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Text

February 1998

ISBN 0-262-53154-2 264 pp.

$24.00/£15.95 (PAPER)

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Cloth (1996)

Series

Bradford Books

Recommended Reading

Notes

The Author

About the Artists

Index

Prologue

There may be nothing new under the sun, tut

permutation of the old within complex systems

can do wonders.

STEPHEN JAY GOULD, 1977

THIS IS A BOOK about thought, memory, creativity, conscious-ness, narrative, talking to oneself, and even dreaming. In a book that parallels this one, How Brains Think, I explored

those subjects in a general way but here I treat them as some of the predicted outcomes of a detailed darwinian theory for how our cerebral cortex represents mental images — and occasionally recombines them, to create something new and different.

This book proposes how darwinian processes could operate in the brain to shape up mental images. Starting with shuffled memories no better than the jumble of our nighttime dreams, a mental image can evolve into something of quality, such as a sentence to speak aloud. Jung said that dreaming goes on contin-uously but you can't see it when you are awake, just as you can't see the stars in the daylight because the sky is too bright. Mine is a theory for what goes on, hidden from view by the glare of waking mental operations, that produces our peculiarly human type of consciousness with its versatile intelligence. As Piaget emphasized, intelligence is what we use when we don't know what to do, when we have to grope rather than using a standard response. In this book, I tackle a mechanism for doing this exploration and improvement offline, how we think before we act and how we practice the art of good guessing.

Surprisingly, the subtitle's mosaics of the mind is not just a liter-ary metaphor. It is a description of mechanism at what appears to be an appropriate level of explanation for many mental phenomena — that of hexagonal mosaics of electrical activity, competing for territory in the association cortex of the brain. This two-dimensional mosaic is predicted to grow and dissolve, much as the sugar crystals do in the bottom of a supersaturated glass of iced tea. Looking down on the cortical surface, with the right kind of imaging, ought to reveal a constantly changing patchwork quilt.

A closer look at each patch ought to reveal a hexagonal pattern that repeats every 0.5 mm. The pattern within each hexagon of this mosaic may be the representation of an item of our vocab-ulary: objects and actions such as the cat that sat on the mat, tunes such as Beethoven's dit-dit-dit-dah, images such as the profile of your grandmother, a high-order concept such as a Turing Machine

— even something for which you have no word, such as the face of someone whose name you haven't learned. If I am right, the spatiotemporal firing pattern within that hexagon is your cerebral code for a word or mental image.

THE OTHER PHRASE IN THE BOOK'S TITLE that is sure to be mistaken

for literary license is, of course, the cerebral code. The word "code" is often only a short way of saying "unlocking the secrets of and newspaper headline writers love such short words. Neurobiolog-ists also speak loosely about codes, as when we talk of "frequency codes" and "place codes," when we really mean only a simple mapping.

Real codes are phrase-based translation tables, such as those of bank wires and diplomatic telegrams. A code is a translation table whereby short abstract phrases are elaborated into the "real thing." If s similar to looking up ambivalence in a dictionary and getting an explanatory sentence back. In the genetic code, the RNA nucleotide sequence CUU is translated into leucine, the triplet GGA into glycine, and so on. The cerebral code, strictly speaking, would be what we use to convert thought into action, a translation table between the short-form cerebral pattern and its muscular implementation.

Informally, code is also used for the short-form pattern itself, for instance, a nucleotide chain such as GCACUUCUUGCACUU. In this book, cerebral code refers to the spatiotemporal firing pattern of neocortical neurons that is essential to represent a concept, word, or image, even a metaphor. One of my theoretical results is that a unique code could be contained within a unit hexagon about 0.5 mm across (though it is often redundantly repeated in many neighboring hexagons).

It was once thought that the genetic code was universal, that all organisms from bacteria to people used the same translation table. Now it turns out that mitochondria use a somewhat differ-ent translation table. Although the cerebral code is a true code, it surely isn't going to be universal; I doubt that the spatiotemporal firing pattern I use for dog (transposed to a musical scale, it would be a short melody, perhaps with some chords) is the same one that you use. Each person's cerebral codes are probably an accident of development and childhood experience. If we find some commonality, for example, that most people's brains innately use a particular subset of codes for animate objects (say, C minor chords) and another subset (like the D major chords) for inanimate objects, I will be pleasantly surprised.

An important consequence of my cerebral code candidate, fall-ing out of the way in which cortical pattern-copying mechanisms seem capable of generating new categories, is that ascending levels of abstraction become possible — even analogies can compete, to help you answer those multiple-choice questions such as "A is to B as C is to D,EF." With a darwinian

process operating in cerebral cortex, you can imagine using stratified stability to generate those strata of concepts that are inexpressible except by roundabout, inadequate means — as when we know things of which we cannot speak. Thaf s the topic of the book's penultimate chapter, "The Making of Metaphor."

AS A NEUROPHYSIOLOGY with long experience doing single neuron recordings in locales ranging from sea

slug ganglia in vitro to human cerebral cortex in situ, I undertook this theoretical venture about a decade ago. I didn't set out to

explain representations, or even the nature of working memory. Like most people in neurobiology, I considered such questions too big to be approached directly. One had to work on their found-ations instead.

Back then, I had a much more modest goal: to seek brain analogies to the darwinian mechanisms that create higher-order complex systems in nature, something that could handle Kenneth Craik's 1943 notion of simulating a possible course of action before actually acting. We know, after all, that the darwinian ratchet can create advanced capabilities in stages, that if s an algorithmic process that gradually creates quality — and gets around the usual presumption that fancy things require an even fancier designer. We even know a lot of the ins-and-outs of the process, such as how evolution speeds up in island settings and why it slows down in continental ones.

However attractive a top-down cognitive design process might be, we know that a bottom-up darwinian process can achieve sophisticated results, given enough time. Perhaps the brain has invented something even fancier than darwinism, but we first ought (so I reasoned) to try the darwinian algorithm out for size, as a foundation — and then look for shortcuts. In 1987,1 wrote a commentary in Nature, "The brain as a Darwin Machine/' propos-ing a term for any full-fledged darwinian process, in analogy to the Turing Machine.

Indeed, since William James first discussed the matter in the 1870s during Charles Darwin's lifetime, darwinian processes have been thought to be a possible basis for mental processes, a way to shape up a grammatically correct sentence or a more efficient plan for visiting the essential aisles of the grocery store. They're a way to explore the Piagetian maze, where you don't initially know what to do; standard neural decision trees for overlearned items may suffice for answering questions, but something creative is often needed when deciding what to do next — as when you pose a question.

When first discovered by Darwin and Wallace and used to explain the shaping up of new species over many millennia, the darwinian ratchet was naturally thought to operate slowly. Then it was discovered that a darwinian shaping up of antibodies also

occurs, during the days-to-weeks time course of the immune response to a novel antigen. You end up with a new type of antibody that is a hundred times more effective than the ones available at the time of infection — and is, of course, far more numerous as well. What would it take, one asks, for the brain to mimic this creative mechanism using still faster neural mechan-isms to run essentially the same process? Might some milliseconds-to-minutes darwinian ratchet form the foundation, atop which our sophisticated mental life is built?

As Wittgenstein once observed, you gain insights mostly through new arrangements of things you already know, not by acquiring new data. This is certainly true at the level of biological variation: despite the constant talk of "mutations," if s really the random shuffle of grandparent chromosomes during meiosis as sperm and ova are made, and the subsequent sexual recombinat-ion during fertilization, that generates the substantial new variations, such as all the differences between siblings. Novel mental images have also been thought to arise from recombinat-ions during brain activity. In our waking hours, most of these surely remain at subconscious levels—but many are probably the same sorts of juxtapositions that we experience in dreams every night. As the neurophysiologist J. Allan Hobson has noted:

Persons, places, and time change suddenly, without notice. There may be abrupt jumps, cuts, and interpolations. There may be fusions: impossible combinations of people, places, times, and activity abound.

Most such juxtapositions and chimeras are nonsense. But during our waking hours, they might be better shaped up in a darwinian manner. Only the more realistic ones might normally reach consciousness.

THE MECHANISTIC REQUIREMENTS for this kind of darwinian process are now better known than they were in the 1870s; they go well beyond the selective-survival summary of darwinism that so often trivializes the issue. Charles Darwin, alas, named his theory natural selection, thus leading many of his followers to focus on

only one of what are really a half-dozen essential aspects of the darwinian process. Thus far, most "darwinian" discussions of the brain's ontogeny, when examined, turn out to involve only several of the darwinian essentials — and not the whole creative loop that I discuss in later chapters.

I attempted to apply these six darwinian attributes to our mental processes in The Cerebral Symphony and in "Islands in the mind/7 published in Seminars in the Neurosciences in 1991, but at that time I hadn't yet found a specific neural mechanism that could turn the crank. Later in 1991,1 realized that two recent developments in neuroscience — emergent synchrony and standard-length intracortical axons — provided the essential elements needed for a darwinian process to operate in the super-ficial layers of our cerebral cortex. This neocortical Darwin Machine opens up a broad neurophysiological-level consideration of cortical operation. With it, you can address a range of cognitive issues, from recognition memory to higher intellectual function including language and plan-ahead mechanisms — even figuring out what goes with the leftovers in the refrigerator.

DESPITE THE HERITAGE from William James and Kenneth Craik, despite the recent interdisciplinary enthusiasm for fresh darwinian and complex adaptive systems approaches to long-standing problems, any such darwinian crank is going to seem new to those scientists who have little detailed knowledge of darwinian principles beyond the crude "survival of the fittest" caricature.

For one thing, you have to think about the statistical nature of the forest, as well as the characteristic properties of each type of tree. Population thinking is not an easily acquired habit but I hope that the first chapter will briefly illustrate how to use it to make a list of six essential features of the darwinian process — plus a few more features that serve as catalysts, to turn the ratchet faster. Next comes a dose of the local neural circuits of cerebral cortex, as that is where the triangular arrays of synchronized neurons are predicted, that will be needed for both the coding and creative complexity aspects. This is also where I introduce the hexagon as the smallest unit of the Hebbian cell-assembly and

estimate its size as about 100 minicolumns involving 10,000 neurons (ifs essentially the 0.5 mm macrocolumn of association cortex, about the same size as the ocular dominance columns of primary visual cortex but perhaps not anchored as permanently). This is where compressing the code is discussed and that puts us in a position to appreciate how long-term memory might work, both for encoding and retrieval.

About halfway through the book, we'll be finished with the circuitry of a neocortical Darwin Machine and ready to consider, in Act II, some of its surprising products: categories, cross-modality matching, sequences, analogies, and metaphors. Ifs just like the familiar distinction we make between the principles of evolution and the products of evolution. The products, in this case, are some of the most interesting ways that humans differ from our ape cousins: going beyond mere category formation to shape up further levels of complexity such as metaphor, narrative, and even agendas. I think that planning ahead, language, and musical abilities also fall out of this same set of neocortical mechanisms, as I've discussed (along with their "free lunch" aspects, thanks to common neural mechanisms) in my earlier books.

SOME READERS MAY HAVE NOTICED BY NOW that this book is not like

my previous ones. They were primarily for general readers and only secondarily for fellow scientists, but that order is reversed here. To help compensate, I've provided a glossary starting at page 203 (even the neuroscientists will need it for the brief tutorials in chaos theory and evolutionary biology). Consult it early and often.

And I had the general reader firmly in mind as I did the book design (ifs all my fault, even the page layout). The illustrations range from the serious to the sketchy. In Three Places in New England, the composer Charles Ives had a characteristic way of playing a popular tune such as "Yankee Doodle" and then dissolving it into his own melody; even a quote of only four notes can be sufficient to release a flood of associations in the listener (something that I tackle mechanistically in Act II, when warming up for metaphor mechanisms). As a matter of writer's technique,

I have tried to use captionless thumbnail illustrations as the briefest of scene-setting digressions, to mimic Ives. I have again enlisted the underground architect, Malcolm Wells, to help me out

— you won't have any trouble telling which illustrations are Mac's! Furthermore, a painting by the neurobiologist Mark Meyer adorns the cover. For some of my own illustrations, alas, I have had to cope with conveying spatiotemporal patterning in a spatial-only medium (further constrained by being grayscale-only and tree-based!). Although I've relied heavily on musical analogies, the material fairly begs for animations.

I have resisted the temptation to utilize computer simulations, mostly for reasons of clarity (in my own head — and perhaps also the reader's). Simulations, if they are to be more than mere animations of an idea, have hard-to-appreciate critical assumpt-ions. At this stage, simulations are simply not needed — one can comprehend the more obvious consequences of a neocortical Darwin Machine without them, both the modular circuits and the territorial competitions. Plane geometry fortunately suffices, essentially that discovered by the ancient Greeks as they contem-plated the hexagonal tile mosaics on the bathhouse floor.

Act I

Everyone knows that in 1859 Darwin demonstrated the occurrence of evolution with such overwhelming documentation tnat it was soon almost universally accepted. What not everyone knows, however, is tnat on tnat occasion Darwin introduced a number of otner scientific and philosophical concepts tkat nave been of far-reaching importance ever since. These concepts, population thinking and selection, owing to their total originality, had to overcome enormous resistance. One might think that among the many hundreds of philosophers who had developed ideas about change, beginning with the Ionians, Plato and Aristotle, the scholastics, the philosophers of the Enlightenment, Descartes, Locke, Hume, Leibniz, Kant, and the numerous philosophers of the first half of the nineteenth century, that there would have been at least one or two to have seen the enormous heuristic power of that combination of variation and selection. But the answer is no. To a modern, who sees the manifestations of variation and selection wherever he looks, this seems quite unbelievable, but it is a historical fact.

E R N S T MAYR, 1994

Looking back into the history of biology, it appears that wherever a phenomenon resembles learning, an instructive theory was first proposed to account for the underlying mechanisms. In every case, this was later replaced by a selective theory. Thus the species were thought to have developed by learning or by adaptation of individuals to the environment, until Darwin showed this to have been a selective process. Resistance of bacteria to antibacterial agents was thought to be acquired by adaptation, until Luria and Delbriick showed the mechanism to be a selective one. Adaptive enzymes were shown by Monod and his school to be inducible enzymes arising through the selection of preexisting genes. Finally, antibody formation that was thought to be based on instruction by the antigen is now found to result from the selection of already existing patterns. It thus remains to be asked if learning by the central nervous system might not also be a selective process, i.e., perhaps learning is not learning either.

N I E L S K J E R N E , 1967

T k e Representation Rroblem

and tke Copying Solution

Even in the small world of Drain science [in the 1860s], two camps were beginning to form. One held that psychological functions such as language or memory could never he traced to a particular region of the hrain. if one had to accept, reluctantly, that the hrain did produce the mind, it did so as a whole and not as a collection of parts with special functions. The

other camp held that, on the contrary, the hrain did

have specialized parts and those parts generated separate mind functions. The rift hetween the two camps was not merely indicative of the infancy of hrain research; the argument endured for another century and, to a certain extent, is still with us today.

A N T O N I O R . D A M A S I O , 1995

ONE CELL, ONE MEMORY may not be the way things work, but it seems to be the first way that people think about the problem of locating memories in cells. Even if you

aren't familiar with how computers store data, the take-home message of most introductions to the brain is that there are pigeonhole memories — highly specialized interneurons, the firing of which might constitute an item's memory evocation. On the perceptual side of neurophysiology, we call it the grandmother's face cell (a neuron that may fire only once a year, at Christmas dinner). On the movement side, if a single interneuron (thaf s an "insider neuron," neither sensory neuron nor motor

neuron) effectively triggers a particular response, it gets called a command neuron. In the simplest of arrangements, both would be the same neuron.

Indeed, the Mauthner cells that trigger the escape reflex of the fish are exactly such neurons. If the fish is attacked from one side, the appropriate Mauthner cell fires and a massive tail flip results, carrying the fish away from the nibbles of its predator. Fortunately these cells already had a proper name, so we were spared the nibble-detector tail-flip cell

But we know better than to generalize these special cases to the whole brain — it can't be one cell, one concept. Yet the reasoning that follows isn't as easily recalled as those pigeonhole memory examples that inadvertently become the take-home message from most introductions to the subject. A singular neuron for each concept is rendered implausible in most vertebrates by the neurophysiological evidence that has accum-ulated since 1928, when the first recordings from sensory nerves revealed a broad range of sensitivity. There were multiple types, with the sensitivity range of one type overlapping that of other types. This overlap, without pure specialties, had been suspected for a long time, at least by the physiologically inclined. Thomas

The three cone types

\ / X \ LI

h. Lh

A™

AOOnm

light wavelength

This combination is:

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"YeHov/1

Ted"

Young formulated his trichromatic theory of colors in 1801; after Hermann von Helmholtz extended the theory in 1865, it was pretty obvious that each special color must be a particular pattern of response that was achievable in various ways, not a singular entity. More recently, taste has turned out the same way: bitter

is just a pattern of strong and weak responses in four types of taste buds, not the action of a particular type.

This isn't to say that a particular interneuron might not come to specialize in some unique combination — but it's so hard to find narrow specialists, insensitive to all else, that we talk of the expectation of finding one as the "Grandmother's face cell fallacy" The "command neuron" usually comes with scare quotes, too, as the Mauthner cell arrangement isn't a common one. While we seek out the specialized neurons in the hopes of finding a tractable experimental model, we usually recognize that committees are likely the irreducible basis of representations — certainly the more abstract ones we call schemas.

Because the unit of memory is likely to be closely related to sensory and motor schemas, pigeonhole schemes such as one-cell-one-memory had to be questioned. After Karl Lashley got through with his rat cortical lesions and found no crucial neocortical sites for maze memory traces, we had to suspect that a particular "memory trace" was a widespread patterning of some sort, one with considerable redundancy. You're left trying to imagine how a unit of memory could be spatially distributed in a redundant manner, overlapping with other memories.

One technological analogy is the hologram, but the brain seems unlikely to utilize phase information in the same way. A simpler and more familiar example of an ensemble representation is the pattern of lights on a message board. Individually, each light signifies nothing. Only in combination with other lights is there a meaning. More modern examples are the pixels of a computer screen or dot-matrix printer. Back in the 1940s, the physiological psychologist Donald Hebb postulated such an ensemble (which he called a cell-assembly) as the unit of perception — and therefore memory. I'll discuss the interesting history of the cell-assembly in the Intermission Notes but for now, just think of one committee-one concept, and that any one cell can serve on multiple committees.

Note that it is not merely the lights which are lit that contain the concept's characteristic pattern: it is just as important that other lights are off, those that might "fog" the desired pattern were they turned on. Fortunately, most neurons of association cortex fire so infrequently that we often take the shortcut of

talking only about "activating cells"; in other parts of the nervous system (especially the retina), there are background levels of activity that can be decreased as well as increased (just as gray backgrounds allow the textbook illustrator to use both black and white type) in an analog manner. But, as we shall see, neocortex also has some "digital" aspects.

A minor generalization to Hebb's cell-assembly would be moveable patterns, as when a message board scrolls: the pattern's the thing, irrespective of which cells are used to implement it. I cannot think of any cerebral examples equivalent to the moving patterns of Conway's Game of Life, such as flashers and gliders, but it is well to keep the free-floating patterns of automata in mind.

The important augmentation of the message board analogy is a pattern of twinkling lights: the possibility that the relevant memory pattern is a spatiotemporal one, not merely a spatial one. In looking for spatiotemporal patterns and trying to discern components, we are going to have the same problems as the child looking at a large Christmas tree, trying to see the independently flashing strings of lights that have been interwoven.

IN THE LONG RUN, however, a memory pattern cannot be a spatiotemporal one: long-term memories survive all sorts of temporary shutdowns in the brain's electricity, such as coma; they persist despite all sorts of fogging, such as those occurring with concussions and seizures. Hebb's dual trace memory said that there had to be major representational differences between long-term memory and the more current "working memories," which can be a distinctive pattern of neuron firings. As Hebb put it:

If some way can be found of supposing that a reverberatory [memory] trace might cooperate with the structural change, and

carry the memory until the growth change is made, we should be able

to recognize the theoretical value of the trace which is an activity only, without having to ascribe all memory to it.

We are familiar with this archival-versus-current, passive-versus-active distinction from phonograph records, where a spatial-only pattern holds the information in cold storage and a spatiotemporal pattern is recreated on occasion, a pattern almost identical to that which originally produced the spatial pattern. A sheet of music or a roll for a player piano also allows a spatial-only pattern to be converted to a spatiotemporal one. I will typically use musical performance as my spatiotemporal pattern analogy and sheet music as my analogy to a spatial-only underpinning.

At first glimpse, there appear to be some spatial-only sensat-ions, say, those produced by my wristwatch on my skin (if s not really static because I have the usual physiological tremor, and a radial pulse, to interact with its weight). But most of our sensat-ions are more obviously spatiotemporal, as when we finger the corner of the page in preparation for turning it. Even if the input appears static, as when we stare at the center of a checkerboard, some jitter is often introduced, as by the small micronystagmus of the eyeball (as I discuss further in the middle of my Intermission Notes, the nervous system gets a spatiotemporal pattern from the photoreceptors sweeping back and forth under the image). Whether timeless like a drawing of a comb or changing with time as when feeling a comb running through your hair, the active "working" representation is likely to be spatiotemporal, some-thing like the light sequence in a pinball machine or those winking light strings on the Christmas tree.

Certainly, all of our movements involve spatiotemporal patterns of muscle activation. Even in a static-seeming posture, physiological tremor moves things. In general, the implement-ation is a spatiotemporal pattern involving many motor neuron pools. Sometimes, as in the case of the fish's tail flip, the command for this starts at one point in time and space, but usually even the initiation of the movement schema is spatiotemporal, smeared out in both time and space.

The sensation need not funnel down to a point and then expand outwards to recruit the appropriate response sequence; rather, the spatiotemporal pattern of the sensation could create the appropriate spatiotemporal pattern for the response without ever having a locus. Spread out in both time and space, such ephemeral (and perhaps relocatable) ensembles are difficult to summarize in flow charts or metaphors. Think, perhaps, of two voices, one of which (the sensory code) starts the song, is answered by the other voice (movement code); the voices are then intertwined for awhile (and the movement eventually gets underway), and then the second voice finishes the song.

To my mind, the representation problem is which spatio-temporal pattern represents a mental object: surely recalling a memory is not a matter of recreating the firing patterns of every cell in the brain, so that they all mimic the activity at the time of input. Some subset must suffice. How big is it? Is it a synchron-ized ensemble like a chord, as some cortical theories would have it? Or is it more like a single note melody? Or with some chords mixed in? Does it repeat over and over, or does one repetition suffice for a while?

THOSE QUESTIONS WERE IN THE AIR, for the most part, even back in

my undergraduate days of the late 1950s, when I first met Hebb after reading his then-decade-old book, It is inaccurate — worse, it The Organization of Behavior. Hebb,

is mislea Jing — to call amazingly, guessed a solution in 1945, psyckology tke study of even before the first single neuron

Lekavior: It is tke study of

recordings from mammalian

cerebral

tke underlying processes,

cortex (glass microelectrodes

weren't

just as ckemistry is tke

invented until 1950). Although our data

study of tke atom ratker

have grown magnificently in recent

tkan pH values, spectro-

decades, we haven't improved much on

scopes, and test tukes.

Hebb's statement of the problem, or on

D. O. HEBB, 1980

his educated guess about where the

solution is likely to be found.

Multiple microelectrode techniques now allow the sampling of several dozen neurons in a neighborhood spanning a few square millimeters. In motor cortex, even a randomly sampled

ensemble can predict which movement, from a standard repertoire, that a trained monkey is about to make. For monkeys forced to wait before acting on a behavioral choice, sustained cell firing during the long hold is mostly up in premotor and prefrontal areas. In premotor and prefrontal cortex, some of the spatiotemporal patterns sampled by multiple microelectrodes are surprisingly precise and task-specific. With the fuzzier imaging techniques, we have recently seen some examples of where work-ing memory patterns might be located: for humans trying to remember telephone numbers long enough to dial them, it's the classical Broca and Wernicke language areas that light up in imaging techniques.

Because recall is so much more difficult than mere recognition (you can recognize an old phone number, even when you can't voluntarily recall it), we may need to distinguish between different representations for the same thing. The cryptographers make a similar distinction between a document and a hashed summary of that document (something like a checksum but capable of detecting even transposed letters). Such a 100-byte "message digest" is capable of recognizing a unique, multipage document ("I've seen that one before") but doesn't contain enough information to actually reconstruct it. So, too, we may have to distinguish between simple Hebbian cell-assemblies — ones that suffice for recognition — and the more detailed ones needed for abstracts and for complete recall.

Hebb's formulation imposes an important constraint on any possible explanation for the cerebral representation: if s got to explain both spatial-only and spatiotemporal patterns, their inter-conversions, their redundancy and spatial extent, their imperfect nature (and characteristic errors therefrom), and the links of assoc-iative memory (including how distortions of old memories are caused by new links). No present technology provides an analogy to help us think about the problem.

THE ROLE OF SIMILAR CONSTRAINTS on theorizing can be seen in how Kepler's three "laws" about planetary orbits posed the gravity problem that Newton went on to solve. Only a half century ago, molecular genetics had a similar all-important

constraint that set the stage for a solution. Biologists knew that, whatever the genetic material was, it had to fit inside the cell, be chemically stable — and, most significantly, it had to be capable of making very good copies of itself during cell "division." That posed the problem in a solvable way, as it turned out.

Most people thought that the gene would turn out to be a protein, its three-dimensional nooks and crannies serving as a template for another such giant molecule. The reason Crick and Watson's DNA helical-zipper model caused such excitement in 1953 was because it fit with the copying constraint. It wasn't until a few years later that it became obvious how a triplet of a 4-letter DNA code was translated into strings from the 20-letter amino acid alphabet, and so created enzymes and other proteins.

Looking for molecular copying ability led to the solution of the puzzle of how genes were decoded. Might looking for a neural copying mechanism provide an analogous way of approaching the cerebral code puzzle?

MEMES ARE THOSE THINGS that are copied from mind to mind. Richard Dawkins formulated this concept in 1976 in his book, The Selfish Gene. Cell division may copy genes, but minds mimic everything from words to dances. The cultural analog to the gene is the meme (as in mime or mimic); if s the unit of copying. An advertising jingle is a meme. The spread of a rumor is cloning a pattern from one mind to another, the metastasis of a representation.

Might, however, such cloning be seen inside one brain and not just between brains? Might seeing what was cloned lead us to the representation, the cerebral code? Copying of an ensemble pattern hasn't been observed yet, but there are reasons to expect it in any brain — at least, in any brain large enough to have a long-distance communications problem.

If the pattern's the thing, how is it transmitted from the left side of the brain to the right side? Or from front to back? We can't send it like a mail parcel, so consider the problems of telecopying, of making a distant copy of a local pattern. Is there a NeuroFax Principle at work?

When tracing techniques were crude, at a millimeter level of resolution, it seemed as if there were point-to-point mappings, an orderly topography for the major sensory pathways such that neighbors remained next to one another. One could imagine that

those

long corticocortical axon

bundles were like fiber optic bun-

Memes are not strung out along

dles

that

convey an

image by

linear chromosomes, ana it is

thousands

little light pipes.

not clear that they occupy and

But with finer resolution, topo-

compete for discrete "loci", or

graphic mappings turn out to be

tnat tney nave identifiable

only

approximately

point-to-

"alleles" . . . . The copying

point; instead, an axon breaks up

process is probably much less

into clumps of endings. For the

precise than in the case of

corticocortical axon terminations

genes. . . . Memes may partially

of the "interoffice mail," this fan-

blend with each other in a way

out

spans

macrocolumnar

that genes do not.

dimensions and sometimes many

RICHARD DAWKINS, 1982

millimeters. Exact point-to-point mapping doesn't occur.

So, at first glimpse, it appears that corticocortical bundles are considerably worse than those incoherent fiber optic bundles that are factory rejects — unless, of course, something else is going on. Perhaps it doesn't matter that the local spatiotemporal pattern is severely distorted at the far end; if codes are arbitrary, why should it matter that there are different codes for Apple in different parts of the brain? Just as there are two equally valid roots to a quad-ratic equation, just as isotopes have identical chemical properties despite different weights, so degenerate codes are quite common. For example, there are six different DNA triplets that all result in leucine being tacked on to a growing peptide.

The main drawback to a degenerate cortical code is that most corticocortical projections are reciprocal: six out of seven interareal pathways have a matching back projection. It might undo the distortion of the forward projection, in the manner of inverse transforms, but thaf s demanding a lot of careful tuning and regular recalibration. And it isn't simply a matter of each local region having two local codes for Apple, one for sending, the other

for receiving. Each region has multiple projection targets and thus many possible feedback codes that mean Apple.

There might, of course, be some sort of error-correction code that allows a single characteristic spatiotemporal pattern for Apple. It would have to remove any distortions caused by the spatial wanderings, plus those associated with temporal dispersions of corticocortical transmission. It would need, furthermore, to operate in both the forward and return paths. I originally dismissed this possibility, assuming that an error-correcting mechanism was too fancy for cerebral circuitry. But, as will become apparent by the end of the following chapter, such error correction is easier than it sounds, thanks to that fanout of the corticocortical axon's terminals contributing to standardization of a spatiotemporal pattern.

COPYING FOR A FAUX FAX is going to be needed for cerebral cortex, even if simpler nervous systems, without a long-distance problem, can operate without copying. Copying might also be handy for promoting redundancy. But there is a third reason why copying might have proved useful in a fancy brain: darwinism.

Perhaps it is only a matter of our impoverished knowledge of complex systems, but creativity seems to be a shaping-up process. During the evolution of new species and during the immune response's production of better and better antibodies, successive generations are shaped up, not especially the individual. Yes, the individual is plastic and it learns, but this modification during life is not typically incorporated into the genes that are passed on (learning and experience only change the chances of passing on the genes with which one was born — the propensity for learning such things, rather than the things themselves). Yes, culture itself passes along imitations, but memes are easily distorted and easily lost, compared to genuine genes.

Reproduction involves the copying of patterns, sometimes with small chance variations. Creativity may not always be a matter of copying errors and recombination, but it is reasonable to expect that the brain is going to make some use of this elementary darwinian mechanism for editing out the nonsense

and emphasizing variations on the better-fitting ones in a next generation.

NATURAL SELECTION ALONE isn't sufficient for evolution, and neither is copying alone — not even copying with selection will suffice. I can identify six essential aspects of the creative darwin-ian process that bootstraps quality.

There must be a reasonably complex pattern involved.

The pattern must be copied somehow (indeed, that which is copied may serve to define the pattern).

Variant patterns must sometimes be produced by chance.

The pattern and its variant must compete with one another for occupation of a limited work space. For example, bluegrass and crab grass compete for back yards.

The competition is biased by a multifaceted environment, for example, how often the grass is watered, cut, fertilized, and frozen, giving one pattern more of the lawn than another. That's natural selection.

There is a skewed survival to reproductive maturity (environmental selection is mostly juvenile mortality) or a skewed distribution of those adults who successfully mate (sexual selection), so new variants always preferentially occur around the more successful of the current patterns.

With only a few of the six essentials, one gets the more wide-spread "selective survival" process (which popular usage tends to call darwinian). You may get some changes (evolution, but only in the weakest sense of the word) but things soon settle, running out of steam without the full process to turn the darwinian ratchet.

Indeed, many things called darwinian turn out to have no copying process at all, such as the selective survival of some synaptic connections in the brain during pre- and postnatal development of a single individual. Selective survival, moreover, doesn't even require biology. For example, a shingle beach is one where the waves have carried away the smaller rocks and sand, much as a carving reflects the selective removal of some material to create a pattern. The copying-mutation-selection loop utilized

by the small-molecule chemists as they try to demonstrate the power of RNA-based evolution captures most of darwinism, as do "genetic" algorithms of computer science.

Not all of the essentials have to be at the same level of organiz-ation. Pattern, copying, and variation involve the genes, but selection is based on the bodies (the phenotypes that carry the genes) and their environment; inheritance, however, is back at the genotype level. In RNA-based evolution, the two levels are combined into one (the RNA serves as a catalyst in a way that affects its survival — but it is also what is copied).

BECAUSE NEURAL VERSIONS OF THE SIX ESSENTIALS are going to play

such a large role in the rest of this book, let me comment on the better-known versions for a moment.

The gene is a string of DNA base-pairs that, in turn, instructs the rest of the cell about how to make a protein, perhaps an enzyme that regulates the rate of tissue growth. We'll be looking back from neural implementations, such as movement comm-ands, and trying to see what patterns could have served as the cerebral code to get them going. Larger genetic patterns, such as whole chromosomes, are seldom copied exactly. So, too, we will have to delve below the larger movements to see what the smaller units might be.

While the biological variations seem random, unguided variation isn't really required for a darwinian process to operate. We tend to emphasize randomness for several reasons. First, randomness is the default assumption against which we test claims of guidance. And second, the process will work fine without guidance, without any foreknowledge of a desired result. That said, it might work faster, and in some restricted sense better, with some hints that bias the general direction of the variants; this need not involve anything as fancy as artificial selection. We will see neural versions of random copying errors and recombination, including (in the last chapter) some discussion about how a slow darwinian process might guide a faster one by biasing the general direction in which its variations are done.

Competition between variants depends on some limitation in resources (space in association cortex, in my upcoming examples)

or carrying capacity. During a wide-open population explosion, competition is minor because the space hasn't filled up yet.

For competition to be interesting, it must be based on a complex, multifaceted environment. Rather than the environment of grass, we'll be dealing with biases from sensation, feedback from our own movements, and even our moods. Most interestingly, there are both current versions of these environmental factors and memories of past ones.

Many of the offspring have variations that are "worse" than the successful parent pattern but a minority may possess a variant that is an even better fit to the particular multifaceted environment. This tendency to base most new variations on the more successful of the old ones is what Darwin called the principle of inheritance, his great insight and the founding principle of what became population biology.

It means that the darwinian process, as a whole loop, isn't truly random. Rather, it involves repeated exploratory steps where small chance variations are done on well-tested-by-the-environment versions. If s an enormously conservative process, because variations propagate from the base of the most successful adults — not the base of the population as born. Without this proviso, the process doesn't accumulate wisdom about what worked in the past. The neural version also needs exactly the same characteristic, where slight variations are done from an advanced position, not from the original center of the population.

AT LEAST FIVE OTHER FACTORS are known to be important to the evolution of species. The creative darwinian process will run without them, but they affect the stability of its outcome, or the rate of evolution, and will be important for my model of cognitive functions. Just like the catalysts and enzymes that speed chemical reactions without being consumed, they may make improbable outcomes into commonplace ones.

Stability may occur, as in getting stuck in a rut (a local peak or basin in the adaptational landscape). Variants occur but they backslide easily. Only particularly large variations can ever escape from a rut, but they are few, and

even more likely to produce nonsense (phenotypes that fail to develop properly, and so die young).

Systematic recombination generates many more variants than do copying errors and the far-rarer cosmic-ray mutations. Recombination usually occurs once during meiosis (the grandparent chromosomes are shuffled as haploid sperm and ova are made) and again at fertilization (as the haploid parent genomes are combined into diploid once again, at fertilization). Sex, in the sense of gamete dimorphism (going to the extremes of expensive ova and cheap sperm), was invented several billion years ago and greatly accelerated species evolution over the rate promot-ed by errors, bacterial conjugation, and retroviruses.

Fluctuating environments (seasons, climate changes, diseases) change the name of the game, shaping up more complex patterns capable of doing well in several environ-ments. For such jack-of-all-trades selection to occur, the environment must change much faster than efficiency adaptations can track it, or 'lean mean machine" special-ists will dominate the expensive generalists.

Parcellation, as when rising sea level converts the hilltops of one large island into an archipelago of small islands, typically speeds evolution. This is, in part, because more individuals then live on the margins of the habitat where selection pressure is greater. Also, there is no large central population to buffer change. When rising sea level con-verted part of the coastline of France into the island of Jersey, the red deer trapped there in the last interglaciation underwent a considerable dwarfing within only a few thousand years.

Local extinctions, as when an island population becomes too small to sustain itself, speed evolution because they create empty niches. When subsequent pioneers rediscov-er the unused resources, their descendants go through a series of generations where there is enough food — even for the more extreme variations that arise, the ones that would ordinarily lose out in the competition with the more optimally endowed, such as the survivors of a resident

population. When the environment again changes, some of those more extreme variants may be able to cope better with the third environment than the narrower range of variants that would reach reproductive age under the regime of a long-occupied niche.

Sexual selection also has the reputation of speeding evolution, and there are "catalysts" acting at several removes, as in Darwin's example of what introducing cats to an English village would do to enhance the bee-dependent flowers, via reducing the rodent populations that disrupt bee hives.

An example of how these catalysts work together is island biogeography, as in the differentiation of Darwin's finches unbuff-ered by large continental gene pools. Archipelagos allow for many parallel evolutionary experiments. Episodes that recombine the islands (as when sea level falls during an ice age) create winner-take-most tournaments. Most evolutionary change may occur in such isolation, in remote valleys or offshore islands, with major continental populations serving as slowly changing reservoirs that provide pioneers to the chancy periphery.

ALTHOUGH THE CREATIVE DARWINIAN PROCESS will run without

these catalysts, using darwinian creativity in a behavioral setting requires some optimization for speed, so that quality is achieved within the time span of thought and action. Accelerating factors are the problem in what the French call avoir Vesprit de I'escalier — finally thinking of a witty reply, but only after leaving the party.

I will not be surprised if some accelerating factors are almost essential in mental danvinism, simply because of the time windows created by fleeting opportunities.

The wheels of a machine

to play rapidly

must not fit with the utmost exactness

else the attrition diminishes the Impetus.

SIR WALTER SCOTT, discussing Lord Byron's mind

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Cloning in Ceretral Cortex

All scribes, however careful, are bound to make a few errors, and some are not above a little willful "improvement." If they all copied from a single master original, meaning would not be greatly perverted. But let copies be made from other copies, wbicb in their turn were made from other copies, and errors will start to become cumulative and serious. We tend to regard

erratic copying as a bad thing, and in tbe case of human

documents it is bard to think of examples wbere errors can be described as improvements. I suppose tbe scbolars of tbe Septuagint could at least be said to bave started something big wben tbey mistranslated tbe Hebrew word for "young woman" into tbe Greek word for "virgin," coming up witb tbe prophecy: "Behold a virgin sball conceive and bear a son. . . . " Anyway, as we sball see, erratic copying in biological replicators can in a real sense give rise to improvement, and it was essential for tbe progressive evolution of life that some errors were made.

RICHARD DAWKINS, The Selfish Gene, 1976

THIS is THE HARDEST CHAPTER in the whole book. In it, I have to delve into the neuroanatomy and neurophysiology of cortical neurons, importing lessons from such seemingly unrelated subjects as synchronously flashing fireflies. By the end of this chapter, I will have shown how copying could arise in neocortex. By the end of the sixth chapter, the cortical equivalents of all the darwinian essentials and all the accelerating factors will

have been examined. But it gets easier, not harder, starting with the fourth chapter.

Neurophysiologists distinguish between cell properties and circuit properties, much as biologists distinguish between genotype and phenotype. Some phenomena are clearly due to the circuit rather than the cells involved, to the wiring rather than the components — a new property "emerges" from the particular combination. You won't find it in any one neuron. The classical example of an emergent property involves lateral inhibition and it is the reason that Keffer Hartline got a Nobel Prize in 1967.

Thanks to local activity contributing to a ring of depression in surrounding neurons, lateral inhibition sharpens up fuzzy bound-aries. Compound eyes, the many narrow-angle photoreceptors of which provide an extreme case of fuzzy optics, have a series of inhibitory interconnections that are capable of recreating a light-dark boundary in the environment, restoring much of what was lost.

But lateral inhibition also has a tendency to produce features where none exist, illusions such as the Mach bands that you see if looking through a narrow slit between your fingers. Georg von Bekesy, whose studies of such sideways interactions in the cochlea were the subject of his 1961 Nobel Prize, also produced illusions from skin surfaces, to illustrate the generality of the lateral inhibition principles. Antagonistic surrounds ("Mexican hats")

are common in all the first half-dozen stages of the analysis of a visual image, though they become somewhat elongated and asymmetric ("Australian bush hats") in primary visual cortex. Because of the many axon collaterals that branch laterally in neocortex,

lateral inhibition extends several millimeters.

Both the sharpening of fuzzy boundaries and the illusions are emergent properties of a laterally inhibiting neural network. What might be the emergent consequences of lateral excitation?

THERE IS GOOD REASON to worry about recurrent excitation. It is potentially regenerative, in the same sense as a string of fire

3.0 mm

PyrwmUM neuron mama eond many

fPOGWVWtt GOmtCTtV VTWGnOB WtOIWty to neighboring areas of cortex, the hash for both lateral Inhibition and

crackers. It is also the most prominent wiring principle of mam-malian neocortex.

A few words about cerebral cortex, the icing on the brain's cake:. in this cake, it's the frosting that has the appearance of layering! Six layers are usually identified on the basis of cell size

or axon packing density, though we sometimes subdivide it further (in primary visual cortex, one talks about layers 4a, 4b, 4ca, and 4cP). At other times, we lump layers together: when I mention the "superficial layers/' I'm combining layers 1,2, and 3.

Part of the monkey superficial pyramidal

neuron reconstructed by McGuIre et al

(J. Comp. Neurol. 1991), showing axon

terminals to Immediate neighbors (thin

fcJ>

axons amidst dendritic tree) as well as

branches to cells a microcolumn away.

Furthermore, there are three functional groupings that have become apparent: on the analogy to the mail boxes stacked on many a desk, layer 4 could be said to be the IN box of neocortex, because most of the inputs from the thalamus terminate there. The deep layers could be called the OUT box, as pyramidal neurons of layers 5 and 6 send axons outside the cortex, back to thalamus or down to the spinal cord, and so forth. The neurons of the superficial layers seem to constitute the INTERNAL mailbox of the neocortex, specializing in the interoffice memos. Interactions among the superficial pyramidal neurons are what this book is mostly about, as these neurons seem capable of implementing a darwinian copying competition, one that can shape up quality from humble beginnings.

The axons of the superficial pyramidal cells are prominent in the corpus callosum and other long corticocortical paths, but also in the intrinsic horizontal connections: those axon branches that never leave the superficial layers because they run sideways. They preferentially terminate on other superficial pyramidal neu-rons — and in a patterned manner, too. Some axon branches go to near neighbors, but the ones that go further ignore a whole series of intermediate neurons before communicating with those about 0.5 mm distant.

Those sparsely populated gaps are something like the Sherlock Holmes story about the dog that didn't bark in the night. It took a long time before anyone noticed this fact. In 1975 came the first hint of these gap patterns. In 1982, when Jennifer Lund and Kathleen Rockland first studied the gaps in the superficial layers' intrinsic horizontal connections, it was in the visual cortex of the tree shrew. Though the gap distance varies, we now know that it is a common arrangement for many areas of neocortex, and for many animal species. Thanks to the detailed reconstructions of several HRP-injected superficial pyramidal neurons by Barbara McGuire and her colleagues, we also know that these synaptic connections are likely to be excitatory, probably using glutamate as their neurotransmitter, and that their predominant targets are other superficial pyramidal neurons.

Their axons have dozens of branches, going sideways in many radial directions, fanning out eventually into thousands of axon terminals. Although no single superficial pyramidal neuron has enough terminals to fill in a doughnut, we might expect a small minicolumn group of such neurons to produce a ring of excitation, as well as the central spot of excitation from the branches to immediate neighbors. Point-to-area is the more common arrangement for axon projections, such as those of the pyramidal neurons of the deep layers. Recurrent inhibition is also seen, but only the recurrent excitation of the superficial layers of neocortex has this Sherlock-Holmes feature of prominent silent gaps.

Optical imaging techniques that look down on the brain's surface are now capable of resolving a spread of activity in cortex. Stimulation of a restricted area of retina, of a type that classically would be expected to concentrate cortical activity in only one area

«ftM-Lonnt»<l«No

branching axon activating cortical loops

of the exposed cortical surface, is now seen to

contribute to multiple hot spots of activity at

4 #

macrocolumnar separations, much as predicted.

The neocortical versions of long-term potentiat-

ion (LTP) are also concentrated in the superficial

layers. We know that N-methyl-D-aspartate

(NMDA) types of postsynaptic receptors, which

I have the unusual characteristic of augmenting their strength when inputs arrive in clusters (such as quasi-synchronously from different sources), are especially

common in the superficial layers.

All of this raises the possibility of self-reexciting loops, not un-like the reverberating circuits postulated for the spinal cord by Rafael Lorente de N6 in 1938, in the very first volume of the Journal of Neurophysiology. If the synaptic strengths are high enough, and the paths long enough to escape the refractory periods that would otherwise limit re-excitation, closed loops of activity ought to be possible, impulses chasing their tails. Moshe Abeles, whose Jerusalem lab often observes more than a dozen cortical neurons at a time, has seen some precise impulse timing

of one neuron, relative to another, in premotor and prefrontal cortex neuron ensembles. It is unknown whether or not these firing patterns represent reverberation, in Lorente's original sense of recirculating loops. These long, precisely-timed firing patterns are important for the notion of spatiotemporal patterns that I will later develop.

EMERGENT SYNCHRONY is well known as a commonplace conseq-uence of recurrent excitation, one that ought to be seen with even weak connection strengths and short paths. In 1665, the Dutch physicist Christiaan Huygens noticed that two pendulum clocks hanging from a common support were synchronized. When he

disturbed the synchrony, it returned within a half hour. Harmonic oscillators are slower to entrain than nonlinear relaxation oscillators, which can take just a few cycles.

The most famous example of entrairunent is probably menstr-ual cycles in women's dormitories. More dramatic in appearance is a whole tree filled with little lights, flashing in unison. No, I don't mean a Christmas tree wired up, under the control of a single flasher — there's a natural, wireless example based on hundreds of independent oscillators. The little lights are hundreds of fireflies, and they have no leader to set the pace.

Imagine a tree thirty-five to forty feet high, apparently with a firefly on every leaf, and all the fireflies flashing in perfect unison at a rate of about three times in two seconds, the tree being in complete darkness between flashes. Imagine a tenth of a mile of river front with an unbroken line of mangrove trees with fireflies on every leaf flashing in synchronization, the insects on the trees at the ends of the line acting in perfect unison with those between. Then, if one's imagination is sufficiently vivid, he may form some conception of this amazing spectacle.

It doesn't require any elaborate notions of mimicry to account for the firefly entrainment; even small tendencies to advance to the next flash when stimulated with light will suffice to create a "rush hour." Furthermore, you usually do not see waves propagating through such a population, except perhaps when the flashing is just beginning or ending. Even in cortical simulations with prop-agation delays, near-synchrony is seen, in much the way (anomal-ous dispersion) that some velocities can exceed the speed of light.

Weak mutual re-excitation (a few percent of threshold) is quite sufficient to entrain; one need not postulate strong connection strengths in the manner needed for Lorente's recirculating chains. So long as the neurons (or fireflies) already have enough input to fire repeatedly, there will be an entrainment tendency if they mutually re-excite one another. And that is exactly what super-ficial pyramidal neurons, 0.5 mm apart, seem so likely to do. The triple combination — mutual re-excitation, silent gaps that focus it, and the resulting entrainment tendencies — is what gives the

superficial layers of neocortex the potential of being a Darwin Machine.

LOOKING DOWN FROM ON HIGH at the superficial layers of neocortex, in what the neuroanatomists call "tangential slices/' is like looking down on a forest from a balloon. Any one neuron is seen in a top-down perspective, orthogonal to that seen from the side in the usual surface-to-depth slice. Like the branches of any one tree, any one neuron has a dendritic tree, but also an axon tree, much as the foresf s tree has branching roots below ground.

The axon of a single superficial pyramidal neuron will be seen to spread in many directions. Though sensory neurons and motor neurons may vary, the average interneuron sends out as many synapses as it receives, usually between 2,000 and 10,000. Not enough radial plots have yet been done to know how symmetric the horizontal spread is, but it seems clear that the axon branches travel in many directions from the cell.

GIVEN standard length excitatory axons,

entrained

...recurrent excitation between some cell pairs produces entrained firing patterns.

An entrained pair tends to recruit additional cells

that are equidistant.

spot"

...and so create a

TRIANGULAR ARRAY.

The distance from the cell body to the center of the axon term-inal cluster, studied mostly in the side views, is not the same in all cortical areas. That "0.5 mm" mentioned earlier is really as small as 0.4 mm (in primary visual cortex of monkeys) or as large as 0.85 mm (in sensorimotor cortex). It scales with the width of the basal dendritic tree. I'll use 0.5 mm as my standard example of this local metric; it corresponds to a basal dendritic tree of about 0.25

mm spread, which is also about the spread of one cluster of axon terminals and the extent of one silent gap.

If two superficial pyramidal neurons, about 0.5 mm apart, are interested in the same features because of similar inputs and thresholds, their spike trains ought to start exhibiting occasional spike synchrony. It need not be all the spikes from each neuron for the following analysis to be relevant; only some of their spikes need synchronize via the recurrent excitation.

There should also be some minor tendency for two such cells, already firing repeatedly, to recruit another cell 0.5 mm away that is almost active. If that third superficial pyramidal neuron becomes active, we should see three often-synchronized neurons forming an equilateral triangle. But that is not the end of it: there is a second site receiving synchronous input from the

parent pair (this is exactly like that elementary exercise in plane geometry where a compass is used to bisect a line or drop a perpendicular). So a fourth neuron might join the chorus.

And because the third and fourth cells provide new annuli of excitation, either can combine with one of the first pair to bring a fifth point into synchrony. What we have, it is apparent, is a mechanism for forming up a triangular array of some size, nodes of synchronized activity 0.5 mm

from corresponding cells of this chorus. It could work either by synchronizing preexisting activity or by recruiting otherwise sub-threshold neurons at the nodes. Once a potential node is surround-ed by a few synchronous nodes exciting it, there ought to be a hot spot, an unusually effective con-vergence of simultaneous inputs.

This triangular array annexat-

ion tendency is not unlimited. (Regions with insufficiently excited neurons, as I discuss in the latter part of chapter 6, provide barriers to any further empire-building.) And the triangular array is surely ephemeral, here now and gone in seconds. When it is shut

down by enough inhibition (or reduction of excitation), it will be as if a blackboard had been erased.

Traces will linger, however, in much the way that blackboards retain ghostly images of former patterns. The synaptic strengths should remain changed for a while; indeed, the synchrony-sensitive long-term potentiation of the superficial neocortical layers suggests that synaptic strength can remain augmented for many minutes. This will make it easier to recreate the active form of the triangular array — perhaps not all of its spatial extent, but part of it.

THE LATTICE CONNEcnviTY seen in the anatomy, it should be said, does not fall into neat triangular arrays, measured by distance in the tangential plane of section. Though the neuroanatomists speak of "polka-dot" patterns and 'lattices" for the axon terminal clusters in the superficial layers, the spacing of the clusters is only roughly triangular. Of course, adjusting conduction velocity or synaptic delay during a tune-up period could make a triangular array, when replotted as "driving time" rather than distance.

But not even an equal conduction time, for converging simultaneously on a potential recruit, is actually required for the present theory. Though exact synchrony has been convenient for introducing the principles, all that triangular arrays require in the long run is a prenatal tune-up period that results in a good-enough self-organization, so that most of the six surrounding

nodes produce axon clusters that mutually overlap in a manner that aids entrainment. It may not matter to this self-organizing principle what an external observer would find "regular." I'll stick to triangular array terminology for the theory, but don't expect to find exact triangles in either the anatomy or physiology, only good-enough approximations.

FROM A PAIR OF LIKE-MINDED CELLS, we see the possibility of a large

chorus, all singing in synchrony. Furthermore, it's a chorus that can recruit additional members out on its edges. Like a choir standing on risers, these singers tend to space themselves so that each is standing in between two singers on the row below. The choir isn't as perfect a triangular array as the fruit displays at your corner grocery, but it's a good enough approximation to the familiar packing principle.

So far, this choir only chants in unison. It's monomaniacal, perhaps only interested in one feature of the stimulus. It's surely not the true Hebbian cell-assembly The choir corresponding to a concept representation would surely sing in parts, just as sopranos carry one melody and the altos another, each group having different interests. We will need polyphony for harmonious categories, not just chants.

A Compressed. Code Emerges

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