Earlier work concluded that the brain’s electrochemical oscillations topped out at a frequency of about 60 hertz. In fact, weak signals between 70 and 200 Hz were simply unable to pierce the skull in detectable quantities, as were certain very-low-frequency signals of less than 10 Hz. Knight and others believe that oscillations between brain areas, influenced by these lower-frequency signals, are a critical part of what the brain does. Down the hall from his office, students examine the survivor of a gang shooting with a bullet still in his brain. They use a grid of 256 electrodes, four times the old standard of 64 electrodes, revealing new subtleties of brain functioning. When technology doubles that density again, the thinking is, we will be able to image oscillations in a single column of the cortex, demonstrating the brain’s own conversation with itself. With this understanding of information flow from one point to another, scientists may be able to posit a new model for an unsupervised, networked brain.

Jeff Hawkins, founder of the Redwood Neuroscience Institute (RNI)

Proving this model may take another cocksure genius synthesizer, like Einstein in physics or Watson for DNA, and it could easily be a century of work getting enough data for our genius to make the leap. One professor at Brandeis, Eve Marder, has for over two decades studied a cluster of about 30 neurons that govern the digestive tract of the lobster. Yet this computational dynamic remains only dimly understood.

For all we can say today about our consciousness, with its wondering and metaphor making, it may be but a byproduct of an infinitely complex system. There is already evidence that conscious choice is really an afterthought, an affirmation delivered by the “higher” parts of the brain to justify tasks already underway. But just as the latest technology upsets old models, new tools may derail the “brain as networked event” metaphor, which will then have to mutate to accommodate fresh data. In the meantime, it is likely that we will see discoveries that could, say, end paralysis. Jose M. Carmena, a professor at both Helen Wills and the engineering department, has already created a system by which a primate can move a prosthetic limb simply by thinking about moving it.

This is thought made manifest, a miracle driven by a tragic necessity. It is another pas de deux in the landscape of the brain. Our brain and our experience shape each other—perhaps we become bird watchers because of generous fusiform areas, and then choose birds over cars because of our childhoods. Our tools and our understanding are likewise intertwined. Computers are of course a crucial tool in brain study. And the lessons we learn from our tools in turn shape development of our silicon-based “thinking machines.”

Given the levels of data we now pile up in our computer networks, there is an urgency to understanding how the brain makes order from its own far larger multitude of random data. Technologists press for brain information as never before, and plenty of researchers have startup companies on the side. In the other direction, Microsoft billionaire Paul Allen has sponsored an online “brain atlas” tapped by 10,000 researchers a month. Closer to home, tech millionaire Jeff Hawkins in 2002 founded the nonprofit Redwood Neuroscience Institute (RNI), a group of more than a dozen interdisciplinary researchers who develop computational models of the brain’s underlying mechanisms. Hawkins, an engineer who earlier developed the PalmPilot, himself is busy synthesizing and extending many existing theories in an effort to build entirely new kinds of computer software.

Hawkins draws from several decades of research and posits that the brain is organized as a hierarchy of memory across many levels. Each level abstracts what the preceding level has learned, seeking patterns and making predictions. The eye transforms light to signals that, as they move up the brain’s functional levels for processing vision, are assembled and processed as representing, say, a cat. Over time the higher levels realize a general picture of what any cat looks like, from any angle. There is interplay among the abstractions and the kinds of learning, so that we casually know that a cat is not a dog, but cats and dogs are pets, although big cats, which aren’t those kinds of cats, usually aren’t pets.

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