SylabUZ
| Course name | Elements of neuroscience |
| Course ID | 13.1-WF-FizD-EN-S17 |
| Faculty | Faculty of Physics and Astronomy |
| Field of study | Physics |
| Education profile | academic |
| Level of studies | Second-cycle studies leading to MS degree |
| Beginning semester | winter term 2018/2019 |
| Semester | 4 |
| ECTS credits to win | 4 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
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.
| Outcome description | Outcome symbols | Methods of verification | The class form |
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
Modified by dr hab. Piotr Lubiński, prof. UZ (last modification: 28-06-2018 22:40)
