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After successful completion of the course
- the student is able to calculate the response of a neuron or of a network of neurons to various inputs, both analytically and by computer simulations
- the student should be able to apply basic principles from Information Theory and Non-linear Systems analysis to quantify information processing by networks of neurons and to determine the attraction domain and stable states of a network of neurons.
- the student should be able to discuss the functional role (if any) of oscillations, neuronal correlations and neuronal variability based the homework problems and assigned literature
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The aim of this course is to give a theoretical description of the neuronal dynamics at the level of a single neuron and at the population level. The theoretical model will be used to explain the information processing and the storage and retrieval of information by populations of neurons. We will cover the following topics: nonlinear dynamics of neurons and systems of neurons, population coding in combination with Fisher information, information theory, neural-mass models and models and function of oscillations.
Instructional Modes
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For students in track Neurophysics: Neurophysics 1 and Neurophysics 2.
For student in other programs (Artificial Intelligence, Research Master Cognitive Neuroscience) that do not satisify these requirements need permission of the instructor to enroll. They will need to demonstrate sufficient background in math (nonlinear dynamics) and neurophysics.
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| Written examination with practical exercises during the course. |
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