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Lecturer(s)
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Mouček Roman, doc. Ing. Ph.D.
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Course content
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Neuroinformatics as a discipline - definition and context of the field. Theories of organization and functioning of biological neural networks - overview of fundamental neuroscience concepts. Basic computational models of biological and artificial neurons and neural networks. Fundamentals of electroencephalography (EEG) and EEG experiments. Lifecycle of neuroinformatics data - from acquisition to publication; concepts of open science. Infrastructure for neuroinformatics research - data and metadata standards, ontologies, and FAIR principles. Preprocessing and analysis of electroencephalographic data, including machine learning and deep learning methods. Brain-computer interface (BCI) systems and their paradigms. Global neuroinformatics infrastructures, initiatives, projects, and current trends.
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Learning activities and teaching methods
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- Contact hours
- 13 hours per semester
- Practical training (number of hours)
- 26 hours per semester
- Team project (50/number of students)
- 50 hours per semester
- Preparation for an examination (30-60)
- 30 hours per semester
- unspecified
- 12 hours per semester
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| prerequisite |
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| Knowledge |
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| - be familiar with high school mathematics - understand the basic concepts of artificial intelligence - understand the basic principles of programming language and programming - be familiar with the basic concepts associated with the organization and functioning of biological neuronal systems (human brain) |
| Skills |
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| - apply knowledge of high school mathematics in common tasks - explain the basic concepts of artificial intelligence - create a simple computer program in at least one programming language - describe the basic structure and functioning of biological neuronal systems (human brain) |
| Competences |
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| N/A |
| N/A |
| N/A |
| N/A |
| N/A |
| learning outcomes |
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| Knowledge |
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| - understand selected theories from neuroscience and their computational models and simulations, - describe different models of neurons and neuronal structures, - explain relationships and connections between biological neuronal models and methods of machine learning and artificial intelligence, - be familiar with current standards used in neuroinformatics, - describe basic concepts of brain-computer interfaces (BCI) and their applications - identify current trends in neuroinformatics, including global initiatives, infrastructures, and projects, |
| Skills |
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| - identify and read the selected neuroinformatics data/metadata standard - process and evaluate a neuroinformatics experiment using electroencephalographic (EEG) data. |
| Competences |
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| N/A |
| N/A |
| - read and critically evaluate a relevant scientific text |
| teaching methods |
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| Knowledge |
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| Lecture |
| Lecture with visual aids |
| Textual studies |
| Project-based instruction |
| Students' portfolio |
| Practicum |
| Task-based study method |
| Skills |
|---|
| Task-based study method |
| Practicum |
| Project-based instruction |
| Individual study |
| Students' portfolio |
| Laboratory work |
| Competences |
|---|
| Lecture |
| Lecture with visual aids |
| Practicum |
| Project-based instruction |
| Individual study |
| Students' portfolio |
| Task-based study method |
| assessment methods |
|---|
| Knowledge |
|---|
| Combined exam |
| Project |
| Skills |
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| Project |
| Skills demonstration during practicum |
| Group presentation at a seminar |
| Competences |
|---|
| Combined exam |
| Project |
| Skills demonstration during practicum |
| Group presentation at a seminar |
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Recommended literature
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František Koukolík. Já : o mozku, vědomí a sebeuvědomování 2., přeprac. a dopl. vyd.. Praha: Karolinum, 2013. ISBN 978-80-246-2249-1.
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Jiří Horáček, Cyril Höschl, Ladislav Kesner, Filip Španiel. Mozek a jeho člověk, mysl a její nemoc. Praha: Galén, 2016. ISBN 9788074922831.
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