The expression "Computational neuroscience" reflects the possibility of generating theories of brain function in terms of the information-processing properties of structures that make up nervous systems. It implies that we ought to be - able to exploit the conceptual and technical resources of computational research to help find explanations of how neural structures achieve their effects, what functions are executed by neural structures, and the nature of representation by states of the nervous system.
The expression also connotes the potential for theoretical progress in cooperative projects undertaken by neurobiologists and computer scientists. This collaborative possibility is crucial, for it appears that neither a purely bottom-up strategy nor a purely top-down strategy for explaining how the brain works is likely to be successful. With only marginal caricature, one can take the purely bottom-up strategy as recommending that higher-level functions can be neither addressed nor understood until all the fine-grained properties of each neuron and each synapse are understood. But if, as it is evident, some properties are network effects or system effects, and in that sense are emergent properties, they will need to be addressed by techniques appropriate for higher levels and described by theoretical categories suitable to that level. Assuming there are system properties or network properties that are not accessible at the single-unit level, then knowing all the fine-grained detail would still not suffice to explain how the brain works.