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.” It’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 different 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, falling 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,E,F.” 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. That’s the topic of the book’s penultimate chapter, “The Making of Metaphor.”

    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 it’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, I wrote a commentary in Nature, “The brain as a Darwin Machine,” proposing 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 mechanisms 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,” it’s really the random shuffle of grandparent chromosomes during meiosis as sperm and ova are made, and the subsequent sexual recombination 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 recombinations 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.

    I attempted to apply these six darwinian attributes to our mental processes in The Cerebral Symphony and in “Islands in the mind,” 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, I 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 superficial 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.

    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 (it’s 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. It’s 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.

    And I had the general reader firmly in mind as I did the book design (it’s 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 assumptions. 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 contemplated the hexagonal tile mosaics on the bathhouse floor.

Everyone knows that in 1859 Darwin demonstrated the occurrence of evolution with such overwhelming documentation that it was soon almost universally accepted. What not everyone knows, however, is that on that occasion Darwin introduced a number of other scientific and philosophical concepts that have 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.
    Ernst 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 Delbrück 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.
    Niels K. Jerne, 1967