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).
In this workshop we would like to create a forum for discussion about several topics related to variability in biological systems and in the brain. We feel that these issues are still open and general enough that approaches from any one field can potentially be valuable in others. We are, thus, encouraging a diversity of themes and perspectives rather than a focus on any of them in particular.
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.