That’s the title of Larry Abbott’s recent perspective piece in Neuron (you can find the full article here).
It starts with an excellent discussion of the roll of theory in
neuroscience, and proceeds with a selective overview of the main
insights gained from modeling over the last 20 years.
Note by ‘theoretical neuroscience’ he isn’t talking about neurophilosophy, but mathematical modeling. He discusses the by now common distinction between word models and mathematical models:
possible to contemplate experimental results in such complex systems
without a model in one’s head?), but prior to the invasion of the
theorists, these were often word models. There are several advantages
of expressing a model in equations rather than words. Equations force a
model to be precise, complete, and self-consistent, and they allow its
full implications to be worked out. It is not difficult to find word
models in the conclusions sections of older neuroscience papers that
sound reasonable but, when expressed as mathematical models, turn out
to be inconsistent and unworkable. Mathematical formulation of a
model forces it to be self-consistent and, although self-consistency is
not necessarily truth, self-inconsistency is certainly falsehood.
It isn’t clear to me that equations force you to be consistent (a>0 and a<0 are both equations, after all). But inconsistencies in your equations will typically be discovered quite fast, especially if you try to use them for simulations. It would be interesting to see examples of word models that turned out to be inconsistent that Abbott mentions.
I think what makes
mathematical models so useful is not our ability to spot
inconsistencies (it isn’t trivial to find inconsistencies in a complicated mathematical model after all!). Rather, it is their ability to make
precise predictions that can be tested. It is also that they make all
assumptions and relevant variables explicit. It is harder to hide
bullshit in a set of differential equations than in a natural language
Note this topic of the difference between natural language analysis and mathematical analysis came up before, over at Brain Hammer in the post Math vs Natural Language.