What’s Computational about Computational Neuroscience?
When non-scientists ask me what I studied for my PhD, if I want to put a complete halt to the conversation, I answer ‘computational neuroscience.’
To continue, for those readers who have not already clicked away, the usual response (eye’s glazing over) is something like ‘wow,’ followed by a quick deflection to another topic. So, I’ve learned instead to start with ‘neuroscience,’ which turns off fewer people (though the number is still substantial). It seems there is something about the word ‘computational’ that just pushes into incomprehensibility for many people.
So what is computational about computational neuroscience? The short answer is that it means thinking about how brains are computers. Notice that I didn’t say brains are ‘like’ computers. Many people think that when scientists talk about brains as computers they are speaking in a kind of metaphor, but that’s not the case. Still, they are not saying that brains are literally ‘silicon microprocessor based generic math devices.’ They are saying that brains are literally ‘things that compute.’ And that’s not just a semantic dodge; it comes from Alan Turing, who originally defined a conceptually simple machine that could theoretically calculate pretty much anything. This led to the concept of computational equivalence, meaning that anything (mechanical, electronic or biological) that can do the elementary steps that a Turing Machine can do is qualitatively similar: they are all computers. One caveat is that Turing’s machine had an infinite memory and an infinite lifespan, so all physical computers are only approximations of the ideal. Actual computers, as opposed to theoretical ones, can be quite different from one another. A particular mathematical operation might be completed by one computer in a nanosecond while requiring another to chug along for a hundred million years, but they are both still computers in the Turing sense.
In other words, brains are computers because they can do elemental mathematical/logical operations. Other systems can also be described by equations too, but they are not considered computers because they do not generalize. For example, a falling object can be described by equations, and the path of the object can be considered a solution to those equations. But the falling object can only really solve one problem (perhaps with different initial conditions), while computers have abstract representations (neural codes) that can be used to do general purpose problem-solving. The internal states of the computer can stand for different things at different times, providing an essentially infinite flexibility.
Saying that brains are computers is not the same thing as saying that brains are only computers (a confusion found often in criticisms of the BRAIN Initiative). Still, this computational view of brains makes some people very uncomfortable. For obvious reasons this discomfort is less acute when we are describing animal brains, but it becomes quite an issue when we start talking about human gray matter. It’s related to the discomfort that people have with the idea that the mental states are in fact brain states, that the mind is actually the brain, the philosophical/theoretical position called materialism (in opposition to the idea that the ‘soul’ or ‘mind’ is somehow distinct from the brain, called dualism). People imagine that talking about brains as computers implies that we are all mindless automatons, subject to the sort of ‘does not compute’ rigidity that Captain Kirk always seemed to use to cause errant robots to self-destruct (puff of smoke out the ears and all). In truth, whatever you may think of the colossal literalness of your computer’s insistence on overwriting the file you intended to actually save, computational theory explores a much more flexible idea of computers. This model has proven delightfully fruitful for understanding all sorts of neural systems, even those that presume to wrestle with their own existence.