Teaching methods
- 16 hrs lecture
- 16 hrs problem session
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Pre-requisites
Introduction Biophysics
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Objectives
- The student can use methods of linear system theory and Poisson statistics to analyse neural spike trains, both theoretically and through computer simulation
- The student knows the Bayesian approach to classification and ROC, Fisher information en Cramer-Rao bound and can apply these to describe the function of neurons
- The student knows the concepts entropy, mutual information and entropy maximization as a principle for neural coding
- The student knows the behaviour and function of feed-forward and recurrent neural networks
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Contents
This course will present various models for neural networks and for learning in the brain. Principles for optimal storage of information and for learning will be introduced using concept from information theory and statistics. This will be done for feed-forward neural networks and for recurrent neural networks. The behavior of the neural networks will be discussed with implications for understanding of biological neural networks, as well as applications of the basic principles of neuronal information processing for pattern recognition.
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Examination
Gewogen middeling van: - het schriftelijk tentamen
- de werkcollege opgaven
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Literature
P. Dayan and L.F. Abott, Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems, MIT Press, Cambridge Massachussetts, edition 2005
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Extra information
The course is part of the Neuroscience Minor for Physics, Mathematics, and an obligatory course for the biophysics students of Natural Science
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