Computational neuroscience is a discipline that has some overlap with scientists and engineers. Scientists in general will conduct experiments to gain a better understanding of a problem, while engineers will seek to solve a problem. Modeling uses much of the data that is derived from scientific experiments to provide data for parameters, connectivity, chemical composition and morphology. Using this data engineers can implement models that can, in some specified way, mimic the behavior of a particular neuron. The model can provide some insight into the behavior of said neuron, such that new questions can be raised that lead to more experiments. This is a very productive feedback loop that helps further the field of neuroscience as a whole. As such, the computational neuroscientist must sit squarely between the two trains of thought.