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Course

NX-414: Brain-like computation and intelligence

Summary

Recent advances in machine learning have contributed to the emergence of powerful models of animal perception and behavior. In this course we will compare the behavior and underlying mechanisms in these models as well as brains.

Lectures

Instructors (2)

Alexander Mathis

Alexander studied pure mathematics with a minor in logic and theory of science at the Ludwig Maximilians University in Munich. For his PhD also at LMU, he worked on optimal coding approaches to elucidate the properties of grid cells. As a postdoctoral fellow with Prof. Venkatesh N. Murthy at Harvard University and Prof. Matthias Bethge at Tuebingen AI, he decided to study olfactory behaviors such as odor-guided navigation, social behaviors and the cocktail party problem in mice. During this time, he increasingly got interested sensorimotor behaviors beyond olfaction and started working on proprioception, motor adaption, as well as computer vision tools for measuring animal behavior. In his group, he is interested in elucidating how the brain gives rise to adaptive behavior. One of the major goals is to synthesize large datasets into computationally useful information. For those purposes, he develops algorithms and systems to analyze animal behavior (e.g. DeepLabCut), neural data, as well as creates experimentally testable computational models.

Martin Schrimpf

Official source

https://edu.epfl.ch/coursebook/en/brain-like-computation-and-intelligence-NX-414

Moodle Page

http://go.epfl.ch/NX-414

Summary

Lectures

Instructors (2)

Alexander Mathis

Martin Schrimpf

Official source

https://edu.epfl.ch/coursebook/en/brain-like-computation-and-intelligence-NX-414

Moodle Page

http://go.epfl.ch/NX-414

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