Modeling communication processes in the human connectome through cooperative learning

Communication processes within the human brain at different cognitive states are neither well understood nor completely characterized. We assess communication processes in the human connectome using ant colony-inspired cooperative learning algorithm, starting from a source with no a priori information about the network topology, and cooperatively searching for the target through a pheromone-inspired model. This framework relies on two parameters, namely pheromone perception and edge perception, to define the cognizance and subsequent behaviour of the ants on the network and, overall, the communication processes happening between source and target nodes. Simulations obtained through different configurations allow the identification of path-ensembles that are involved in the communication between node pairs. These path-ensembles may contain different number of paths depending on the perception parameters and the node pair. In order to assess the different communication regimes displayed on the simulations and their associations with functional connectivity, we introduce two network measurements, effective path-length and arrival rate. These communication features are tested as individual as well as combined predictors of functional connectivity during different tasks. Finally, different communication regimes are found in different specialized functional networks. Overall, this framework may be used as a test-bed for different communication regimes on top of an underlaying topology.