In this advanced course the concept of hierarchy is going to be explored from the point of view of theoretical and experimental neuroscience as well as from a machine learning perspective. In lecture-driven talks we will review what is known and discuss which are the open and most relevant questions at the field. Specifically, how theoretical neuroscience and machine learning integrate hierarchy to describe neuronal networks and which anatomical and behavioral data supports the existence of such in the nervous system.
Nano course: Henning Sprekeler
Title: How not to build a model, painful lessons learned over the years
Affiliation: Bernstein Center for Computational Neuroscience, Berlin, Germany
Lab website
Additional information: Sprekeler’s group currently has a focus on the plasticity and function of inhibition. His lab has also done work on plasticity and learning, perceptual decision making, grid cells, memory, or a combination of all of the above.
