Neuroinformatics

Relations:

It is not possible to register for the course BEAM33NIN if the student is concurrently registered for or has already completed the course BAM33NIN (mutually exclusive courses).

It is not possible to register for the course BEAM33NIN if the student is concurrently registered for or has already completed the course A6M33NIN (mutually exclusive courses).

In order to register for the course BEAM33NIN, 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 BEAM33NIN if the student is concurrently registered for or has previously completed the course BAM33NIN (mutually exclusive courses).

Course guarantor:

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/wiki/courses/beam33nin/start

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

	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. roomKN:E-230 Bakštein E. 16:15–17:45 (lecture parallel1 parallel nr.101) Karlovo nám.
Thu
Fri

The course is a part of the following study plans:

• Medical Electronics and Bioinformatics - Specialization Image Processing (compulsory elective course)
• Medical Electronics and Bioinformatics - Specialization Signal Processing (compulsory elective course)
• Medical Electronics and Bioinformatics - Specialization Bioinformatics (compulsory elective course)
• Medical Electronics and Bioinformatics - Specialization Medical Instrumentation (compulsory elective course)