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Mathematical models of biological systems provide understanding and predictive power. However, as more information about a particular system is collected, its model becomes increasingly complicated. This makes the models better, because they are becoming more realistic and therefore potentially more useful, but in turn they are becoming harder to simulate and analyse. The aim of this research is to overcome this problem by using recently-developed, so-called 'equation-free' techniques for the study of complex systems, concentrating on a variety of model neural networks. In particular we will study the respiratory neural system. This complex system regulates breathing by sending electrical signals to the diaphragm and other respiratory muscles, causing them to periodically contract and relax. Equation-free techniques allow one to automatically simplify a complex system such as the respiratory neural network by using judiciously designed computer simulations to capture the 'essence' of the original complex system in terms of the correct variables. This provides insights and understanding that cannot be found in any other way.