The only current solution to what is called the “horizon problem” was first proposed by Alan Guth and colleagues of Stanford, in 1980, who suggested that shortly after the initial explosion there was something called the inflationary period, where the universe suddenly entered an accelerated expansion, and then once again slowed down into the more or less constant expansion that we observe today. In that case things that were close together were suddenly pulled apart in this faster-than-light expansion and now find themselves even farther apart as the universe has continued expanding at a more “normal” rate. Because this happened so soon after the initial event (somewhere in the first 10–35 seconds), it may be that the entire observable universe we find ourselves in now was no more than a 1 centimeter patch at the moment of inflation. I should say that this is not just some idea pulled out of a hat, although it may sound that way. There are good reasons to believe that in the very, very early universe conditions were ripe for this sort of accelerated expansion. An inflationary period in the evolution of the universe solves numerous problems besides the “event horizon” and is now generally accepted in one form or another, but the proof is admittedly still thin.
One of the main proofs for the inflationary model has come from the work of experimental physicists like Amber Miller, measuring with exquisite precision the cosmic background radiation that presumably arose just after this accelerated expansion when the universe had its “hot big bang” and separated matter from energy. What Miller and others have found may at first sound like the opposite of proof because what they have detected are very tiny disturbances in that smooth background radiation. These are called anisotropies, an unfortunately difficult-sounding word standing for a relatively simple idea that means that the cosmic microwave background is not the same in all directions—there are smudges and clumps here and there. Now these smudges would have been the result of very small quantum fluctuations in this centimeter-sized piece of universe, and they are then stretched to astronomical scales by the inflationary expansion. Over hundreds of millions of years these sparse densities coalesced into galaxies and stars and planets—indeed, we wouldn’t be here if it were not for them and inflation.
Here is a case that blends all the best of science—a serendipitous discovery (cosmic microwave background radiation) that was the result of building a more sensitive instrument (the radio telescope) that gave rise to a wildly imaginative theory of the beginning of the universe (inflation) intended to solve some deep paradoxes in the accepted theory (Big Bang) that motivated experimentalists to make more sophisticated devices to gather more sensitive measurements that led to a theory of how galaxies and stars and our planet—and we—arose out of the primordial dot. And the circle isn’t closed; theorists are still at work and Miller is still launching balloons, now looking for ephemeral gravitational waves—“if they’re there”, she says—another black cat in a dark room.
We have taken a quick tour of physics and cosmology using three researchers who exemplify different approaches to asking how the universe really is. Brian Greene is concerned with the deepest questions of how to describe a universe that we can’t really imagine but may be able to solve mathematically; David Helfand sees solving some very hard but accessible problems by using the universe as his laboratory and thereby creating a long list of new questions; and Amber Miller wants to know about a moment in time that occurred so many billions of years ago that one could call it creation and that may contain a fundamental limit to what we can know of our universe.
This case history also brings up an important point about the uses of ignorance and also what it won’t do. By emphasizing the questions, you have heard and understood, I hope, some of the most sophisticated issues of modern cosmology and physics. But can you solve them? No. You don’t have the tools, the mathematics, the intuition, the technical expertise, to actually be a physicist. Science is indeed a technical and sometimes difficult activity that requires training and experience—lots of it. But is that the point? What is critical is not that everyone in America becomes a scientist. Rather it is that everyone can understand what’s going on, what the stakes are, what the game is about. Science is not exclusionary; it does not belong to a small coterie of eggheads speaking a secret language. You can watch a sporting event and enjoy it without having the training or skills of an athlete. You can enjoy a painting or a symphony without possessing any of the know-how of an artist or a musician. Why not science? There is no more sense in getting hung up with the details of experimental results or systems of differential equations than there is with chord structures and harmonies in a musical composition.
3. THAT THING YOU THINK YOU THINK WITH
The smartest thing I’ve ever heard said about the brain was from the comic Emo Philips. “I always thought the brain was the most wonderful organ in my body; and then one day it occurred to me, ‘Wait a minute, who’s telling me that?’”
And that gets right to the heart of the matter, if you will pardon me that twisted metaphor.
Because you see, the single biggest problem with understanding the brain is having one. Not that it isn’t smart enough. It isn’t reliable. The first-person experience of having a brain is just not remotely similar to any third-person explanation of how it works. We are regularly fooled by our brains. Constructed as they were from evolutionary pressures directed at solving problems such as finding food before becoming food, they are ill equipped to solve problems like how they work. Just as quantum mechanical descriptions of the physical world are weirdly unintuitive to our brains, so biological and chemical explanations of the brain are even more weirdly unintuitive to itself. I mentioned in the last case history that a hard problem for astrophysicists and cosmologists is one of perspective—they are studying something, the universe, that they live inside of. You could say the same thing about your brain.
Take, for example, trying to figure out what are the most important questions to ask about the brain. In our terms here: where’s the best ignorance? For more than 50 years the visual system has served as one of the premier model systems for brain research. The retina, a five-layered piece of brain tissue covering the inside of the back of your eyeball, has been dubbed a tiny brain, processing visual input in a complexly connected circuit of cells that manipulate the raw image that falls upon it from the outside world, before sending it along to higher centers in the brain for yet more processing, until a visual perception reaches your consciousness—and all in a flash of a few dozens of milliseconds. A batter in a professional baseball game has less than 400 milliseconds to make up his mind about whether to swing at a 3-inch diameter sphere traveling the 60 feet and 6 inches from the pitcher at 90 miles per hour. Since making the decision and the coordinated muscle movement of swinging the bat take up part of this time, the work of the visual system must be accomplished in something like 250 milliseconds. Pretty fancy stuff that can do that, no? Certainly worth figuring that out, no? This must be some high-class brain ignorance. Well, let’s see.
Because of the apparent difficulty of visual tasks and the effortlessness with which we seem to accomplish them, the visual system has been regarded as one of the highest developments of evolution. Indeed, one of the most common arguments made against evolution, and one that even worried Darwin, is how something as marvelously complex as the eye could have developed in small steps by random mutations. (In fact, it appears that visual systems of varying sorts, some better than those found in mammals like us, have evolved as many as 10 different times in evolution—counterintuitively, it seems to be fairly easy to evolve eyes.) Being such visually oriented animals, we have understandably assumed that vision is a very high-order brain process and that studying vision will therefore tell us a lot about how the brain does all the other amazing stuff it does. There are so many neuroscientists working on the visual system that they have formed a subfield that has its own annual meeting to discuss current research. The Association for Research in Vision and Opthamology (ARVO) has more than 12,000 membe
rs. The eye is a perfect example of a model system—the retina is accessible, well-organized (meaning that it has a limited number of cell types that are connected to each other in stereotypical patterns called circuits—that is, one can make a wiring diagram of the retina much the way one could for a radio), and it performs a straightforward if complicated task. It can therefore be investigated for its own sake, and also because it will reveal fundamental principles about how the whole brain works. So far so good.
At what might be considered the opposite end of the scale is a task like walking—mundane, simple, something every healthy person over the age of 12 months does. It feels thoughtless, reflexive, unconscious, so we take it for granted because it seems to use so little of our brain power. Running to first base certainly appears far less neurologically demanding than getting the bat on the ball in the first place.
But it happens that we have become fairly adept at producing technology that mimics the visual system—photography, television, movies, pattern recognition algorithms. Doing what the visual system does can at least be imperfectly reproduced in our technology. Not so true for walking. More than a century of robotics research has failed to produce a machine that can walk more than a few steps on two legs, let alone go backward or up a slightly sloping plane, not to even mention steps. Walking around on two legs is in fact in many ways a more complex and demanding mental task than much of what goes on in the visual system. Daniel Wolpert, of Cambridge University, is fond of pointing out that IBM’s Deep Blue supercomputer is capable of beating a grand master at the game of chess, but no computer has yet been developed that can move a chess piece from one square to another as well as a 3-year-old child.
So what’s the more complex thing for the brain to do? Seeing or moving? Which would make the better model system for understanding how brains work? Have 12,000 neuroscientists been looking at the wrong model system? The short answer: Quite possibly.
The nervous system is often divided up into two main functional branches: sensory systems and motor systems. There are other ways to make distinctions between the various functions of the nervous system (conscious versus instinctive, for example), but this seems the most fundamental, and the sensory and motor divisions tend to divide neuroscientists as well. In much of neuroscience, you are either a sensory or a motor researcher.
The sensory systems include the five basic senses of vision, audition, touch, olfaction, and taste, although there are many more senses that could be included in this list, which hasn’t been substantially revised since Aristotle first enunciated it. For example, just within touch there is pain (piercing and throbbing), temperature, itch, friction and rubbing, and hard and soft touch. Then there is the sixth sense, proprioception, a word that I still have to sound out to say, that simply means knowing the position of your body, and in particular of your head, at any moment. It is rarely listed as one of the main senses, but without it the world would jump around in a dizzying manner, you would be unable to stand or sit, let alone walk, and it is doubtful that you would even have a real sense of yourself in the world.
Motor systems refer to the parts of the brain that initiate and control action or behavior, that is, movement. Some of those movements can be the big ones like reaching for something, performing an athletic feat, or walking, and some of them can be quite small and unconscious like the tiny but constant movements of your eyes called saccades, or regular and repeatable movements like chewing or breathing.
Admit it though, even you find all that motor system stuff less appealing than the cool sensory systems that give us the perception of a beautiful painting, concert, perfume, or sumptuous meal, that allow us to appreciate a majestic landscape or a pretty face; plus they warn us of danger, keep us from bumping into things, and generally seem to make life more interesting. But the motor system—it even sounds like a boring machine, a kind of vocational rather than intellectual part of the nervous system—may in fact hold the key to our cognitively rich lives. This is why having a brain is such an obstacle to understanding how one works. Our opinions about what parts of it are more interesting or more complex are so terribly biased as to be nearly worthless—worse than worthless, they are obstacles.
Consider that one of the highest cognitive abilities we possess is language. Communicating ideas through speech is perhaps uniquely human and unquestionably has allowed us to develop and transmit all the important trappings of culture—art, history, philosophy, science. And this most cognitive of all brain activities is basically a motor act—we speak by controlling and coordinating a vast array of muscles in our chests, throats, tongues, and lips. Andre Breton, the leader of the Surrealist movement, if it can properly be said to have had a leader, once remarked that the speed of speech is faster than that of thought. Yes, we can all believe that, having let some utterance escape from our mouths that we are immediately sorry for. But aside from the humorous implications, given a moment’s thought, it’s clearly true. We don’t think long, if at all, about the words we are saying in the middle of a conversation; they just “come out.” All of this supposedly high-level cognitive apparatus in the brain ends up as an almost reflexive motor act.
So the brain and how it works may be the biggest question in biological science, but have we got the right small questions? From philosophers to undergraduates we all seem fascinated with the debate about whether it’s even possible for a brain to understand itself. Throughout history it has always been compared to the most complicated technology of the day—it was all pneumatics and hydraulics to Aristotle and the Greeks and Romans of antiquity with their fabulous aqueducts and sewage systems; then it was a clockwork-like gadget when newly invented timepieces full of springs and miniature levers got everyone to church on time and started humans on the miserable path to deadlines and scheduling; then it was a complex engine in the Industrial Revolution and more recently a computer; and today it is compared, predictably, to the Web. The two things that are common about all of these comparisons are that they recognize the brain as a very complicated thing, and they are all otherwise mechanistically incorrect. We still don’t know how it works.
There’s a lot that could be said about the brain and about the field of neuroscience, especially about what we don’t know. Anything written about the brain is necessarily incomplete, and that is no less true for this case history in neuroscience. Thus, I have resorted to constructing a case history by piecing together the work of multiple neuroscientists. I have focused on three in particular who are trying to understand the brain by understanding, first of all, that our current knowledge, the result of decades of modern brain research, rich as it is, has a bias and may be sending us off in some mistaken directions. The neuroscientists in this selected group, who work with and represent a much larger contingent of colleagues who I am not mentioning specifically, are going back before some of our current conceptions arose, asking fundamental questions that were assumed to be settled. In other, simpler words, they are actually creating new areas of ignorance where we thought things were already known. And this is progress.
Larry Abbott is a theoretical neuroscientist. That is, he is a real person and a real scientist, but he asks questions about brain function by using computer-generated mathematical models of how bits and pieces of it might work. The value of this kind of thing is that he can ask questions for which there are currently no good experiments, for which the technology isn’t available or there are ethical considerations. Computer-generated mathematical models can use a million neurons at a time, or more, while experimentally it is impossible to check the activity of each of even a few neurons. Models do this by using statistics and it gets frankly complicated mathematically, so it’s best to leave this part to the professionals. But just to give you a flavor for how it can work, imagine that you wanted to figure out the air pressure in the room where you’re sitting. You could use a model that described the average behavior of air molecules, and it would not be essential to know the actual activity—the position, speed, a
nd direction—of each air molecule at each moment to know the room’s air pressure. The average behavior of a very large number of individual particles can result in a very precise value. So it may be with the brain, where knowing the average activity of neurons under different circumstances could predict with great accuracy how the brain performs a task, even a complex one.
Theoretical work in biology is relatively new compared to physics, where it has a long and rather successful history. Indeed, Abbott, like many other theoretical neuroscientists, was trained initially as a physicist. The application of mathematics to biological problems has come slowly, but nowhere as rapidly or as importantly as in studies of the brain. This is not without resistance because the mathematical skills necessary to do this work are not generally part of the training of a typical biologist. A lot of what theorists do has the aura of cabalistic symbolism—multiple equations and odd symbols pile up to cover the pages in their journal articles. “Simplifying” assumptions are made that don’t seem obvious, or simple. Among experimentalists there is often a suspicion that all this mumbo jumbo is just a bluff hiding a lack of data.
I made a comparison of neuroscience to quantum physics earlier in this case history, about how it is so unintuitive to our brains. To take that a step further, the difference between modern physics and brain science is that the unintuitive thoughts one has to think in physics can be done with the language of mathematics. We don’t use math in biology that way—at least not yet. The objection to equations most often voiced by biologists is that they oversimplify, that you cannot capture the complexity of biology, of a biological system, whether it be a single cell or a whole animal, in an equation. Nonsense, I say. You can capture the whole physical universe in a few of them. This is another case of pre-Copernican thinking creeping into our reasoning as received wisdom. The brain seems complicated, so its explanation must be also.
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