The theoretical background of the master thesis is formed by the place and grid cells of the hippocampus, which are responsible for a wide variety of navigation tasks. This ranges from classical spatial navigation in a city or a building to abstract assignments in cognitive rooms, like the maximum speed of a vehicle based on engine power and weight. Since basic place cell firing patterns have already been investigated by machine learning, the thesis will focus on whether this method can also be used to process speech in order to draw conclusions about the involvement of place and grid cells in this domain. For this purpose, the theory of cognitive maps and its mathematical formulation the Successor Representation will be used.
To apply this concept to language, different techniques of Natural Language Processing as well as a neural network will be used. The former are mainly used to provide the training data for the network. These consist of successive pairs of words, one serving as input, the other as output. The goal is to infer the grammatical structure from the word-by-word predictions. To achieve this, several configurations are investigated, with the main focus on processing books that are used as a proxy for valid language data.