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 are the anatomical and behavioral data supporting the existence of such in the nervous system. Participants will be presented with a historical frame of reference on hierarchy in neural networks (day one), hierarchical coding or hierarchy of sensory systems (day two) and finally hierarchical neural networks in the context of behavior (or solving a task/action/decision making).
From artists, to writers, to philosophers, to scientists, to experts and dilettantes alike, our species as a whole has sought to understand the nature of emotions for centuries. The reason for this is multifaceted and includes the intrinsically curious nature of our kind and the fact that the experience of emotions is one of the most familiar phenomenon to us.
With this advanced course, our aim is to give room for discussions on how the brain and different peripheral systems influence each other. More specifically, we will ask how the digestive system, the gut-microbiome and the immune system shape, and are shaped, by the brain and the behaviour it produces. What is the evidence for these interactions? What are their possible underlying mechanisms?
Our lecturers will tell us how the hippocampus contributes to memory-guided decision-making, from imagining possible actions to integrating contextual information, and they will give us hands-on experience in analyzing data from hippocampal recordings.
Since the 1990s, Reinforcement Learning has been pivotal in providing ideas for models of learning and decision making.
The most successful example is the understanding the activity of phasic dopaminergic neurons, but there are many more neural and behavioral applications.
It is the aim of this course to give the foundations of the models, with a hands-on approach, while making the connection to relevant concepts in psychology and neuroscience.