Neuroinformatics

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.

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

The course uses grades A-F. Work during the semester 60%, final written and oral exam 40% (both parts of the exam are online for EuroTeQ students). For details, see the course website: https://cw.fel.cvut.cz/wiki/courses/bam33nin/start

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

• [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.

Lectures, lab-work, homeworks