Triesch Lab - Teaching


Summer Semester 2020 - Theoretical Neuroscience II

Theoretical Neuroscience II is building on the course Theoretical Neuroscience I. Topics include neural plasticity, network dynamics, criticality, pattern formation and self-organization in neural circuits, reservoir computing, unsupervised learning, reinforcement learning, active efficient coding

Summer Semester 2019 - Reinforcement Learning

Reinforcement Learning is a research field at the intersection of Computer Science/Engineering (machine learning, artificial intelligence, control theory, robotics) and Neuroscience/Psychology (reward systems, human and animal learning, emotion).

Visualisation of a neural network

Winter Semester 2018/2019 - Theoretical Neuroscience I

This course provides an introduction to modern theoretical neuroscience with an attempt to cover all relevant spatial scales (from molecules to brain areas) as well as temporal scales (sub-millisecond to evolutionary timesscales).