In Delgado's heyday, neuroscientists believed that the brain employed just a single, simple coding scheme discovered in the 1930s by Lord Edgar Adrian, a British neurobiologist. After isolating sensory neurons from frogs and eels, Adrian showed that as the intensity of a sensory stimulus increases, so does a neuron's firing rate, which can peak as high as 200 spikes per second. In the next few decades, experiments confirmed that the nervous systems of all animals employ this method of conveying information, called a rate code. Researchers also demonstrated that specific neurons are dedicated to extremely specific tasks, such as seeing vertical lines, hearing sounds of a specific pitch, or flexing a finger. Together, these findings suggested that controlling the brain might be a simple matter of delivering the right jolt of electricity to the right clusters of brain cells.
It turns out that things are not so simple. Recent research has undermined two basic assumptions about how the brain processes information. One is the view of neurons as drones single-mindedly carrying out specific tasks. Cells can be retrained for different jobs, switching from facial expressions to finger flexing or from seeing red to hearing squeaks. Our neural circuits keep shifting "massively and continuously" not only during childhood but throughout our lives, says Michael Merzenich of the University of California at San Francisco, whose research has helped expose just how plastic neurons really are.
Neuroscientists are also questioning whether the firing rate serves as a brain cell's sole means of expression. Rate codes are extremely inefficient. They are analogous to a language that conveys information only through modulations of a voice's volume, and they imply that the brain is inherently noisy and wasteful. What counts as a genuine signal is a surge in the firing rate of a cell from, say, 2 to 50 times a second; variations in the intervals between successive spikes in a surge are considered irrelevant. But just as some geneticists suspect that the junk DNA riddling our genomes actually serves hidden functions, so some neuroscientists believe that information may lurk within the fluctuating gaps between spikes. Schemes of this sort, which are known as temporal codes, imply that significant information may be conveyed by just a spike or two.
Another time-sensitive code involves groups of neurons firing in precise lockstep, or synchrony. Some evidence suggests that synchrony helps us focus our attention. If you are at a noisy cocktail party and suddenly hear someone nearby talking about you, your ability to eavesdrop on that conversation and ignore all the others around you could result from the synchronous firing of cells. "Synchrony is an effective way to boost the power of a signal and the impact it has downstream on other neurons," says Terry Sejnowski, a computational neurobiologist at the Salk Institute. He speculates that the abundant feedback loops linking neurons allow them to synchronize their firing before passing messages on for further processing.
Then there is the chaotic code championed by Walter J. Freeman of the University of California at Berkeley. For decades, he has contended that far too much emphasis has been placed on individual neurons and action potentials, for reasons that are less empirical than pedagogical. The action potential "organizes data, it is easy to teach, and the data are so compelling in terms of the immediacy of spikes on a screen." But spikes are ultimately just "errand boys," Freeman says; they serve to convey raw sensory information into the brain, but then much more subtle, larger-scale processes immediately take over.
The most vital components of cognition, Freeman believes, are the electrical and magnetic fields, generated by synaptic currents, that constantly ripple through the brain. These fields are chaotic, in the sense that they conceal a hidden, complex order and are extremely sensitive to minute influences—the so-called butterfly effect. A sound enters the ear and triggers a stream of action potentials, which nudge the waves of electrical activity coursing through the cortex into a particular chaotic pattern, or attractor. The result is fantastically precise, almost instant comprehension. "You pick up the telephone and hear a voice," Freeman says, "and before you even know the meaning of the words, you know who you're talking to and what her emotional state is."
Although none of these alternatives to rate codes has been proven yet, so little is known about how the brain processes information that "it's difficult to rule out any coding scheme at this time," argues neuroscientist Christof Koch of Caltech. Koch and Itzhak Fried, who is both a neuroscientist and a practicing neurosurgeon at UCLA Medical School, recently uncovered evidence for a coding scheme long ago discarded as implausible. This scheme has been disparaged as the "grandmother cell" hypothesis, because in its reductio ad absurdum version it implies that our memory banks dedicate a single neuron to each person, place, or thing that inhabits our thoughts, such as Grandma. Most theorists assume that such a complex concept must be supported by large populations of cells, each of which corresponds to one component of the object (the bun, the bifocals, the leather miniskirt).
Yet Fried and Koch have found neurons that act very much like grandmother cells. Their subjects were epileptics who had electrodes temporarily inserted into their brains to provide information that could guide surgical treatment. The researchers monitored the output of the electrodes while showing the patients images of animals, people, and other things. A neuron in the amygdala of one patient spiked only in response to three quite different images of Bill Clinton—a line drawing, a presidential portrait, and a group photograph. A cortical cell in another patient responded in a similar way to images of characters from The Simpsons. In future experiments, Koch and Fried plan to show patients photographs of their grandmothers to see if they can locate actual grandmother cells.
It makes intuitive sense, Koch says, that our brains should dedicate some cells to people and things frequently in our thoughts. He adds that his findings might seem less surprising if one realizes that neurons are much more than simple "threshold" switches that fire whenever incoming pulses from other neurons exceed a certain level. A typical neuron receives input from thousands of other cells, some of which inhibit rather than encourage the neuron's firing. The neuron may in turn encourage or suppress firing by some of those same cells in complex positive or negative feedback loops.
In other words, a single neuron may resemble less a simple switch than a customized minicomputer, sophisticated enough to distinguish your grandmother from Grandma Moses. If this view is correct, meaningful messages might be conveyed not just by hordes of neurons screaming in unison but by a small group of cells whispering, perhaps in a terse temporal code. Discerning such faint signals within the cacophony of the brain will be "incredibly difficult," Koch says, no matter how far neurotechnology advances.
Efforts to detect the whispers amid the cacophony are further complicated by the improvisational dexterity of the brain. Studies of the motor cortex, which underlies body movement, have shown that the brain invents entirely new coding schemes for novel situations. In the 1980s, researchers discovered neurons in a monkey's motor cortex that peaked in their firing rate when the monkey moved its hand in a specific direction. Rather than falling silent when the hand diverged even slightly from its so-called preferred direction, the cells' firing rate diminished in proportion to the angle of divergence.
Several teams, including one led by Andrew Schwartz of the University of Pittsburgh, have sought to exploit these findings to create neural prostheses for paralyzed patients. They have demonstrated that electrodes implanted in a monkey's motor cortex can detect signals accompanying a specific arm movement; these same signals—after being processed by an algorithm—can initiate similar movements by a robot arm. If the monkey's arm is tied down, the monkey learns to control the robot arm through pure thought—but with an entirely different set of neural signals. These findings dovetail with others showing that neurons' coding behavior shifts in different contexts. "What you're aiming at is sort of a moving target," Schwartz says. "If you make an estimate of something at one point in time, that doesn't mean it's going to stay that way."
