The Universal Sense (34 page)

Each neuron has a characteristic sound depending on its condition and input/output state. But contrary to the increasingly passé “neuron theory,” which suggests that if we could
just get down to the smallest individual element of the brain we could finally see how it all works, those of us who spend a lot of time recording neurons realize that, individually, they don’t mean much to a living organism. Outside the experimental recording area is a whole living brain with billions of neurons firing, each with its own electrical pattern based on what it and its neighbors and all the other neurons that seem to be unrelated to the stimulus are doing. Thousands of neurons die every day and few are replaced, yet cognitive or functional degradation takes decades to notice or, for some lucky portion of the population, doesn’t change at all until the final off switch is thrown.

Complex computation, found in any brain more complicated than a few neurons floating in a culture dish, requires ensemble or population coding, hundreds or thousands or millions of neurons working in gangs to isolate features and characteristics of fuzzy input, figure out what bits go together, and put together complex perceptions on the way to something more interesting, such as consciousness. To encode (and decode) reality, which is a very noisy place, requires a system capable of taking the noise and changing it into useful signals with neuronal equivalents of filters and amplifiers, modules that while running small programs gather this noise and interact in loops of input, turning stochastic noise into quantal percepts—this tone, those smells, that color.

One mistake that people make when thinking about filters is falling prey to the idea that since filters remove information, the filtered output is necessarily less complex and more constrained than the original input. This underlies a lot of philosophical statements that the human experience is but a subset of reality—the path from physics to sensation to perception gets
narrower as you go. But our perceptual filters no more act in isolation than do our neurons. The output from two (or two thousand, which is more biologically likely) filters, due to the multitude of integrations that occur from divergent neuronal populations, can and will interact. The filtered outputs may be parallel, in which case they will sum and strengthen specific inputs, or they may be completely opposite, generating neuronal and perceptual cancellation, but often they will resonate and beat against each other to create new response patterns. These neuronal filters are not blinders—they are what allow us to take the noise and get a handle on it at biological speeds, not worrying overly much about femtosecond changes in thermal energy of vibrating atoms and focusing instead on the lush harmonies of rising strings.

Depending on the brain’s state (which depends on whether you’re awake, asleep, aroused, thinking, reading this book, or scratching), certain neuronal activities will be amplified and others attenuated; however, some trends will remain across all conditions as long as the organism is alive. These differences are analogous to filters and amplifiers in a sound system and are based on individual differences in the brain. Your regular habits become encoded in your brain as defaults: you are comfortable paying attention to certain inputs and ignoring others. These defaults are flexible, allowing you to enjoy your favorite songs but be delighted at hearing a new one that you like or switching off one that makes your head hurt.

The differences between individual brains derive from development, environment, health, culture—almost anything that has made its mark on the living being. With 10 billion to 100 billion neurons to work with and trillions of synapses in play, strengthening, weakening, beating, and looping at biologically relevant
rates, the outputs create a personalized neuronal signature, a sort of neuronal timbre, as individualized as the sound from a century-old musical instrument. If you dare to think of the brain as a whole, composed of billions of neurons, each with its own dynamic sound changing along with its function, the function of the brain as a whole is reflected in a metasound—a neuronal orchestra.

Think back to psychoacoustics and the chapter on music. A song is an epiphenomenon, a whole that is more than the summed aspects of the physical acoustics of the instruments, the architectural acoustics of the performance space and/or the recording gear, the level of skill of the musicians, and the effects of what they had for breakfast the morning of their performance. In the same way, all the individual firings of neurons, transportation of fluids, and activation and deactivation of genes that occur in the normal function of the brain give rise to something more than a collection of sonifiable neural responses—they produce a mind.

No one person, lab, or field understands the brain or the mind. But with 30,000 neuroscientists attending the Society for Neuroscience’s annual conference, you get a feel for how incredibly deeply we want to understand it. Wandering through posters covering cutting-edge work before it’s even published, hearing talks about new directions based on unexpected breakthroughs, realizing that entire careers can be focused on trying to comprehend how a single molecular channel microns across contributes to a structure that may have trillions of similar molecules, you begin to get some idea both of how much there is to learn and of how long it will be before we can truly put it all together. The workings of the human brain and the mind that arises from it are as much unexplored territory as interstellar space is.

There is certain uneasiness among a lot of neuroscientists about using the word “mind.” While it is frequently thrown around, often somewhat sloppily, in cognitive or psychological papers, you don’t usually hear a neuroscientist talking about the mind until well after she’s gotten her Nobel Prize and it’s safe to do so. As a non-Nobel winner, I tend to focus on the function of the brain, most likely the home of the mind. But the brain is no more the mind than the seed is the sunflower. It is the place from which the mind grows, develops, emerges, functions, and eventually fades. I and most of my colleagues search for the mind in our data—sheaves of electrical tracings, graphs of neural responses, ornate illustrations of stimulus and response. Those with a more molecular tilt search for its underpinnings in the slightest change in cellular behavior. Those who do neuro-imaging hope that by viewing a living, unanesthetized working brain, they will glimpse that subtle epiphenomenon emerging.

But all of our studies tend to be problematic. The mind can’t be found in the slow temporal smears of fMRI or the vast baroque operations of genetics. These are too far from the temporal domain in which our minds seem to operate. Nor has it been found in the fast-moving but spatially gross architecture of an EEG, or in the accurate but constrained single-neuron pulse of an electrophysiological recording. Over the last fifty years we have looked into working brains of humans and our kin; seen complex metabolic and electrical changes in response to subtle sensory and cognitive inputs; explored the biochemical underpinnings of thought, imagination, and language; crowed when a gene was found that seemed to underlie human specialness and then grumbled quietly when it was found in hundreds of other living things, doing subtly different things to and in their brains. And yet we are still identifying the pieces.
We are splitting the underpinnings of the mind into smaller and smaller elements, much as early particle physicists used larger and larger particle colliders to find smaller and smaller elements of matter. But the overview, a theory of everymind, still eludes us. And despite the technology we throw at it, in our recursive way, we still need to simplify things to try to figure out how to put all the disparate pieces together.

The one thing we can do with all these unassimilated pieces is build models. Models are small-scale representations of what we think might be happening, based not only on past research but also on our prediction of where our data will lead us. But our ability to predict the future is uneven, and models are fragile things. Breakthroughs can completely unravel years of scientific theories, so even bold models from brilliant minds are subject to constant revamping and reevaluation. This is the basis of honest science. There have been dozens of models proposed to describe how the mind works and what it is, or at least what it is
like
, and each of them arose from the public interpretation of what was cutting-edge science at the time.

During the age of telephony, the mind was described as a switchboard, bidirectionally connecting different brain modules the way an old telephone network would connect phones. In the 1950s, in the early days of laser experimentation and holography, the stability of the mind and the ability to retain memory even after traumatic brain damage led some to describe it as a holographic device, with every bit of the brain containing a holistic memory of the entire mind. In the 1970s, as computers began to become more commonplace in the lab, the mind was described as a computer, and shortly thereafter computers were designed to try to model aspects of how the mind worked, bringing about the first steps into developing artificial intelligence. By
the late 1990s, as our ability to work more easily with genetics expanded with the introduction of polymerase chain reaction techniques and gene sequencing equipment, the mind started being viewed as something that emerged from the confluence of genetic tendencies and environmental conditions, a sort of neurogenetic entity. As we entered the twenty-first century and our computers, phones, and even books started merging into a single interconnected web of information, people started suggesting that intelligence and mind emerge from the sheer complication and mass of data processing. And now, as we enter the second decade of the twenty-first century, our wealth of information has brought us no closer to a really useful model of the mind. Some even suggest that the current focus on fMRI and some other forms of neuroimaging has sent us backward, substituting pretty pictures of living brains for an understanding of what those brains are actually doing.

I’ve thought about this for many years and had many discussions about it (and a few knock-down, drag-out fights). I’m convinced that if we really want to get to the root of what mind is, we should stop diving deeper into data and actually
think
about the knowledge that we already have, preferably in new ways. One of the advantages of trying to write a book that covers a subject as huge as sound and the mind is that you are forced to step out of your normal daily perspective and try to figure out how things work in the real world rather than just the lab. And during one of the days I spent thinking rather than writing, I remembered the minor war that broke out in my world when it was suggested that bats may be able to respond to sounds that differ only on the nanosecond scale (orders of magnitude faster than their nervous system could possibly cope with). This could have just been another one of those in-house battles between
neuroscientists that never escapes the bounds of academia. But still, the problem bothered me: How can a bat respond to a change in a signal only a few hundred nanoseconds long when their nerves fire on a scale thousands of times slower than that, and by all the classical models of brain function can’t even detect changes hundreds of times slower?

When I started thinking about that in a larger format, I realized something about brains in general: if we want to look for the mind, we’ve been looking in the wrong place and at the wrong time. We tend to scale our thinking about how the brain functions based on what we think of as its basic units—its nerves and, to a lesser extent, the supporting tissue associated with them, such as glia and blood vessels. But each nerve, depending on where it is in the brain, can have thousands or millions of predecessors, all contributing not just the yes/no of an impulse but slight modulations of the tendency to fire or not. What we detect with any of our narrow-sighted techniques is just a view through a tiny temporal window of an extraordinarily complex neurochemical orchestral arrangement, not just shifting the charged ions that flow in response to the order to fire or not, but creating states of potential change. Will the three hundred excitatory inputs to this hippocampal cell be overridden by the forty inhibitory inputs? Will the built-in gap junctions contribute enough to the currents to change an onset response to a series of periodic pulses that swamp further inputs? And will all this actually take that sound that I just heard and turn it into a memory or just put it on hold to compare it to what comes next or what has come before? At any given site in the cortex, where 10 billion neurons may have fired before, it may be that tension, that moment between when all the summed inputs have arrived and when an output is chosen—to sing, to
say a word, to smile, to have that neurochemical rush that is the moment of epiphany—in which the mind hides. Think of it as a kind of mental quantum foam, where the possible probabilistic states of what will happen next collapse.

Our minds are built from our past experiences, whether biochemical or environmental. Our experiences as we develop cause us to amplify some types of experiences, wonderful or traumatic, and filter out others than have been of limited usefulness or interest in the past, modulated in intensity by choice and circumstances. In short, our minds are built moment by moment within the tension between input and output, formed by our life experiences just as the breath from a sax player shifts the energy in the body of the instrument, adding the reverberations of the metal and the room, amplifications and filtering of lungs, lips, face, and instrument, guided by hands. And what emerges is not just modulated air but music.

One of the best ways to discover new things is to look at them a new way. So: is the mind itself music?

The idea of the musical mind is not a new one. You can find the phrase in the titles and subtitles of at least fifty books and journal articles from professional journals as lofty as
Science
. But it usually refers to how behavior or neural function changes when exposed to elements of music or how the musically trained may be different from the musically naive. What I’m suggesting is that we think about the mind in the ways we think about music, as a process to be explored instead of a thing to be identified. The failure (so far) of science to define music comes from its focus on the infrastructure rather than the process and flow. A melody or song is not just a string of notes of known duration and loudness. It is not what activates the left versus the right amygdala. It is not what makes you smarter or dumber. It is the
tensions between the discrete events and the flow of what comes next. And perhaps that is the mind as well, with the brain driving the loops of massed neuronal activities, each contributing a bit of filtering, a bit of amplification, a change of tempo to signal something worth attending to or an increase in noisiness when things get confusing. Perhaps
between
the moments of tension and release of neuronal activation and inhibition is the emergent portal of consciousness. The mind, like music, seems likely to me to be more about the flow of information than the information itself, popping into consciousness after all input has arrived, from the quantal to the conversational, when all input is processed, filtered, amplified, and streamed by the parameters that make up the emergent mind; the moment between the wordless thought and the word.