Spatial navigation is a common ability among animals because almost all animals are required to move from one location to another for survival and reproduction. Therefore it is not surprising that many neural populations in the brain of mammals exhibit activity that is correlated with spatial variables. Computational modeling studies have suggested how patterns of neural activity emerge given the biological constrains of neuronal networks in the hippocampal formation. However, much less is known about the functional role of different aspects of neural spatial representations in driving complex spatial learning and navigation that have been studied in parallel in behavioral experiments. Here, we built on an existing computational modeling tool-chain and extended it to study neuronal activity in the hippocampal formation and their effect on spatial learning and navigation in a closed-loop simulation. We are developing a closed-loop system associating neural activity to behavior and vice versa. The neural network in our system is biologically realistic, therefore, its predictions potentially improve our understanding of the particular brain regions we include in our system. We use spiking neurons as building blocks of the neural network and for simulating this neural network we use NEST, which is a widely used simulator in computational neuroscience community worldwide (www.nest-simulator.org). We offer projects at bachelor and master level to further develop this system incrementally by adding neural networks that mimic the activity of particular brain regions that are involved in particular functions. These functions include (but not limited to) reward learning (reinforcement learning in biology), goal-directed spatial navigation and decision making.
Very good programming skill is required (preferably in Python).