What is the purpose of modelling in neuroscience? Ideally, it should always be able to verify a theory by experiment, but in practice not all experiments are technically feasible, or ethical, or manageable with practical resource limits. Experiments require careful construction to ensure the results have as few possible explanations as possible when attempting to verify a theory: to do so requires a a mental model of the underlying processing. Building computerised models will clarify and formalise the theories used. The true test for the success of a model is often held to be not just ability to mimic results, but successful prediction of experimental results. The complexity of the underlying system requires theorists and modellers alike to assume simplifications. At the other extreme, attempts to incorporate every feature of a system translates to an excessive parameter search space which increases the scope of results. This inevitably increases the probability of including the desired outcomes without being meaningful.Yet perhaps the underlying power of the suppositions incorporated could also perhaps be evaluated given the appropriate computational framework. The main aim of this project is to establish a framework for evaluating biological principles. Understanding how important these principles may be by incorporating them interactively into a model may give insight into the priorities that have shaped a system: not just how it works, but also why. For example,neurons should remain within non-toxic ring ranges, yet an underutilised neuron may die or be recruited to another system. Therefore the system must load balance effectively, but what priority this remains in the face of threats could be explored.