After a century of research, we are just beginning to understand the brain. Unlike the heart, or even our DNA, we do not have an in-depth sense of how the brain accomplishes its basic functions. How does it generate emotions? Make decisions? Learn? Become encultured?
Two issues come up as we try to grasp how the brain works. First, we think we know how we think. Our visceral sense of feeling and thought, as well as cultural traditions of how to understand ourselves, combine to provide some shallow illumination on how our brains work. But since Freud, it is apparent that our subjective experience and linguistically-mediated thought only scratch the surface of what our brain do. Even today, we still use inadequate metaphors to understand the brain – brains are like computers, except they really are not.
Second, unlike other organ systems, the brain does many things at once. The richness of our lives, from the four f’s to learning and language, runs through our brains. These multiple functions developed over evolutionary time, meaning that our evolutionary history has gifted us with an organ of cobbled contingency. The upshot is that even if we figure out something about the brain (per #1), it’s not immediately generalizable. While one can expect evolutionary tinkering – modifications on existing functions – there is also the potential for engineering different solutions to the many problems we need to solve every day.
For interdisciplinary efforts in brain science, including endeavors like neuroanthropology and cultural neuroscience, these two issues highlight an interesting problem. If there is not just one way the brain works, not one code (like DNA) that will unlock the brain’s mystery box, then we are in a situation where many people will develop partial answers. However, those answers are tentative, and there is no clear framework for integrating them. Put differently, we are dealing with a normal academic situation – multiple fields with multiple truths.
A metaphor that is often used to portray research on complex problems is that of blind men feeling an elephant. One touches the tail, another the trunk, a third the foot, a fourth the tusk, a fifth the ear. They argue over what animal they touch, each declaring that the evidence at hand indicates a specific type of animal. The story is often used to highlight that we have difficulty grasping a larger truth – that there really is one elephant there, the men are simply touching different parts of it.
But in the case of the brain, research points to multiple mammals, not one big grey beast. It as if one touches an elephant, another a rhinoceros, a third a hippopotamus. They might seem the same, but they are quite different animals. And one might even have stumbled across a gorilla that others simply don’t see as they busily pass their science ball hand to hand. In other words, the elephant in the room isn’t really an elephant, but a whole menagerie.
It can get even more confusing. We think (see #1) that the elephant, rhinoceros, and hippo might be closely related. They are big, they live in Africa, they eat plants, and so forth. But evolutionary research actually points to the closest relatives of the elephant as manatees and hyraxes. Looks can be deceiving, and what we think goes together might not. Elephants live on the savannah and in the forest, hyraxes on rocky terrain, and manatees in the ocean. Understanding the brain, then, might mean overcoming our lookalike assumptions and the then integrating information across very different scholarly ecologies.
In the meantime, it likely makes good sense for researchers to tackle how the brain works in conjunction within a specific ecology. The elephant’s multifunctional trunk here, the hyrax’s undescended testicles over there, a manatees’ cutting encounters with speed boats in the distance. These different types of problems might not fit together, but each represents different aspects of these evolutionary relatives’ lives.
But that’s not so juicy, is it? It doesn’t fit with what we already so cunningly know – it’s common sense – or with a need to find one overarching logic. But, remember, the brain is an alien technology. Folk psychology and computer technology, especially when combined, don’t make for a robust brain-ology. So when making generalizations, it often pay to be circumspect – or at least informed by science and technology studies – before sounding off on grand generalizations based more on the sexes of planetary systems and computers using 10% of their processing power than anything else.
Perhaps we need to recognize that we have multiple spirit animals in our brain. Elephant, hyrax, and manatee. And they animate us in ways like the phlogistons of old, fire elements that burn as we come alive. Sounds like it could be a best seller.
In the end, I am most struck by the multiple codes idea. That’s what evolution has gifted to us. So different ways of tackling the brain, and of using neuroscience to illuminate problems, are needed. History runs differently through brains than vision. Language is separate from circadian rhythms. We won’t find one code, because there isn’t just one.
This little essay was inspired by two recent pieces of writing and a video lecture. The oddness of biology and evolution, the fact of multiple codes as part of neural function, and the utter strangeness of the brain. Here they are, Gary Marcus, Christof Koch and Gary Marcus again, and Terry Sejnowksi.
Gary Marcus, The Trouble with Brain Science
Gary Marcus, professor of psychology at New York University, wrote an op-ed in the New York Times, taking on recent controversy over the direction of large-scale government funding into neuroscience. He illuminates that controversy because the funding is largely going to quite technical and ultimately narrow approaches towards understanding the brain. Many neuroscientists want something much broader. Marcus explains the background to why:
Different kinds of sciences call for different kinds of theories. Physicists, for example, are searching for a “grand unified theory” that integrates gravity, electromagnetism and the strong and weak nuclear forces into a neat package of equations. Whether or not they will get there, they have made considerable progress, in part because they know what they are looking for.
Biologists — neuroscientists included — can’t hope for that kind of theory. Biology isn’t elegant the way physics appears to be. The living world is bursting with variety and unpredictable complexity, because biology is the product of historical accidents, with species solving problems based on happenstance that leads them down one evolutionary road rather than another. No overarching theory of neuroscience could predict, for example, that the cerebellum (which is involved in timing and motor control) would have vastly more neurons than the prefrontal cortex (the part of the brain most associated with our advanced intelligence).
But biological complexity is only part of the challenge in figuring out what kind of theory of the brain we’re seeking. What we are really looking for is a bridge, some way of connecting two separate scientific languages — those of neuroscience and psychology…
We know that there must be some lawful relation between assemblies of neurons and the elements of thought, but we are currently at a loss to describe those laws. We don’t know, for example, whether our memories for individual words inhere in individual neurons or in sets of neurons, or in what way sets of neurons might underwrite our memories for words, if in fact they do.
Cristof Koch & Gary Marcus, Cracking the Brain’s Codes
Christof Koch, chief scientific officer of the Allen Institute for Brain Science, and Gary Marcus wrote the piece Cracking the Brain’s Codes in MIT Technology Review. It goes into greater depth on why a methods driven approach – computation and imaging ramped up – is not likely to answer many of the important questions society has about our brains. It’s rather like trying to train an elephant, a cute trick for a kid. But those sad eyes? And all just so it could sit for our entertainment?
I provide a short excerpt from the beginning, but this piece itself goes into greater depth with strong examples.
At stake is virtually every radical advance in neuroscience that we might be able to imagine—brain implants that enhance our memories or treat mental disorders like schizophrenia and depression, for example, and neuroprosthetics that allow paralyzed patients to move their limbs. Because everything that you think, remember, and feel is encoded in your brain in some way, deciphering the activity of the brain will be a giant step toward the future of neuroengineering.
Someday, electronics implanted directly into the brain will enable patients with spinal-cord injury to bypass the affected nerves and control robots with their thoughts. Future biofeedback systems may even be able to anticipate signs of mental disorder and head them off. Where people in the present use keyboards and touch screens, our descendants a hundred years hence may use direct brain-machine interfaces.
But to do that—to build software that can communicate directly with the brain—we need to crack its codes. We must learn how to look at sets of neurons, measure how they are firing, and reverse-engineer their message.
A Chaos of Codes
Already we’re beginning to discover clues about how the brain’s coding works. Perhaps the most fundamental: except in some of the tiniest creatures, such as the roundworm C. elegans, the basic unit of neuronal communication and coding is the spike (or action potential), an electrical impulse of about a tenth of a volt that lasts for a bit less than a millisecond. In the visual system, for example, rays of light entering the retina are promptly translated into spikes sent out on the optic nerve, the bundle of about one million output wires, called axons, that run from the eye to the rest of the brain. Literally everything that you see is based on these spikes, each retinal neuron firing at a different rate, depending on the nature of the stimulus, to yield several megabytes of visual information per second. The brain as a whole, throughout our waking lives, is a veritable symphony of neural spikes—perhaps one trillion per second. To a large degree, to decipher the brain is to infer the meaning of its spikes.
But the challenge is that spikes mean different things in different contexts. It is already clear that neuroscientists are unlikely to be as lucky as molecular biologists. Whereas the code converting nucleotides to amino acids is nearly universal, used in essentially the same way throughout the body and throughout the natural world, the spike-to-information code is likely to be a hodgepodge: not just one code but many, differing not only to some degree between different species but even between different parts of the brain. The brain has many functions, from controlling our muscles and voice to interpreting the sights, sounds, and smells that surround us, and each kind of problem necessitates its own kinds of codes.
Terrence Sejnowski, Suspicious Coincidences in the Brain
To finish up, the Terry Sejnowski lecture from 2012. Here’s where I got that great line about the brain as alien technology.
In this 2012 talk, Sejnowski, Francis Crick Professor at The Salk Institute for Biological Studies, addresses the brain’s complexity. He opens (about the 6:40 mark) by speaking about trying to reverse engineer the brain as a way to understand it, much like we do trying to figure out how a new product from Apple or Samsung or Sony work. Engineers can do that today with products that other people have made. But the same route doesn’t apply to the organ of our lives.
The problem with taking that approach to the brain is that we don’t know [enough]. It’s basically an alien technology. We don’t really know the basic principles. And if you don’t know the basic principles behind something and you try to copy it, it’s basically a cargo cult.
We hastily arrange something on our altar of technology, and think that will give us the riches that come from our brain. We try to build something that looks like a plane, hoping that will get us into the sky. But we remain grounded, because we don’t understand the engine or the aeronautics or anything much else.
Just being able to see, for example. To recognize where we are, the scene and the people in the scene and the details and remember the context and also just to be able to walk around and do things in a complex world under uncertain conditions is a miracle. We can’t build robots that do that, but somehow nature managed to do it, and that’s really the question.