Typical steps in a neural network modeling study are the definition of a particular cognitive phenomenon, the creation and definition of the network model by specifying the neural architecture, activation functions and the learning rules. One then sets out to test
how well the network model matches up with the phenomenon, and to the extent that it does and is parsimonious, and has neural plausibility, it is a good model.
It is then not difficult to imagine how we can do the same thing with the brain by treating it as a neural model that's already built, and we're just trying to discover its architecture, its activation functions and its learning rules. Thus, we run the brain through simulations, observe the input and resulting output, and hypothesize the parameters that led to the observation.
We can then reverse engineer these parameters into the model (which is what we do anyway), and again, test how good the model is.