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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

Neuroinformatics

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Code Completion Credits Range Language
BAM33NIN Z,ZK 6 2P+2C Czech
Vztahy:
During a review of study plans, the course A6M33NIN can be substituted for the course BAM33NIN.
It is not possible to register for the course BAM33NIN if the student is concurrently registered for or has already completed the course A6M33NIN (mutually exclusive courses).
It is not possible to register for the course BAM33NIN if the student is concurrently registered for or has already completed the course BEAM33NIN (mutually exclusive courses).
In order to register for the course BAM33NIN, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.
It is not possible to register for the course BAM33NIN if the student is concurrently registered for or has previously completed the course A6M33NIN (mutually exclusive courses).
It is not possible to register for the course BAM33NIN if the student is concurrently registered for or has previously completed the course BEAM33NIN (mutually exclusive courses).
Garant předmětu:
Daniel Novák
Lecturer:
Ján Antolík, Eduard Bakštein, Pavel Filip, Jiří Hammer, David Kala, Daniel Novák, Karla Štěpánová
Tutor:
Eduard Bakštein, Daniel Novák
Supervisor:
Department of Cybernetics
Synopsis:

The Neuroinformatics Course concentrates on modelling of neurons, stochastic learning on cellular level, information coding and decoding in brain and single unit processing. Examples from clinical practices are provided throughout the course. The labs focus on signal neuron analysis from human and animal brain.

Requirements:

Prerequisites: Signal Theory, Statistics and Reliability in Medicine, Pattern Recognition and Machine Learning.

Syllabus of lectures:

1. Introduction, how we can explore brain functions - single cell recording, functional neurosurgery, functional lesions, transcranial magnetic stimulation, local field potentials, surface EEG, methods of visualization of neuron activity.

2. Neuron Models: Equilibrium potential, Synapses, Spatial Structure: The Dendritic Tree, Ion Channels.

3. Poisson process, Spike train variability, Integrate & Fire model.

4. Point process in space and time, spike trains measures.

5. Neural encoding & decoding: Firing rates and spike statistics, information transmission in spikes.

6. Cellular learning mechanisms: short-term potentiation and long-term potentiation.

7. Rate based and spike based learning.

8. Stochastic neurons and learning I - what can we learn from mathematical statistics.

9. Stochastic neurons and learning II - what can we learn from mathematical statistics.

10. Organization and Modelling of Cortex.

11. Clinical application I - modelling of epilepsy.

12. Spike Sorting, signal preprocessing, clustering, evaluation, ROC analysis.

13. Clinical application II - single unit processing in Parkinson patients.

14. Reserve.

Syllabus of tutorials:

1. Neurons modelling, Hodgkin-Huxley model, coefficient of variation, PSTH histogram.

2. Poisson and point processes.

3. Signal coding in brain - temporal approach.

4. Signal coding in brain - frequency approach.

5. Decoding information.

6. Transmission of information, regularity measures, spike train metrics.

7. Statistics characteristics of neuron firing.

8. Rate based and spike based learning.

9. Artificial spike train generation.

10. Spike sorting.

11. Result evaluation- ROC, visualization.

12. Case study I: IAPS experiment in Parkinson patients.

13. Case study II: epilepsy.

14. Reserve.

Study Objective:

The course deals with data and application of computational models and analytical tools in the field of neurosciences.

Study materials:

[1] Christof Koch, Biophysics of Computation-Information Processing in Single Neurons, Oxford University Press, 1999.[2] Thomas P. Trappenberg, Fundamentals of Computational Neuroscience, Oxford University Press, 2002.

[3] Fred Rieke,Spikes Exploring the Neural Code, MIT Press, 1999.

[4] Peter Dayan, Theoretical Neuroscience, MIT Press, 2001.

[5] Wulfram Gerstner, Spiking Neuron Models, Cambridge University Press, 2002.

Note:
Further information:
https://cw.fel.cvut.cz/b232/courses/beam33nin/start
Time-table for winter semester 2023/2024:
Time-table is not available yet
Time-table for summer semester 2023/2024:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Wed
roomKN:E-112
Novák D.
Bakštein E.

12:45–14:15
(lecture parallel1)
Karlovo nám.
Cvičebna Vyčichlova
roomKN:E-230
Bakštein E.
16:15–17:45
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
Thu
Fri
The course is a part of the following study plans:
Data valid to 2024-05-29
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet5467506.html