- The course cannot be taken simultaneously with:
- Neuroinformatics (BAM33NIN)
- Department of Cybernetics
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.
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.
- 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.
- Study Objective:
The course deals with data and application of computational models and analytical tools in the field of neurosciences.
- Study materials:
 Christof Koch, Biophysics of Computation-Information Processing in Single Neurons, Oxford University Press, 1999. Thomas P. Trappenberg, Fundamentals of Computational Neuroscience, Oxford University Press, 2002.
 Fred Rieke,Spikes Exploring the Neural Code, MIT Press, 1999.
 Peter Dayan, Theoretical Neuroscience, MIT Press, 2001.
 Wulfram Gerstner, Spiking Neuron Models, Cambridge University Press, 2002.
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: