A robust model of the neuronal regulator of the lower urinary tract based on artificial neural networks

The neuronal regulator of the lower urinary tract is a very complex nervous system that consists of a heterogeneous group of neuronal centres. We have developed a new system from a model based in a multi-agent system in which each neuronal centre corresponds with an agent. This system incorporates a heuristic in order to make it more robust in the presence of possible inconsistencies. The heuristic used is based on a neural network (orthogonal associative memory). Knowledge through training has been added to the system, using correct patterns of behaviour of the urinary tract and behaviour patterns resulting from dysfunctions in two neuronal centres as a minimum. The experiments prove that the model is robust and its functioning coincides with the behaviour of the biological system. This work fulfils the expectations of providing a model of the regulator system that allows breaking the problem into simple modules each with its own entity.