SylabUZ
| Nazwa przedmiotu | Elements of neuroscience |
| Kod przedmiotu | 13.1-WF-FizD-EN-S17 |
| Wydział | Wydział Fizyki i Astronomii |
| Kierunek | Fizyka |
| Profil | ogólnoakademicki |
| Rodzaj studiów | drugiego stopnia z tyt. magistra |
| Semestr rozpoczęcia | semestr zimowy 2018/2019 |
| Semestr | 4 |
| Liczba punktów ECTS do zdobycia | 4 |
| Typ przedmiotu | obowiązkowy |
| Język nauczania | angielski |
| Sylabus opracował |
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| Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
| Wykład | 30 | 2 | - | - | Egzamin |
| Laboratorium | 30 | 2 | - | - | Zaliczenie na ocenę |
To familiarize the student with the theoretical, computational and practical elements of neuroscience. Preparation for work at a neurosciences laboratory either in a medical healthcare center or a research facility.
Knowledge of the elements of probability theory, programming and mathematical methods of biophysics. Elements of the physiology of the brain. The ability to programming in either Python or R
Neuron and conductance based models.
Simplified neuron and population models
Spike time variability
Associatiors and synaptic plasticity
Large volume data analysis in bioinformatics / big data in bioinformatics
Basic network models
Fast, freed forward maping networks
Self organizing network architectures and genetic algorithms
Statistical methods in neuroscience
Chaotice networks
In the laboratory the students will carry out programming exercises covering the above topics in the Python or R programming languages.
Lectures on problems and discussions. Laboratory, programming assignments and projects.
| Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
LECTURE: A course credit for the lectures is obtained by taking a final exam composed of tasks of varying degrees of difficulty.
Laboratory: During the laboratory the students will be given a series of open-ended projects covering the lectures.
Credit will consist of 40% the result of the exam and 60% of the grades achieved for the laboratory projects.
[1] Thomas Trappenberg, Fundamentals of Computational Neuroscience 2nd Edition
[2] Peter Dayan, Laurence F. AbbottTheoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series) Revised ed. Edition
Zmodyfikowane przez dr hab. Piotr Lubiński, prof. UZ (ostatnia modyfikacja: 28-06-2018 22:40)
