| The goal is to provide the neurobiological background and the mathematical/computational skills necessary to understand current technical publications in the field of Brain networks and neuronal communication. |
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This course covers the experimental methods to assess connectivity; data analysis methods to extract connectivity from dynamics; statistical models for connectivity and dynamical models that reproduce the observed coherent brain states.
The brain is all about communication, between individual neurons as well as between brain areas comprised of billions of neurons. To study brain communication advanced new experimental and mathematical techniques have been developed, which require mathematical sophistication and a neurobiological background to fully comprehend.
Instructional Modes
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(Physics master): Neurophysics 1 and Neurophysics 2.
Students in other programs (Artificial Intelligence, Research Master Cognitive Neuroscience) that do not satisfy these requirements need approval of the course instructor to enroll. They need to demonstrate sufficient math and neurophysics background.
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| The final grade will be based on your class/practice hours participation, handed-in homework and a final exam |
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