Cognitive neuroscience constantly works to find the appropriate level of description (or, in the case of computational modeling, implementation) for the topic being studied. The goal of this post is to elaborate on this point a bit and then illustrate it with an interesting recent example from neurophysiology.
As neuroscientists, we can often choose to talk about the brain at any of a number of levels: atoms/molecules, ion channels and other proteins, cell compartments, neurons, networks, columns, modules, systems, dynamic equations, and algorithms.
However, a description at too low a level might be too detailed, causing one to lose the forest for the trees. Alternatively, a description at too high a level might miss valuable information and is less likely to generalize to different situations.
For example, one might theorize that cars work by propelling gases from their exhaust pipes. Although this might be consistent with all of the observed data, by looking “under the hood” one would find evidence that this model of a car’s function is incorrect.