Over the past decade, popular
science has been suffering from neuromania. The enthusiasm came from studies
showing that particular areas of the brain ‘light up’ when you have certain
thoughts and experiences. It's mystifying why so many people thought this
explained the mind. What have you learned when you say that someone's visual
areas light up when they see things? People still seem to be astonished at the
very idea that the brain is responsible for the mind—a bunch of gray goo makes
us see! It is astonishing. But scientists knew that a century ago; the really
interesting question now is how the gray goo lets us see, think and act
intelligently. New techniques are letting scientists understand the brain as a
complex, dynamic, computational system, not just a collection of individual
bits of meat associated with individual experiences. These new studies come
much closer to answering the ‘how’ question. Fifty years ago researchers made a
great Nobel Prize-winning discovery. They recorded the signals from particular
neurons in cats' brains as the animals looked at different patterns. The
neurons responded selectively to some images rather than others. One neuron
might only respond to lines that slanted right, another only to those slanting
left. But many neurons don't respond in this neatly selective way. This is
especially true for the neurons in the parts of the brain that are associated
with complex cognition and problem-solving, like the prefrontal cortex.
Instead, these cells were a mysterious mess—they respond idiosyncratically to
different complex collections of features. What were these neurons doing?
In a new study researchers at Columbia
University College and the Massachusetts Institute of Technology taught monkeys
to remember and respond to one shape rather than another while they recorded
their brain activity. But instead of just looking at one neuron at a time, they
recorded the activity of many prefrontal neurons at once. A number of them
showed weird, messy ‘mixed selectivity’ patterns. One neuron might respond when
the monkey remembered just one shape or only when it recognized the shape but
not when it recalled it, while a neighboring cell showed a different pattern. To
analyze how the whole group of cells worked the researchers turned to the
techniques of computer scientists who are trying to design machines that can
learn. Computers aren't made of carbon, of course, let alone neurons. But they
have to solve some of the same problems, like identifying and remembering
patterns. The techniques that work best for computers turn out to be remarkably
similar to the techniques that brains use. Essentially, they found the brain
was using the same general sort of technique that Google uses for its search
algorithm. You might think that the best way to rank search results would be to
pick out a few features of each Web page like ‘relevance’ or ‘trustworthiness’.
With neurons that detect just a few features, you can capture those features
and combinations of features, but not much more. To capture more complex
patterns, the brain does better by amalgamating and integrating information
from many different neurons with very different response patterns.
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