpracy |
Skrócony opis: |
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. |
Pełny opis: |
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. |
Metody i kryteria oceniania: |
- In class activity; 30%; - Homeworks: 30% - Final project 40%; |
Zajęcia w cyklu "Semestr zimowy 2022/23" (zakończony)
Okres: | 2022-10-01 - 2023-02-19 |
Przejdź do planu
PN CW
WT ŚR CZ PT |
Typ zajęć: |
Ćwiczenia, 30 godzin
|
|
Koordynatorzy: | Jan Nikadon | |
Prowadzący grup: | Jan Nikadon | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Zaliczenie na ocenę
Ćwiczenia - Zaliczenie na ocenę |
Zajęcia w cyklu "Semestr zimowy 2023/24" (zakończony)
Okres: | 2023-10-01 - 2024-02-19 |
Przejdź do planu
PN WT ŚR LAB
CZ PT |
Typ zajęć: |
Laboratorium, 30 godzin
|
|
Koordynatorzy: | Jan Nikadon | |
Prowadzący grup: | Jan Nikadon | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Zaliczenie na ocenę
Laboratorium - Zaliczenie na ocenę |
Właścicielem praw autorskich jest Uniwersytet Mikołaja Kopernika w Toruniu.