These pathways end when axons in nerves make synapses onto muscle fibers, which respond to secretion of neurotransmitter by contracting. The coordinated contraction of many fibers causes a muscle to shorten and produce a movement. More generally, every one of your muscles is controlled by axons that come from motor neurons. The English scientist Charles Sherrington, who won a Nobel Prize in 1932 and coined the term synapse, emphasized that muscles are the final destination of all neural pathways: “To move things is all that mankind can do . . . for such the sole executant is muscle, whether in whispering a syllable or felling a forest.”
Between sensory and motor neurons there are many pathways, some of which we will consider in detail in later chapters. It’s clear that these pathways exist; if they didn’t, you wouldn’t be able to respond to stimuli. But exactly how do signals travel along pathways?
When California joined the United States in 1850, communicating with the eastern states took weeks. The Pony Express was created in 1860 to speed up mail delivery. Along its two-thousand-mile route from California to Missouri were 190 stations. A mailbag traveled day and night, switching horses at every station and changing riders every six or seven stations. After reaching Missouri, messages traveled by telegraph to states farther east. The total transit time for a message between the Pacific and the Atlantic was reduced from twenty-three to ten days. The Pony Express operated for only sixteen months before being completely replaced by the first transcontinental telegraph, which in turn was succeeded by telephone and computer networks. The technology may have changed, but the underlying principle has not: A communication network must have a means of relaying messages from station to station along pathways.
It’s tempting to think of the nervous system as a communication network that relays spikes from neuron to neuron. A neural pathway would behave like dominoes, with each spike igniting the next spike in the pathway in the same way that each falling domino tips over the next one in the chain. This would explain how your eye tells your legs to move when you see a snake. But in fact it’s not that simple. While it’s true that an axon relays spikes from the cell body to synapses, it turns out that a synapse does not simply relay spikes to the next neuron.
Almost all synapses are weak. The secretion of neurotransmitter causes a tiny electrical effect in the next neuron, far below the level required to cause a spike. Imagine a chain of dominoes spaced too far apart. The falling of one won’t have any effect on the next. Likewise, a single neural pathway cannot typically relay a spike on its own—but as I’ll explain below, this is a good thing.
“Two roads diverged in a yellow wood / And sorry I could not travel both / And be one traveler, long I stood,” wrote Robert Frost in “The Road Not Taken.” A spike does not share Frost’s dilemma when it comes to a fork in an axon. Not limited to being “one traveler,” the spike duplicates itself, giving rise to two spikes that take both branches. By doing this repeatedly, a single spike starting near the cell body becomes many spikes that reach every branch of the axon, amplitude undiminished. All of the synapses made by the axon onto other neurons are stimulated to secrete neurotransmitter.
Through these outgoing synapses, neural pathways diverge like the roads in the poem. That’s why stimulating one sense organ can cause multiple responses. The sight of a snake makes you want to run, because of pathways from your eyes to your legs. But the sight of a tasty steak causes your mouth to water, this time thanks to pathways from your eyes to your salivary glands. Because these two types of pathways diverge from the eyes, it’s no mystery that either running or salivation is possible after you see something. The mystery is quite the opposite: Why is there only one response? If signals took all possible pathways, any stimulus would cause every muscle and gland to become activated, and clearly that doesn’t happen.
The reason is that signals don’t get through pathways so easily. We already saw that single synapses and pathways do not relay spikes. So how do signals ever get through? Although the branches of dendrites look similar to those of axons, their function is completely different. Axons diverge, but dendrites converge. Where two branches join, electrical currents can meet as they flow toward the cell body, and can combine like the water of merging streams. And as a lake collects water from many streams, the cell body collects currents from the many synapses converging onto its dendrites.
Why is convergence important? Although a single synapse is typically too weak to drive a neuron to spike, multiple converging synapses can do the job. If they are activated simultaneously, they can collectively “convince” a neuron to spike. Because a spike is “all or none,” we can regard it as the output of a “neural decision.” By this metaphor, I do not mean that a neuron is conscious or thinks in the same way that a human does. I simply mean that a neuron is not wishy-washy. There is no such thing as half a spike.
When we’re deciding, we may seek advice from friends and family. Similarly, a neuron “listens” to other neurons through its converging synapses. The cell body sums the electrical currents, effectively tallying the votes of the “advisors.” If the tally exceeds a threshold, the axon spikes. The value of this threshold determines whether a neuron decides easily or reluctantly, much as political systems can require a simple majority, a two-thirds majority, or unanimity.
In many neurons, the electrical signals of dendrites are continuously graded, unlike the all-or-none spikes of the axon. This is well suited for representing the entire range of possible vote tallies. A spike in the dendrites would be premature—like calling an election before all the votes are in. Only after the cell body tallies all the votes can spikes occur in the axon. If dendrites lack spikes, they cannot transmit information over long distances; that’s the reason dendrites are much shorter than axons.
One of the basic slogans of a democracy is “One person, one vote.” All votes are weighted equally, as in the neural model above. But we may be less democratic when combining the advice of our friends and family, giving more weight to some opinions than to others. Similarly, a neuron actually weights its “advisors” unequally. Electrical currents have magnitudes. Strong synapses produce large currents in the dendrite, and weak synapses produce small currents. The “strength” of a synapse quantifies the weight of its vote in the decision of a neuron. And it’s possible for a neuron to receive multiple synapses from another neuron, as if allowing it to cast multiple votes—a further kind of favoritism.
We’ve arrived at the “weighted voting model” of a neuron. In any type of voting there is some requirement for simultaneity. In politics, this is achieved by asking everyone to go to the polls on a predetermined day. Since synapses can vote at any time, it’s always election day in the brain. (Actually, the metaphor is slightly misleading—synaptic votes are tallied over a time period much shorter than a day, ranging from milliseconds to seconds.) The votes of two synapses are counted in the same tally only if their electrical currents are close enough in time to overlap.
Think of synaptic currents as insults being thrown at someone. Any single insult is too weak to excite a temper tantrum (a spike), so if the insults come only infrequently, the person won’t get angry. But if there are many simultaneous insults or if they come in quick succession, they can add up—until the “last straw” pushes the person over the threshold.
In the explanation of neural voting I left out an important feature of synapses for the sake of simplicity. It turns out that “yes” votes are not the only kind tallied by neurons. Another kind of synapse registers “no” votes. The yes–no distinction arises because activation of a synapse causes current to flow, and two directions of flow are possible. Excitatory synapses say “yes” because they make electrical current flow into the receiving neuron, which tends to “excite” spiking. Inhibitory synapses say “no” because they make current flow out of the neuron, which tends to “inhibit” spiking.
Inhibition is crucial to the operation of the nervous system. Intelligent behavior is not just a matter of making appropriat
e responses to stimuli. Sometimes it’s even more important to not do something— not reach for that doughnut when you’re on a diet, or not drink another glass of wine at the office holiday party. It’s far from clear how these examples of psychological inhibition are related to inhibitory synapses, but it’s at least plausible that there’s some sort of connection.
The need for inhibition might be the chief reason why the brain relies so heavily on synapses that transmit chemical signals. There is actually another kind of synapse, one that directly transmits electrical signals without using neurotransmitter. Such electrical synapses work more quickly, since they eliminate the time-consuming steps of converting signals from electrical to chemical and then back to electrical, but there are no inhibitory electrical synapses, only excitatory ones. Perhaps because of this and other limitations, electrical synapses are much less common than chemical ones.
Given that inhibition is a factor, how should our voting model be revised? Earlier I mentioned that a neuron spikes when the number of “yes” votes exceeds a threshold. If we include inhibition, spiking happens when “yes” votes exceed “no” votes by some margin set by the threshold. Like their excitatory brethren, inhibitory synapses can be stronger or weaker, so the vote is weighted rather than totally democratic. Some inhibitory synapses are even strong enough to effectively veto many excitatory synapses.
There’s one last thing to know about neural voting. Neurons behave like conformists or contrarians, because they too can be classified as either excitatory or inhibitory. An excitatory neuron makes only excitatory synapses on other neurons, while an inhibitory neuron makes only inhibitory synapses. A similar uniformity does not hold for the synapses received by a neuron, which can be a mixture of excitatory and inhibitory.
In other words, an excitatory neuron either says “yes” to all other neurons by spiking or abstains by remaining silent. Similarly, an inhibitory neuron chooses between “no” and abstaining. A neuron cannot say “yes” to some neurons and “no” to others, or “yes” at some times and “no” at others.
If an excitatory neuron hears many “yes” votes, it also says “yes,” conforming to the crowd. If an inhibitory neuron hears many “yes” votes, it says “no,” bucking the trend. In many brain regions, including the cortex, most neurons are excitatory. You could think of the brain as being like our society, which abounds in conformists but also harbors some contrarians.
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.
4. Neurons All the Way Down
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.
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