Modelling of syntactical processing in the cortex.

Probably the hardest test for a theory of brain function is the explanation of language processing in the human brain, in particular the interplay of syntax and semantics. Clearly such an explanation can only be very speculative, because there are essentially no animal models and it is hard to study detailed neural processing in humans. The approach presented in this paper uses well established basic neural mechanisms in a plausible global network architecture that is formulated essentially in terms of cortical areas and their intracortical and corticocortical interconnections. The neural implementation of this system shows that the comparatively intricate logical task of understanding semantico-syntactical structures can be mastered by a neural network architecture. The system presented also shows additional context awareness, in particular the model is able to correct ambiguous input to a certain degree, e.g. the input "bot show/lift green wall" with an artificial ambiguity between "show" and "lift" is correctly interpreted as "bot show green wall" since a wall is not liftable. Furthermore, the system is able to learn new object words during runtime.