Description

Spatial navigation is a fundamental ability essential for the survival and reproduction of many organisms. Effective navigation relies heavily on memory, allowing an organism to recall important locations and find them again. This project combines neuroscience and machine learning to explore how these navigational and memory processes work.

Our research uses reinforcement learning (RL) to model how artificial agents navigate and form memories. By training RL agents in various simulated environments to perform navigation tasks, similar to how animals explore their surroundings to find rewards, we aim to understand how memories are encoded, stored, and retrieved. We then compare these artificial memory mechanisms with those observed in biological systems.

We offer bachelor and master projects focused on developing RL agents, analyzing their memory dynamics, and exploring the underlying neural mechanisms.