Connectome (11 page)

Certain sedatives work by increasing the strength of inhibition, empowering the inhibitory neurons to dampen activity. And drugs that weaken inhibition give the upper hand to excitatory neurons, which may go out of control and ignite epileptic seizures. Here you could think of excitatory neurons as rabble rousers who incite the mob to riot, whereas inhibitory neurons are like the police, summoned to dampen the excitement of the crowd.

Many other properties of synapses are under investigation by neuroscientists. But I hope it's clear that saying two neurons are “connected” only begins to describe their interaction. The connection may occur through one or more synapses—chemical or electrical or both. A chemical synapse has a direction, may be excitatory or inhibitory, and may be strong or weak. The electrical currents it produces may be lengthy or brief. All of these factors matter when synapses cause neurons to spike.

 

I've explained that neural pathways diverge from the eye to both the legs and the salivary glands. To make clear why any given stimulus activates some pathways but not others, I've focused on synaptic convergence, which is crucial for spiking by the voting model. If a neuron doesn't spike, it functions as a dead end for all the pathways converging onto it. The myriad dead ends imposed by nonspiking neurons are essential for brain function. They allow the sight of a snake to
not
trigger the salivary glands, and the sight of a steak to
not
make you run away.

Failing to spike is just as important to neural function as spiking. That's why single synapses and single pathways are not capable of relaying spikes. In the voting model, there are two mechanisms for making neurons choosy about when to spike. I mentioned that the axon spikes only when the total electrical current collected by the cell body exceeds some threshold. Raising the threshold for an axon is a way of making the neuron even choosier. If a neuron receives a “no” vote from an inhibitory synapse, that also increases its selectivity,
as now even more “yes” votes are required for a spike. In other words, there are two mechanisms that prevent neurons from spiking indiscriminately: the threshold for spiking and synaptic inhibition.

Spikes have two functions. The generation of a spike near the cell body represents the making of a decision. The propagation of a spike along the axon communicates the result of the decision to other neurons. Communication and decision-making have different goals. The goal of communication is to preserve information, to transmit it without change. But discarding information is fundamental to making decisions. Imagine a friend trying on a coat in a boutique, unable to decide whether to purchase it. There are many inputs to his or her decision, such as the color, the fit, the designer label, the ambiance of the store, and so on. You might listen to your friend go on and on about this information. But at some point you'll lose patience and ask, “Are you buying this coat or not?” In the end, the final decision—not the many reasons for it—is what matters.

Likewise, an outgoing spike indicates that a neuron's tally of votes exceeded its threshold, but does not convey details about the individual votes of its “advisors.” So neurons may transmit some information, but they also throw a lot away. (I'm reminded of my father, who likes to say proudly, “Do you know why I'm so smart? It's because I'm so good at forgetting the right things.”) That's why the brain is far more sophisticated than a telecom network. It would be appropriate to say that neurons
compute,
not just communicate. We've come to associate the notion of computation exclusively with our desktop and laptop computers, but these are just one type of computational device. The brain is another—albeit a very different kind.

Though we should be cautious about comparing brains to computers, they are similar in at least one important respect. They are both “smarter” than the elements from which they're constructed. According to the weighted voting model, neurons perform a simple operation, one that does not require intelligence and can be performed by a basic machine.

How could brains be so sophisticated when neurons are so simple? Well, maybe a neuron is not so simple; real neurons are known to deviate somewhat from the voting model.
Nevertheless, a single neuron falls far short of being intelligent or conscious, and somehow a network of neurons is.

This idea might have been difficult to accept centuries ago, but now we've become accustomed to the idea that an assembly of dumb components can be smart. None of the parts in a computer is by itself capable of playing chess—but a huge number of these parts, when organized in the right way, can collectively defeat the world champion. Similarly, it's the organized operation of your billions of dumb neurons that makes you smart. This is the deepest question of neuroscience: How could the neurons of your brain be organized to perceive, think, and carry out other mental feats? The answer lies in the connectome.

Spikes and secretions. Is there really nothing more to your mind than these physical events inside your brain? Neuroscientists take it for granted that there is not, but most people I've encountered resist the idea. Even neuroscience fans, who may start by peppering me with questions about the brain, often end up expressing the belief that the mind ultimately depends on some nonmaterial entity like the soul.

I don't know of any objective, scientific evidence for the soul. Why do people believe in it? I doubt that religion is the only reason. Everyone, religious or not, feels that he or she is a single, unified entity that perceives, decides, and acts. The statement “
I
saw a snake, and
I
ran away” assumes the existence of that entity. Your subjective feeling—and mine—is “I am one.” In contrast, neuroscience contends that the unity of the mind is but an illusion hiding the spikes and secretions of a staggering number of neurons, a concept of the self that could be summed up as “I am many.”

Which is the ultimate reality—the many neurons or the one soul? In 1695 the German philosopher and mathematician Gottfried Wilhelm Leibniz argued for the latter:

 

Furthermore, by means of the soul or form, there is a true unity which corresponds to what is called the
I
in us; such a thing could not occur in artificial machines, nor in the simple mass of matter, however organized it may be.

In the last years of his life, he took the argument one step further, asserting that machines were fundamentally incapable of perception:

 

One is obliged to admit that perception and what depends upon it is
inexplicable on mechanical principles,
that is, by figures and motions. In imagining that there is a machine whose construction would enable it to think, to sense, and to have perception, one could conceive it enlarged while retaining the same proportions, so that one could enter into it, just like into a windmill. Supposing this, one should, when visiting within it, find only parts pushing one another, and never anything by which to explain a perception.

 

Leibniz could only imagine observing the parts of a machine that perceives and thinks—and he did so purely for the sake of arguing that no such machine could ever exist. But his fantasy has literally come true, if you regard the brain as a machine constructed from neuronal parts. Neuroscientists regularly measure the spiking of neurons in living, functioning brains. (The technology for measuring secretions is less advanced.)

Most of these measurements are done on animals, but occasionally they are performed on humans. The neurosurgeon Itzhak Fried operates on patients with severe cases of epilepsy. Like Penfield, he uses electrodes to map the brain before surgery, and also to make scientific observations
(always with the consent of his patients). In a collaborative experiment with the neuroscientist Christof Koch and others, Fried showed a collection of photos to several patients and recorded neural activity in the medial part of the temporal lobe, or MTL. (
Medial
means “close to the plane dividing the left and right hemispheres.”) Many neurons were studied, but one in particular became famous. Fried stumbled on a neuron that generated many spikes when a patient viewed photos of the actress Jennifer Aniston. The neuron generated few or no spikes when the patient viewed photos of other celebrities, nonfamous people, landmarks, animals, and other objects. Even a photo of Julia Roberts,
another famously beautiful actress, elicited no response.

Reporters ate up the story, joking that scientists had finally identified the neurons in our brain that store useless information. They made quips like “Angelina Jolie may have gotten Brad Pitt, but Jennifer Aniston is the one with her own namesake neuron.” They gleefully noted that the neuron remained quiet when presented with photos of Jennifer Aniston with the actor Brad Pitt. (The paper by Fried and his collaborators appeared in 2005, the same year that the celebrity supercouple
divorced.)

All joking aside, how should we think about this neuron? Before drawing any conclusions, you should know that other neurons were studied too. There was a “Julia Roberts neuron” that spiked only for photos of Julia Roberts, a “Halle Berry neuron,” a “Kobe Bryant neuron,” and so on. Based on these findings, we could venture a theory: For every celebrity you know, there exists a “celebrity neuron”
in your MTL—a neuron that spikes in response to that particular celebrity.

To be even bolder, we might suggest that this is the way perception works more broadly. This general ability is too complex to be carried out by a single neuron. Instead, it is divided up into many specific functions, each of which is the detection of some person or object and is carried out by a corresponding neuron. You might compare the brain to an army of paparazzi employed by a magazine that seeks to publish titillating photos of movie stars. Each photographer is assigned to a single celebrity. One hounds Jennifer Aniston with his camera, another devotes himself to Halle Berry, and so on. Every week, their activities determine which celebrities appear in the magazine, just as the spiking of MTL neurons determines which celebrities are perceived by a person.

Have we refuted Leibniz? It seems that we've just peeked inside the machine and seen perception reduced to spikes. But let's pause for a moment of caution. Although Fried's experiment is fascinating, it had a major limitation: Relatively few celebrities were studied. Overall, each patient viewed photos of only ten or twenty celebrities. We can't exclude the possibility that the “Jennifer Aniston neuron” would have been activated if a photograph of some other celebrity had been shown.

So let's revise our theory a bit. In our preliminary theory, we assumed a one-to-one correspondence between neurons and celebrities. Suppose instead that a neuron responds to a small percentage of celebrities, rather than only one. And suppose that each celebrity activates a small percentage
of neurons, rather than just one. The spiking of this
group
of neurons is the event in the brain that marks the perception of that celebrity. (The groups activated by different celebrities are allowed to overlap partially but not completely. You can imagine that each photographer in our army of paparazzi would be assigned to cover more than one celebrity, and each celebrity would be hounded by a group of photographers.)

You might protest that perceptions are too complex to be reduced to something as simple as spiking. But remember that the spiking of a
population
of neurons defines a pattern of activity in which some neurons spike and others do not. The number of possible patterns
is huge—more than enough to uniquely represent every celebrity, and indeed every possible perception.

So Leibniz was wrong.
Observing the parts of the neuronal machine has told us a great deal about perception, even though neuroscientists have generally been limited to measuring spikes from a single neuron at a time. Some have measured spikes from tens of neurons simultaneously, but even this is meager compared with the enormous number of neurons in the brain. From the experiments that have been done so far, we might extrapolate: If I could observe the activities of
all
your neurons, I would be able to decode what you are perceiving or thinking. This kind of mind reading
would require knowing the “neural code,” which you can picture as a huge dictionary. Each entry of the dictionary lists a distinct perception and its corresponding pattern of neural activity. In principle, we could compile this dictionary by recording the activity patterns generated by a huge number of stimuli.

 

Physicist, mathematician, astronomer, alchemist, theologian, and Master of the Royal Mint—Sir Isaac Newton pursued many careers in a single lifetime. He invented calculus, a branch of mathematics essential to the physical sciences and engineering. He explained how planets orbit around the sun by applying his famous Three Laws of Motion and the Universal Law of Gravitation. He theorized that light is composed of particles, and discovered mathematical laws of optics describing how the paths of these particles are bent by water or glass to produce the colors of the rainbow. During his lifetime Newton was already recognized as a transcendent genius. When he died in 1727, the English poet Alexander Pope composed the epitaph: “Nature and nature's laws lay hid in night; / God said ‘Let Newton be' and all was light.” In a 2005 poll conducted by England's Royal Society, Isaac Newton was voted even greater than Albert Einstein.

We exalt the lone genius through such comparisons and through honors like the Nobel Prize. But another view of science places less emphasis on the individual. Newton himself acknowledged his intellectual debts by writing, “If I have seen further it is only by standing on the shoulders of giants.”

Was Newton really so special? Or did he just happen to be in the right place at the right time and put two and two together? Calculus was independently invented around the same time by Leibniz. Stories like this—of nearly simultaneous discovery—are common in the history of science, because new ideas are created by combining old ideas in a new way. At any given moment in history, more than one scientist could potentially find the right combination. Since no idea is truly new, no scientist is truly special. We cannot understand the accomplishments of one without knowing how she or he drew on the ideas of others.