Modeling Memory Mechanisms in Spatial Navigation Computational Neuroscience


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.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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