Subject description - BEAM33NIN

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BEAM33NIN Neuroinformatics
Roles:PV Extent of teaching:2P+2C
Department:13133 Language of teaching:EN
Guarantors:Novák D. Completion:Z,ZK
Lecturers:Too many persons Credits:6
Tutors:Bakštein E., Novák D. Semester:L

Web page:


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.

Study targets:

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

Course outlines:

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.

Exercises outline:

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.


[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.


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


Neurons modelling, Signal coding and decoding in brain, tatistics characteristics of neuron firing

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEBIO2_2018 Medical Instrumentation PV
MEBIO4_2018 Signal Processing PV
MEBIO1_2018 Bioinformatics PV
MEBIO3_2018 Image Processing PV

Page updated 18.6.2024 07:51:48, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)