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Silicon Cognition

Back at the University of Southern California, Berger’s team is pushing the farthest frontier of brain-machine interfaces. Once they have mapped out the signal patterns of several regions of the brain, the researchers plan to manipulate the ways the brain processes information and communicates with itself-in short, how the brain thinks. This work could one day lead to neural prostheses that restore and even enhance such cognitive processes as memory. Imagine going to the doctor to recover memories long since faded or buying hardware that sharpens your ability to remember people’s names.

Berger’s team is taking a baby step toward that vision by developing a computer chip that mimics the signal processing of the hippocampus, a spiral-shaped region of the brain that is instrumental in learning and forming memories. Fortunately, the information flow in the hippocampus of rats is straightforward, says Berger, and the circuit looks similar, though more complicated, in the human hippocampus.

What makes things challenging is that-at least in Berger’s view-memory in the brain is represented in the dynamic firing patterns of neurons, not in a fixed arrangement of bits like that of a computer’s memory. “If any part of the brain looks like RAM, we haven’t found it yet,” Berger says. And neurons are inherently tricky. To get one to fire, timing is everything: it may take a combination of impulses from surrounding neurons or repeated inputs from one messenger spaced in time just so.

To capture these dynamics, Berger’s team has developed mathematical models of the individual neurons in question and has begun to implement the models in hardware. If neuron A sends a particular pattern of impulses to neuron B, says University of Southern California biomedical engineer Vasilis Marmarelis, the model tells you what pattern neuron B will send to neuron C. “It isn’t sexy,” he says, “but it’s the first step of a very long journey.” From there, the researchers will put thousands of neuron models onto a low-power silicon chip.

Later this year, says Berger, the proof-of-principle experiment will go like this: In a slice of a rat’s hippocampus, the scientists will demonstrate that electrical signals from region A are processed by region B and sent on to region C. They will then remove neurons from region B and show that the output of region C is disrupted. Finally, they will reroute the signals through a prototype chip-in place of region B-to see whether that completes the circuit and produces the same overall pattern of signals as the healthy slice.

Image courtesy of John MacNeil

If this is successful, the next step will be to test the chip in an animal. Within three years Berger’s group plans to turn its interface over to a team led by physiologist Sam Deadwyler at Wake Forest University. Deadwyler is training monkeys to remember clip art pictures flashed on a screen and to pick the images from a subsequent lineup. At the same time, he is recording signals from the hippocampus that allow him to identify which neurons are important for the task-and even to predict whether the monkey will choose correctly. When Berger’s interface is ready, says Deadwyler, the researchers will temporarily inactivate the hippocampus so the primate can no longer do the task; then they will plug the chip into the affected area to see whether the interface can restore the monkey’s performance.

Eventually, Berger and Deadwyler plan to determine whether the chip can augment memory: they will implant the chip in an animal whose hippocampus is intact. With the chip, the monkey might be able to remember a picture for a longer period of time or be able to pick it out of a larger lineup of distractions. In the future, says Deadwyler, it might be possible to connect a person’s brain to hardware that makes memories last longer or that allows one to keep track of ever increasing amounts of information-like when you’re dashing through a busy airport and need to remember a phone number for a few seconds. But don’t expect to see this anytime soon. “We’re a long way from improving on paper and pencil,” says the NIH’s Heetderks.

For one thing, Berger’s group faces the skepticism of some scientists who don’t buy into the fundamental premise that memory is constituted solely of dynamic patterns of neuron activity. And it faces many of the practical challenges other neural-prosthesis research teams grapple with. For now, nobody knows exactly which neurons-or how many-need to be tapped in order to achieve useful devices. Depending on the application, the researchers may need to access thousands of brain cells all at once. And there are computational hurdles they must overcome before the interfaces can process massively parallel streams of neural data in real time.

But perhaps the greatest technical challenge lies in physically connecting rigid hardware to delicate brain cells and sustaining those connections for months or even years at a time, says John Chapin, a physiologist at the State University of New York Downstate Medical Center who helped pioneer methods for accessing brain signals in the mid-1990s. Because neurons continually shift their positions and alter their connections, the interface must be flexible, biocompatible, and adaptable to changes in the signals it receives. With this in mind, DARPA’s Rudolph is pushing to promote a standardized electrode platform across the initiative so that each team doesn’t reinvent the wheel. But this is easier said than done. “Scientists would rather use each other’s toothbrushes than each other’s electrodes,” says Caltech’s Koch.

Even if the interface technologies work, they might face a long road to acceptance. Paralyzed patients anxious to gain enhanced physical abilities may be willing to accept the risks of surgery and to live with hardware implanted in their brains, but most healthy people would probably balk at the proposition. In fact, says Rudolph, “we really don’t envision implanting healthy people with these kinds of devices.” The key to being able to restore or augment human capabilities, he says, will be gaining access to the brain signals in an unobtrusive way-ideally, without wires, electrodes, or surgeries.

Before DARPA-or anyone else, for that matter-will invest in that next generation of brain-signal-detection technology, researchers must determine whether neural prostheses will be practical in their new applications. “If successful,” says Rudolph, “we will have seeded the important work to demonstrate that this can be done and-if a noninvasive tool can be found to extract the same kinds of information-that human performance enhancement can be envisioned.” And though this vision is still years away, our minds may already be on the road to a new way of thinking.

Other Brain-Machine Research
Richard Andersen Caltech Electrode systems for recording brain impulses
Niels Birbaumer University of Tbingen (Germany) Noninvasive brain-signal detectors
John Donoghue Brown University
and Cyberkinetics
(Providence, RI)
Neural prostheses that give paralyzed patients control over computers
Philip Kennedy Neural Signals
(Atlanta, GA)
First human tests of brain implants for restoring communication in completely paralyzed patients
Andrew Schwartz University of Pittsburgh Neural prostheses that control robot arms
Harvey Wiggins Plexon
(Dallas, TX)
Hardware and software for recording and analyzing brain signals

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