A new neural network model - the GasNet - has been recently reported in the literature, which, in addition to the traditional electric type, point-to-point communication between units, also uses communication through a diffilsable chemical modulator. Here we assess the applicability of this model in three different scenarios, the XOR problem, a food gathering task for a simulated robot, and a docking task for a virtual spaceship. All of them represent discrete domains, a contrast with the one where the GasNet was originally introduced, which had an essentially continuous nature. These scenarios are well-known benchmark problems from the literature and, since they exhibit varying degrees of complexity, they impose distinct performance demands on the GasNet. The experiments were primarily intended to better understand the model, by extending the original problem domain where GasNet was introduced. The results reported point at some difficulties with the current GasNet model.
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