Description

Spatial learning is the process by which organisms acquire information about their surroundings and navigate through them. To learn specific paths and locations in a given environment, it is crucial to encode not only a map of the environment but also learn sequential and temporal relationships between locations, specifically the connections between past, current, and future locations.

Spatial learning is largely attributed to the hippocampus. Place cells are neurons in the hippocampus that become active when an organism is in a specific location whithin its environment. These cells are key components in the brain's navigation system, forming a cognitive map of the surroundings. Learning sequences of place cell activation for path learning is suggested to be mediated by two key mechanisms: synaptic plasticity, which allows to form patterns of connectivity in a neuronal layer, and temporal coding of activity by aligning the timing of spikes with local field potential (LFP) oscillations, such as theta rhythms.

Our project employs a closed-loop simulation model that integrates a spiking neural network and a behaving agent within a controlled environment. This setup allows us to study neural mechanisms involved in sequence learning for spatial navigation using biologically plausible models. We offer projects for bachelor or master theses to to further develop our model and/or explore the neural mechanisms involved in spatial learning and navigation.

Requirements

Python programming skills are required.

Contact

Julia Pronoza (julia.pronoza@ruhr-uni-bochum.de)