Examining the role of spatial representations in navigation learning Computational Neuroscience

Description

Recent advances in reinforcement learning have demonstrated the ability of machine learning models to learn complex planning and navigation tasks. In parallel, modern brain research has uncovered the mechanisms and neural representations that underlay animal behavior in similar tasks. In this project we investigate the the neural representations that emerge in a behaving simulated reinforcement learning agent that interacts with a biologically inspired task. Different mechanisms that are known from machine learning, such as working memory are studied and compared to their biological counterpart.

The project can be scaled to fit a bachelor or master level thesis, and gives the opportunity to work with both, state-of-the art machine learning tools and integrating them with insights from modern neuroscience experiments. The project will be conducted in the Python programming language. Experience in python and Tensorflow are advantageous.

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.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

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