Lecturer(s)
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Hrdý Radek, doc. Ing. Ph.D.
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Course content
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1. Introduction - neuroinformatics as a discipline, definition and context of the field. 2. Linear space, linear dependence and independence. Bases and dimensions of space. 3. Concept of graph, oriented and non-oriented graphs, paths and distances in graphs. 4. Theory of organization and functioning of biological neural networks, an overview of basic concepts of neuroscience. 5. Basic computer models of biological and artificial neurons. 6. Basic computer models of neural networks. 7-8. Impulse and artificial neural networks - a comparison of concepts, an overview of simulation options and software simulators. 9-10. Data and metadata standards in neuroinformatics. 11. Brain-computer interface systems - concept and use. 12. Current challenges of neuroinformatics. Global neuroinformatics initiatives, projects and infrastructures.
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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 computer models and simulations - describe different models of neurons and neuronal structures - understand the differences and interconnections of existing neuroscience models and computer simulations of real neural systems with the world of machine learning and artificial intelligence - identify differences, common features and areas of application of spiking and artificial neural networks - to be familiar with current data and metadata standards used in neuroinformatics - describe the basic concept of the brain-computer interfaces - to be familiar with current trends in neuroinformatics with regard to existing global initiatives, infrastructures and projects |
Skills |
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- use a selected software simulator and simulate a simple neural network - make a simple program modification in simulations of neural networks - identify and read the selected neuroinformatics data/metadata standard - perform selected practical activities in setting up/controlling the brain-computer interface |
Competences |
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N/A |
N/A |
- read and critically evaluate a simple scientific text |
teaching methods |
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Knowledge |
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Lecture |
Lecture with visual aids |
E-learning |
Multimedia supported teaching |
Textual studies |
Project-based instruction |
Practicum |
Skills |
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Task-based study method |
Practicum |
Project-based instruction |
Individual study |
Students' portfolio |
Competences |
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Lecture |
Lecture with visual aids |
E-learning |
Practicum |
Project-based instruction |
Individual study |
Students' portfolio |
Task-based study method |
assessment methods |
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Knowledge |
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Combined exam |
Project |
Skills |
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Project |
Skills demonstration during practicum |
Group presentation at a seminar |
Competences |
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Combined exam |
Project |
Skills demonstration during practicum |
Group presentation at a seminar |
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. Galén, 2016. ISBN 9788074922831.
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