Connectome by Sebastian Seung Page B

reveals little about function, but we look at it anyway because that’s what we can see with existing technologies.
    To understand the failings of phrenology, can we compare with a more successful example of relating function to size? Instead of investigating whether brainy people are smarter, let’s ask whether brawny people are stronger. The size of a muscle can be measured via MRI, and its strengthwith a machine that looks like one in the weight room at your health club. Researchers have found correlation coefficientsranging from 0.7 to 0.9, which is much stronger than the correlation between brain size and IQ. Muscle size accurately predicts strength, just as we’d expect.
    Why are size and function so closely related for muscles but not for brains? Think of a muscle as operating like a factory in which all workers do the same thing. If every worker singlehandedly performs all the steps required for making an entire widget, doubling the size of the workforce will double the factory’s output of widgets. Likewise, every fiber of a muscle performs the same task. All the fibers are lined up in parallel, and all pull in the same direction. Their contributions to the force are additive (you can simply add them together to get the total), so a muscle with more fibers should be stronger.
    Now consider a factory with a more complex organization. Each worker performs a different task, like fastening a screw or welding a joint. To make even a single widget, all the workers must cooperate. Economists say that such division of labor is efficient because specialization allows each worker to become highly skilled at each task. However, doubling the number of workers will likely fail to double the output of widgets. It’s not easy to integrate the new workers into the existing organization in a way that increases output. In fact, adding more workers could even reduce output by disrupting the workflow. As Brooks’ Law—a maxim of software engineers—puts it, “Adding more programmers to a late software project makes it later.”
    The brain works like the more complex factory. Each of its neurons performs a tiny task, and they cooperate in intricate ways to carry out mental functions. That’s why performance depends less on the number of neurons and more on how they are organized.
    The factory analogy explains the limitations of phrenology. Can it also explain remapping? The American neuropsychologist Karl Lashley believed that mental functions were widely distributed across the cortex, and charged that most of the boundaries of Brodmann’s mapwere figments of the imagination. Nevertheless, this archenemy of localizationism could not completely deny the experimental evidence in its favor. In 1929 he countered with his doctrine of cortical
equipotentiality.
Lashley granted that every cortical area is dedicated to a specific function, but every area also has the
potential
to assume some other function, he claimed.
    Returning to our imaginary factory—the more complex one—let’s suppose that a worker is reassigned to a new task. The initial clumsiness will eventually give way to proficiency. Workers may be specialized, but they are also equipotential. When provided with new inputs, they can change their functions.
    Lashley’s doctrine has some element of truth but is too sweeping. The cortex is not infinitely adaptable. If it were, every stroke patient would recover completely. To understand the limits of adaptation and develop ways to enhance it, we need a deeper understanding. We know that the cortex can remap, but how exactly does the function of an area change?
    We can’t answer this without addressing a more basic issue: What defines the function of a cortical area in the first place? Broca’s and Wernicke’s regions are dedicated to language, and Brodmann areas 3 and 4 are dedicated to bodily sensation and movement. But
why
these functions? And how are they