Psychobiology 1989, Vol. 17 (3),326-333 BOOK REVIEW , computation, and selection: An essay review of 's Neural

PAUL PATrON Uniuersity of Texas at Austin, Austin, Texas and THOMAS PARISI Saint Mary's College, Notre Dame, Indiana

Neural Darwinism: The of neuronal tunately, however, the book suffers on both substantive group selection and stylistic grounds, ultimately failing to convince the By Gerald M. Edelman. 1987, New York: Basic Books. reader that a theory of function has been either de­ 371 pp. $29.95. veloped or presented. In what folIows, we offer a sum­ mary and a critical discussion of Edelman 's ideas as Neuroscientists will have a difficult time ignoring the presented in Neural Darwinism. publication of Neural Darwinism, Gerald Edelman's ex­ The central aim of Edelman 's book is to gain an under­ position of his theory of neuronal group selection. Lav­ standing ofthe neural bases ofperceptual categorization. ish brochures were sent to members of such groups as The ideas that Edelman puts forth on this topic are clearly the Society for and the American Associa­ rooted in his earlier work in , for wh ich he tion for the Advancement of , and prominent ad­ won the Nobel Prize in 1972. Edelman's immunological vertisements heralded the publication in periodicals such work elucidated the structure of molecules, and as the New York Review 0/ Books. In dust-jacket blurbs, in doing so helped to resolve a long-standing puzzle about Maxwell Cowan described the book as "perhaps the most the way the recognizes specific original work on the in thirty years," and (for an account ofthis work see Rosenfield, 1988). Prior lohn Szentagothai remarked that "this is the first occa­ to the work of Edelman and his colleagues, an hypothe­ sion that areal brain theory begins to take shape." To sis of immune function that had been put forward by Linus be sure, not all the hoopla has been positive. In arecent Pauling in 1940 was widely accepted. This hypothesis cover story in the New York Times Magazine, Gunther maintained that the immune system manufactures only one Stent said that he tried hard, but simply could not under­ type of antibody molecule, which is then altered in its con­ stand what Edelman is trying to say. H. B. Barlow (1988), formation according to the kinds of antigens it encoun­ reviewing the book for , asked rhetorically whether ters. In this view, the immune system, at least in part, Edelman's book presages a new era in neuroscience or is built by instruction from the outside world (lerne, is simply a hopeless muddle. Barlow clearly favors the 1967). Edelman helped demonstrate that this "instruc­ latter view. tionist" view is wrong. Rather than one kind ofmolecule, In a time of high specialization in the , shaped or instructed by incoming information, he demon­ when much attention is devoted to reductionist approaches strated that enormous variability exists among the active that emphasize the most molecular details of neural func­ regions of antibody molecules, and that a particular anti­ tion, Edelman's book is a welcome attempt at a compre­ gen selects a particular antibody from among this diver­ hensive synthesis. He has brought together research from sity. The production of a specific immune response then, many disparate areas to defend the notion that the brain involves a form of recognition or categorization. Edel­ works by a process of random variation and selection simi­ man's basic thesis in Neural Darwinism is that the lar to that characteristic of Darwinian . Unfor- processes of perceptual categorization in wh ich the ner­ vous system engages are based on similar "selectionist" mechanisms. The authors would like to thank Gerald Edelman, Bob Wyatt, Vinod Edelman sees current approaches to the problem of per­ Menon, and Torsten Minkwitz for discussions that were useful in the ceptual categorization, which are based on cognitive psy­ preparation of this review. Address a11 correspondence to Thomas Parisi, Department of Psychology, Saint Mary's College, Notre Dame, IN chology and computational neuroscience, as containing 46556. an implicit instructionist assumption that the organism is

Copyright 1989 Psychonornic Society, Inc. 326 BOOK REVIEW 327 instructed by fixed categories present in the environment. occurs a very large degree of individual variation in shape, "Processors of information," Edelman writes, "must extent, and connectivity. (p. 6) have information defined for them apriori" (p. 42). Noting that computers and other electronic devices must What Edelman seems to be saying is that if perceptual be precisely wired in order to function properly,. he ar­ categorization is an information-processing task, then gues that this variation "would doom any eqUivalent the environment must contain a clear and unique set of parallel computer to produce meaningless output even categories that the brain is prearranged to interpret cor­ with the best of error correcting codes" (p. 42). Edel­ rectiy. "At one stage or another of such models," Edel­ man believes that this variability is precisely what is man writes, "one sees evidence of typological thinking needed, however, to fuel a Darwinian process of se­ or -the positing of prior categories either in lection. the environment or in the brain or both" (p. 43). Given the need to account for adaptively varying per­ Having labeled information-processing models of brain ceptual categories and a source of variability upon which function as both essential ist and instructionist, Edelman a process of selection might act in the nervous system, uses evidence drawn from to argue Edelman proceeds to layout the specifics of his theory. that perceptual categories are not fixed or closed, and that In his theory, Edelman makes three specific assertions, these models must therefore be wrong. He notes three which are, confusingly, sometimes referred to as premises major features of perceptual categorization that he regards and sometimes as claims. The first assertion is that the as incompatible with information-processing models. ~h.e does not fully specify the details of the pattern first feature is that perceptual categories are not vendl­ of synaptic connections generated in the developing ner­ cal, but rather are adaptive and modifiable by context. vous system and, therefore, the details of connectivity in "The very same object," he notes, "may be classified the nervous system are highly variant in the sense de­ differently at different times, and an animal may use scribed above. As evidence for this assertion, Edelman different means to classify that object at different times" cites neuroanatomical evidence as weil as his own work (p. 30). Second, Edelman notes that involves on cell adhesion molecules (CAMs). He argues that CAMs the capacity to generalize category membership on the ba­ guide neural morphogenesis, but lack the specificity n~~s­ sis of only a few examples. Referring to Herrnstein's sary to determine fully the pattern of neural connectlvlty (1979, 1985) work demonstrating such an ability in pi­ established during development. The developmental pro­ geons, he argues that this capacity must be independent cess thus produces "primary repertoires [italics addedl of language. of structurally variant neuronal groups" (p. 5). A neu­ A third feature of perceptual categorization that Edel­ ronal group, as Edelman defines it, is "a collection of man believes to be incompatible with an information­ cells of similar or variant types, ranging in number from processing view of perception is that "the classical sum­ hundreds to thousands, that are closely connected in their mary description of a class defined by singly necessary intrinsic circuitry and whose mutual dynamic interaction and jointiy sufficient conditions does not hold" (p. 31) may be further enhanced by increases in synaptic ef­ for perceptual categories; most perceptual categories are ficacy" (pp. 46-47). Each primary repertoire of neuronal polymorphous sets. To qualify for membership in such groups is "degenerate" in that it includes "a signific~nt a set, an element must satisfy a specified number, but not number of non-identical variant groups, each of WhlCh all, of the rules governing membership in the category. could respond more or less weil to a particular input" Edelman concludes that these three features of percep­ (p. 6). . tion "together challenge any logic-based or 'information­ Edelman's second assertion is that there is a competl­ driven' explanation ofthe data" (p. 30). He argues that tive process of selection among neuronal groups within the evidence clearly shows that "the categories of received a primary repertoire, resulting in the selection of "com­ signals can be defined only after the signals have been binations of those particular groups whose activities are received, either because of evolutionary selection or as correlated with various signals arising from adaptive be­ a result of somatic experience" (p. 43-44), and, there­ havior" (p. 5). This selection process occurs by chang­ fore, that the necessary apriori code assumed by ing synaptic with activity, as specified by pre­ information-processing models cannot exist. Edelman synaptic and postsynaptic rules. The competition can result clearly sees an analogy between perceptual categories and in the growth of some neuronal groups by the capture of the Darwinian view of living species, in which species cells from other groups and by the shrinkage or disap­ are regarded as changing and adaptable entities rather than pearance of still others. The outcome of this process of as the fixed categories of the essential ist. selection and competition is the secondary repertoire, In Edelman ' s view, the structure of the nervous sys­ which consists of "functioning groups that are more likely tem is also incompatible with the perception of the brain to be used in future " (p. 5). According to Edel­ as an information-processing system. man, the process is constant and ongoing throughout the Although structures in a particular area of the brain are mod­ life of the organism. As evidence for such plasticity, Edel­ ally similar among conspecific animals, at the level of fine man cites the findings of Michael Merzenich and his axonal and dendritic rarnifications and connections, there colleagues (Merzenich et al., 1983a, 1983b; Merzenich 328 PATTON AND PARISI et al. , 1984), who have shown that the organization of as nothing more than incomplete anticipations of his own the somatosensory cortex in a monkey can change fol­ work, and that lack essential features of his model lowing the amputation of a finger. are dismissed for this reason alone. For example, Edel­ The third assertion of Edelman's theory is that func­ man criticizes Hebb's (1949) theory because "it neglected tionally related groups within different secondary reper­ Darwinian interpretations" (p. 13), and he rejects both toires are linked by "re-entrant" connections. These con­ behaviorism and cognitive psychology because nections provide for spatiotemporal coordination between there is no hint in either view ... that perhaps the problem interacting neuronal groups contained in different second­ [of generalization and categorization] is one of adaptively ary repertoires. If one is observing a firecracker, for ex­ matching neural variants in adegenerate repertoire to ex­ ample, the loud bang and the flash oflight activate differ­ temal situations involving disjunct sets of features or proper­ ent brain regions. Re-entrant connections, in Edelman's ties, thus allowing generalization to occur through processes view, provide a way of linking these different aspects of similar to those by which taxa are created during evolution. an object or event into a coherent perception. Individual, (p. 13) electrophysiologically described maps are regarded by Edelman's view of the history of theories of brain func­ Edelman as being examples of secondary repertoires, and tion and psychology is especially ironic in light of his em­ are referred to as "local maps." Re-entrant connections, phasis on perception as adaptive and historically contin­ both sensory and motor, between groups contained within gent rather than veridical. multiple local maps, together with connections with non­ Edelman often dismisses the views of others with dis­ mapped regions, form "global maps." Global maps em­ paraging remarks rather than with a cogent defense of his body spatiotemporally continuous representations of ob­ dismissal. For example, in discussing Ebbesson's theory jects or events, and generate motor with respect of parcellation in development, Edelman disrnisses the the­ to them: ory at least twice because it is "recapitulationist": "this Eaeh repertoire within the loeal maps of agiobai mapping ontogenetic explanation smacks too much of recapitula­ disjunetively sampies various aspeets or features of the en­ tionist views and is much too narrow" (p. 155). Why is vironment. Connection of these local maps ... in agiobai it recapitulationist? In what way is it too narrow? Edel­ mapping serves to link sampies by reentry so that various man feels no need to explain. representations of features are correlated in space and time. Let us now turn to a consideration of the substantive None of the local maps would be adequate for perceptual issues raised by Edelman's book. We will begin with a categorizations or generalizations. Agiobai mapping pro­ critique of Edelman's metatheoretical assertions regard­ vides the minimal unit capable of such function. (p. 210) ing the value of computational approaches to brain func­ According to Edelman, global mappings, and their for­ tion, and will then turn to an analysis of the specific as­ mation, dissolution, and selective alteration with time, in sertions of his theory of neuronal group selection. turn provide the basis for learning and . The motive behind Edelman's theory, it seems to us, Before turning to a critique of the substantive aspects is a dissatisfaction with the idea of the computer as a of Edelman's book, we must first note that the book metaphor for brain function. In recent decades, this suffers from serious stylistic difficulties which often make metaphor has exerted a profound influence on all dis­ it nearly impossible for the reader to understand the cen­ ciplines that deal with the brain, behavior, and tral elements of what Edelman is trying to say. One often (Gardner, 1985). Edelman, however, believes that the encounters awkward, convoluted sentences. When faced problems of accounting for adaptively varying perceptual with the choice between a simple and an obtuse term, categories, perceptual generalization, the polymorphous Edelman either chooses the latter or invents a new one character of perceptual categories, and the occurrence of that is even more troublesome. To cite one example, Edel­ reliable brain function in the presence of anatornical vari­ man introduces the term "instructionism" in an appar­ ance are so serious as to warrant the abandonment of the ently novel context without ever defming it. We were able computer metaphor altogether and replacing it with his to infer what Edelman means by this term only after dis­ selectionism. Edelman teIls us that he seeks "to account covering its usage in the immunological literature. In ad­ for [perceptuaI] categorization without assuming informa­ dition, the book is repetitive: the main points are sum­ tion processing or " (p. 4). "It is surprising, " marized many times. More attention to organization and he writes, "to observe that neurobiologists ... can ... some sense of audience would have resulted, we would believe that precise algorithms are implemented and that hope, in a much briefer and more readable book. It is computations and calculations of invariance can take place difficult to imagine how an editor could have allowed this inside neural structures" (p. 42). "The selective behavior book to be published in its present form. of ensembles or ," he states, "may be describ­ In addition to problems of style, we must also remark able by certain mathematical functions ... but it seems that the book is, at times, marred by a seeming arrogance as unlikely that a collection of neurons carries out the com­ and condescension. The arrogance comes through most putation of an algorithm as that interacting lions and ante­ c1early in the first chapter ofthe book, in which Edelman lopes compute Lotka-Volterra equations" (p. 235). Un­ traces the history of ideas leading up to his own theory. fortunately, as we will see, Edelman's views regarding Edelman appears to view past theories of brain function the computer metaphor are full of inconsistencies and BOOK REVIEW 329 contradictions that are likely to thoroughly confuse even eyes' images. The mathematics of a valid computational the most careful reader. theory of binocular stereopsis must, in some sense, be Edelman ' s specific point of attack is the computation­ embodied in any system that successfully solves the alist approach to brain function developed by the late problem of binocular stereopsis (whether by a straight­ David Marr of the Massachusetts Institute of Technology forward route or by an awkwardly convoluted one), in his book Vision (1982). "Tbe present proposal," Edel­ whether that system is a computer or a brain. man writes, "differs strongly from the computationalism Edelman is not alone in criticizing Marr's computational of Marr: it rejects the idea that the nervous system com­ theories of vision. Patricia Kitcher (1988) has noted that putes a function" (p. 235). Marr's central concern in Vi­ Marr's theories contain an implicit assumption that com­ sion was to explain how the brain gene rates an internal putations are carried out by the brain in the most optimal representation of a persisting three-dimensional world way. Citing the arguments of Gould (1980) and of Gould of surfaces and shapes from the shifting array of light and Lewontin (1979), she points out that such an assump­ intensities and wavelengths that impinge on the two­ tion is unlikely to be valid in a system produced by an dimensional surfaces of the retinas of the eyes. Marr's bistorical process of random and natural se­ definition of the problem of vision in this fashion, and lection without an intelligent designer. Paul Grobstein his methods of approaching the problem, were novel. Al­ (in press-a) noted that throughout much of Vision Marr though Edelman charges that in Marr's work "categories seemed to advocate computational theory over direct in­ of natural objects in the physical world are implicitly as­ vestlgation of the nervous system. Grobstein has stressed sumed to fall into defined classes or typologies that are the importance of direct investigations in the identifica­ accessible to a program" (p. 38), in fact Marr did not tion and definition of the computational tasks performed address the problem of object categorization or recogni­ by the nervous system, and in the discovery of new modes tion. "This theory, " he wrote, "has nothing to say about of neural information processing that may not have been semantic recognition, object naming, or function.. .. I imagined by computer scientists. He has suggested that think that an understanding of what we mean by the computational investigations can best be guided by nervous­ semantics of an object are fascinating, but I also think that system studies involving brain Iesioning and behavioral they are very difficult indeed and at present much less analysis. Unlike Edelman, however, neither of these accessible" (p. 355). critics has advocated the abandonment of the computa­ Marr' s computationalism consists of a set of particular tional level of investigation. theoretical models of visual function and, more fundarnen­ Let us now consider whether Edelman has been suc­ tally, of a set of metatheoretical assertions about the way cessful in consistently rejecting computationalism. What such problems should be addressed. Marr began bis career does it mean to say that the brain computes an algorithrn? as an electrophysiologist, but eventually realized that if An algorithrn is defined as a set of unambiguous rules for brain function is to be understood, something more is solving a problem or for performing a procedure in a finite needed than simply adescription of the behavior and the number of steps (Knuth, 1969). Like computers, brains properties of neurons in particular regions of the brain. can solve complex problems within a finite period of time, and there must be some orderly process by which they Trying to understand perception by studying only neurons do so. At its most basic level (what Marr would call the is like trying to understand bird flight by studying only level of implementation), an electronic computer is noth­ feathers. It just cannot be done. In order to understand bird flight, we have to understand aerodynamies; only then do ing more than a that gates electrical impulses in the structure of the feathers and the different shapes of birds' a complex pattern. On this level, a computer no more wings make sense. (Marr, 1982, p. 27) computes than do the lions and antelopes of Edelman's example. To understand the orderly process that is going Marr rejected the reductionist notion that complex brain on in the computer, and to develop new computer pro­ functions can be explained in terms of neurons alone, and grams, however, computer scientists must make use of instead proposed a multileveled explanatory scheme with algorithms and computational theories. Computational­ neural implementation as only one level. He placed par­ ism asserts that it is also valuable to make use of al­ ticular importance on the top level of his scheme, that of gorithms and computational theories to describe the tasks computational theory. the brain performs and the orderl~ process by which it Just as a theory of aerodynamics is essential for under­ performs them. Computationalism no more requires that standing bird flight, Marr maintained, so must one de­ an algorithrn somehow exist objectively inside the brain velop an abstract theory of how a particular information­ than it requires that an algorithrn exist objectively inside processing task can be performed in principle if one the computer. It only requires that the tasks that the brain wishes to understand how the brain performs that task. performs be describable by a computational theory, and This computational theory must, by its nature, be mathe­ that the methods that the brain uses to perform them matical. One example, which Marr studied in great de­ be describable using algorithms (Sejnowski, Koch, & tail, is the task ofbinocular stereopsis. To develop a theory Churchland, 1988). of the task of stereopsis, one must deal in the trigonome­ Can the solutions to problems such as perceptual try ofbinocular parallax. One must also have mathemat­ categorization be stated in a finite number of unambigu­ ical rules for matching corresponding points in the two ous steps? Currently, computers are generally used to 330 PATTON AND PARISI solve problems that possess a clear logical structure and tion. In other words, it would be possible fOT a neural com­ a single correct solution. An example of such a problem puter to program itself. (Abu-Mostafa & Psaltis, 1987, p. 93) is the computation of the logarithm of a number. For such problems, a relatively compact algorithm can be stated This is precisely what Edelman is claiming for the brain that permits the computation of a solution in only a few in his theory ofneuronal group selection. Edelman's con­ steps. However, Edelman doubts that the problem of per­ cerns about the lirnitations of algorithms seem meaning­ ceptual categorization or object recognition is one of this ful only if one accepts a far more restrictive definition sort. The world, he comments, "is initially an unlabeled of an algorithm than that adopted by Abu-Mostafa and place" (p. 3), by which he means that it contains no prior Psaltis. However, the usefulness of such a restriction is logical categorical structure upon which a succinct al­ unclear. gorithm for perceptual categorization might be based. Given Edelman's stated aim of accounting for percep­ Many of Edelman ' s central concerns about perceptual tual categorization "without assuming information categorization have also been raised by some computa­ processing or computing, " most readers of NeuraL Dar­ tionalists, who believe that neural pattern recognition may winism will be surprised to discover that Edelman presents involve the solution of problems that lack a clear logical a number of computer models that illustrate various structure (Abu-Mostafa & Psaltis, 1987). These investi­ aspects of bis theory of perceptual categorization. In chap­ gators distinguish between two different sorts of computa­ ter 3, for example, Edelman presents a computer model, tional problems. The first sort of problems are structured called Darwin I, that illustrates some aspects of his theory problems, like those discussed above, that can be solved of perceptual categorization. This program is presented using a succinct algorithm. The second sort of problems, as a model for several aspects of perceptual categoriza­ known as random problems, lack sufficient logical struc­ tion considered in the abstract, without direct reference ture to permit a simple algorithmic solution. Abu-Mostafa to the nervous system. It appears to us that, without know­ and Psaltis, like Edelman, regard the problem of object ing it, Edelman is attempting to formulate a computational recognition as one of this sort. They do not, however, theory for the task of perceptual categorization. We have see this as reason for abandoning the notion that such the same impression of Edelman ' s model of pattern recog­ problems can be solved by a process that involves a finite nition, Darwin 11. Here, Edelman teIls us that pattern number of unambiguous steps. They propose instead that categorization can be accomplished by means of two neu­ the algorithm for solving such a problem should consist ral networks, linked as a classification couple. One ofthe of a stored list of all possible solutions to the problem networks serves as a feature detector and the other as a along with rules for choosing among them. feature correlator. The assertion that coupled networks Consider, for example, the problem of recognizing a that possess these characteristics are sufficient for pattern picture of a tree. One may attempt to solve this problem recognition seems to us to be an abstract statement about as a structured problem by using a brief list of stored rules the task of pattern recognition rather than a statement that distinguish a tree from all other objects. This is clearly about the brain itself. In other words, it seems to be an the sort of approach to which Edelman objects when he assertion concerning the computational theory of the task denies that the world possesses a prior categorical struc­ of pattern recognition. Perhaps there is some way this ap­ ture. Indeed, computer models of object recognition based parent contradiction can be justified in terms of computer on such an approach have had little success. Another ap­ theory. If there is, Edelman makes no serious attempt to proach would be to store the images of a large number present it. In the absence of such an explanation, few of trees in the computer, along with rules for comparing readers are likely to find his rejection of computational­ any input image with the stored images. Abu-Mostafa and ism convincing. What does seem clear is that if aspects Psaltis (1987) have reported some success in using such of Edelman's theory can be embodied in computer mod­ a strategy for pattern recognition in experiments aimed els, then those aspects must be statable in terms of al­ at developing pattern-recognizing parallel computers us­ gorithms, or the necessary computer programs cannot be ing optical technology. Their strategy closely parallels that written. Edelman thus does not seem to have escaped suc­ employed in Edelman's computer model of perceptual cessfully from the assumption that the brain computes in generalization: Darwin I. an algorithrnically describable fashion. Abu-Mostafa and Psaltis (1987) have pointed out that Although Edelman does not consistently seem to have if the problem of recognizing a picture of a tree is treated rejected the computer metaphor in any deep sense, his as a random problem, the need for any prior knowledge approach to brain function clearly differs in important of trees is eliminated. The programmer of a computer ways from that of traditional or com­ designed to solve random problems (which Abu-Mostafa putational neuroscience. Marr and other computationalists and Psaltis refer to as a "neural" computer) have concerned themselves with the question of how the brain performs its functions and have devised algorithms does not have to understand in a fonnal mathematical sense the problem for wh ich he OT she is programming. The and computational theories that incorporate knowledge programmer only has to provide enough "training" data about such functions. Edelman instead asks how the brain (consisting ofpossible solutions) to the computer and allow arrives at the ability to perform its functions without prior it to set up a unique pattern of connections for each solu- knowledge. We do not currently know of any way that BOOK REVIEW 331 algorithms or computer programs can be generated with­ and that no two individual brains exhibit an identical anat­ out a computer programmer . Because Marr and other omy. He also points out that cortical sensory maps, as computationalists have not addressed the problem of mapped electrophysiologically, show an extraordinary where the brain gets its algorithms, Edelman charges that degree of variability from individual to individual. these investigators implicitly assume a homunculus in the However, this evidence is hardly compelling. The fact brain to serve as its programmer . that variability exists in repeated structures teIls us noth­ Although Edelman's statement on the homunculus ing about whether the source of that variability is genetic problem does not, in our view, invalidate the computa­ or epigenetic. Since different individuals have different tionalist program, it does highlight an area of major im­ , individual differences in cortical maps might portance that computational neuroscience has so far left be due to genetic, rather than epigenetic, influences. largely unexplored. Edelman's answer to the homuncu­ As additional evidence against complete genetic specifi­ lus problem is that orderly, adaptive function in the course cation, Edelman presents a detailed review of the role of of Darwinian evolution must arise from the bottom up by CAMs in development and of studies of the formation of natural selection and by a process of neuronal group selec­ cortical maps. He argues that the picture of development tion acting during the lifespan of a single organism. The as presented by modern developmental suggests basis for generating this order, in Edelman 's view, is the that the development process lacks sufficient specificity presence of small-scale anatomical variability in the ner­ to allow the genome to determine neural connectivity vous system, sensory input from the environment, and fuIly. However, an assertion that the known mechanisms a set of rules governing the modification of synaptic governing development lack the specificity to determine strengths with activity. It should be noted that these rules, neural connectivity fully is not in itself compelling evi­ unlike the algorithms generally postulated by computa­ dence that such specificity could not exist. tional neuroscientists, contain no information about the On a theoreticallevel, Changeux and Danchin (1976) task that the network is to perform. Therefore, this sort have argued that the genome cannot contain sufficient in­ of model is known as a noninstructed neural-network formation to specify neural connectivity fuIly. In a recent model. Edelman claims that such a network, if properly review, Grobstein (1988c) cited a considerable body of organized, can arrive at adaptive function by means of evidence, derived from studies of growth, metamorpho­ a competitive process of neuronal group selection that sis, and sensory experience, that ceIl-cell recognition arises as a result of the operation of the synaptic efficacy processes are not by themselves sufficient to determine rules. At its deepest level, such a network is a computa­ network connectivity uniquely, and argued that additional tional procedure like any other, with rules governing the epigenetic influences are needed. Given the strength of modification of synaptic efficacy with activity. The oper­ these theoretical and empirical arguments, it seems rea­ ation ofthese rules, however, makes the system capable sonable to grant Edelman's assertion that the genome does of -organization in response to activity, and thus makes not fully determine the pattern of neural connectivity. it capable, in Edelman 's view, of generating adaptive be­ However, such a conclusion cannot be reached on the haviors without built-in instructions for those behaviors. strength of the evidence that Edelman himself emphasizes. For many neurobiologists, a model of this general sort Edelman reviews some evidence that is suggestive of will hold great appeal. Grobstein (1988b, 1988c), for ex­ a role for competitive and selective interactions during ample, has argued cogently that the phenomenon of adap­ the formation of what he calls the primary repertoire. He tive plasticity in brain and behavior is of central impor­ notes the widespread occurrence of cell death during de­ tance to any account of behavioral brain function. The velopment, along with evidence that patterns of cell death merit of Edelman's particular development of this idea, are governed by the success of individual cells in form­ however, depends on the specifics of his proposal. Let ing appropriate connections rather than being genetically us now turn to an examination of these specifics as they preprogrammed. He also notes evidence of competition are presented in Neural Darwinism. in synapse formation at neuromuscular junctions. The first major assertion of Edelman's theory of neu­ Edelman devotes a much greater effort to the develop­ ronal group selection is that the genome does not fully ment of selectionist models related to the second major. determine the details of neural connectivity. This indeter­ assertion of his theory of neuronal group selection: the minism, together with the tendency ofaxonal terminal ar­ formation of a secondary repertoire of neuronal groups bors to overlap broadly, provides a source of random by selection from the primary repertoire. Here, however, variability upon which a selective process may act. The we encounter problems that have already been noted by opposing view, that the nervous system is hardwired by Barlow in his review of Neural Darwinism (Barlow, the genome, is generally associated with the chemoaffinity 1988). A major problem is that Edelman's definition of hypothesis, commonly attributed to Roger Sperry (see dis­ a neuronal group is confusing and imprecise. At one point, cussion in Grobstein, 1988c). he defines a cortical neuronal group as "an ensemble of Edelman seems to regard the mere existence of ana­ cohesively interconnected ceIls, all of which express the tomical variability in the nervous system as evidence for same receptive field" (pp. 164-165). In the visual cor­ genetic indeterminism. He notes, for example, that in tex, as Barlow has noted, there is no experimental evi­ repeated neural structures no two cells within a given dence to support the claim that nearby cells have identi­ structure exhibit an arbor with precisely the same shape, cal receptive fields. When questioned on this matter by 332 PATTON AND PARIS I one of the present authors, Edelman replied that he meant nated motor acts to acquire the information necessary to that the cells express similar receptive fields. Edelman's form perceptual categories. carelessness in describing such crucial aspects of his the­ Edelman bolsters these claims by presenting a computer ory is a major barrier to understanding and accepting it. model of perceptual categorization known as Darwin 11. We also wonder, as did Barlow (1988), what functional This model, which uses printed characters as its input advantage could be conferred by having a set of cells with stimuli, consists of a digital computer simulation of two a similar or identical function that grows to incorporate interconnected neural networks known as "Darwin" and more cells if it is frequently activated? Much of Edelman 's "Wallace. " The Darwin network is a feature-detector net­ discussion of the formation of the secondary repertoire work. The first level of the network consists of an array is directed specifically toward accounting for the plas­ of detectors sensitive to line segments of various orienta­ ticity of topographic somatosensory maps reported by tions or with particular bends. The Wallace network is Merzenich and his colleagues (Merzenich et al., 1983a, a feature-correlator network, the first level of which 1983b; Merzenich et al., 1984), rather than dealing with responds to continuity properties of objects. The in­ the question of the functional significance and origin of dividual elements of these networks represent neuronal the varied and often complex receptive-field properties groups, each network corresponds to a set of secondary found in the cells that form cortical maps. repertoires, and the system as a whole corresponds to In Edelman's theory, the appearance, selection, and a global mapping. Edelman presents evidence that this competitive growth of neuronal groups is an outcome of model exhibits a lirnited capability to form categories and the operation of pre- and postsynaptic rules that act in­ to generalize category membership using letters ofthe al­ dependently to alter the efficacy of pre- and postsynaptic phabet as stimuli. terminals. The novel feature ofthese rules is that changes Edelman believes that global mappings are sufficient in the efficacy of a synaptic terminal can be influenced to account for perceptual categorization, and that they are by activity at other synaptic terminals of the same cello the foundations for higher order brain functions such as In the postsynaptic rule, changes in the efficacy of a post­ learning and memory. More generally , he asserts that synaptic terminal occur when its activation closely cor­ peripheral sensory- systems "abstract major adap­ res ponds in time to that of other synaptic terminals of the tive properties and that the function of higher order neu­ same cello The influences of other terminals are conveyed . ral networks is not to 'compute' but to correlate these ab­ to the terminal in question either by electronic spread or stracted properties by further reentrant selection among by diffusible messenger molecules. Edelman's presynaptic selected populations of participating neuronal groups" rule states that the efficacy of presynaptic terminals is al­ (p. 234). Perception, however, involves more than sim­ tered based on the long-term average ofthe instantaneous ply categorizing groups of sensory stimuli. Studies of presynaptic efficacy of the cello As elsewhere in his book, many animals, for example, have provided evidence that this vital discussion is obscured by stylistic problems. We they are capable of extracting information about the dis­ were not able to determine precisely what governs changes tribution and three-dimensional structure of shapes and in the instantaneous presynaptic efficacy of the cell in surfaces in their environment from the patterns of light Edelman's model. falling on their retinas (Collett & Harkness, 1982; Marr, Although Edelman 's discussion of the formation of 1982). It may be that, for some animals, the genera­ secondary repertoires seems geared primarily towards ex­ tion of such an internal representation of their three­ plaining the plasticity of topographic representations dimensional environment is a necessary first step or a reported in experiments such as those of Merzenich and valuable aid to perceptual categorization. The question his colleagues (Merzenich et al. 1983a, 1983b; Merzenich of how this representation is formed was the central con­ et al. 1984), broader functional considerations are more cern of Marr's computational investigation of vision. Marr directly addressed in the third assertion ofEdelman's the­ showed that to generate an internal representation of this ory of neuronal group selection. This assertion states that sort, the brain must solve computational problems of con­ individual secondary repertoires are linked into global siderable difficulty. Edelman has little to say as to whether mappings by reentrant connections, and that these global his global mappings can solve these problems, and if mappings form the minimal units of perceptual categori­ so, how. zations. Edelman's major concern in proposing reentrant There are many instances in which animals must deal connections is that different properties of a particular ob­ with the geometry of their world, and the evidence sug­ ject or event in the sensory environment are represented gests that the brain mechanisms that have evolved to carry in separate, spatially distinct, topographic maps. These out these tasks are complex indeed. Orientation of the head maps are Edelman's answer to the question of how all or body with respect to astimulus, for example, was once these separately registered, but simultaneously occurring, thought to be a simple matter of establishing a fixed corre­ properties are linked together and placed in a single lation between a locus on a sensory map corresponding category. Global mappings also link sensory structures to the direction of the stimulus, and a locus on a motor to motor structures. Edelman believes that perceptual map capable of triggering an appropriately directed move­ categorization is an active process that requires coordi- ment. The study of occulomotor orientation in the mon- BOOK REVIEW 333 key (Mays & Sparks, 1980; Sparks & Mays, 1983) and, GOULD, S. J. (1980). 1he pandas thumb. New York: Nonon. in particular, of visually evoked prey orienting movements GOULD, S. J., & LEWONTlN, R. (1979). The Spandreis of San Marco in the frog (Grobstein, 1988a, in press-b; Kostyk & Grob­ and the panglossian paradigm: A critique of the adaptationist program. Proceedings of the Royal Society of London, B205, 581-598. stein, 1987), have demonstrated that the control of such GROBSTEIN, P. (1988a). Between the retinoteclal projection and directed movements involves computations of far greater complex­ movement: Topography of a sensorimotor interface. Brain, Behavior. ity than the simple correlation of sensory and motor maps. & Evolution, 31, 34-48. In the case of the frog, there is evidence that generating GROBSTEIN, P. (1988b). From the head to the hean: Some thoughts on similarities between brain function and morphogenesis, and on their an orienting turn involves an intermediate processing level significance for research methodology and biological theory. Expe­ in which signals are coded in neither a retinal- nor a rientia, 44, 960-971. movement-coordinate frame, but rather in some more GROBSTEIN, P. (1988c). On beyond neuronal specificity: Problems in generalized frame of reference (Grobstein, 1988a). Edel­ going from cells to networks and from networks to behavior. In P. G. man's theory deals with how the brain categorizes sen­ Shinkman (Ed.), Advances in neural and behavioral development: Vol. 3 (pp. 1-58). New York: Albex. sory stimuli and motor acts, but he has very little to say GROBSTEIN, P. (in press-a). Strategies for analyzing complex organi­ about how the brain deals with the geometrical aspects zation in the nervous system: 1. Lesion experiments: The old re­ of either perception or movement. In neglecting to con­ discovered. In E. Schwanz (Ed.), Systems development foundation sider such matters explicitly, he risks building aglobai benchmark series in computational neuroscience. Cambridge, MA: theory of perception and action that fails to capture es­ MIT Press. GROBSTEIN, P. (in press-b). Strategies for analyzing complex organi­ sential elements of both. zation in the nervous system: 2. A case study: Directed movement A driving force behind Edelman's book is bis insistence and spatial representation in the frog. In E. Schwanz (Ed.), Systems on rejecting the computational approach to brain function development foundation benchmark series in computational neuro­ developed by Marr and others. Although he supposedly science. Cambridge, MA: MIT Press. HEBB, D. O. (1949). 1he organization ofbehavior: A neuropsychalog­ rejects computationalism, Edelman seems, paradoxically, ical theory. New York: Wiley. to be groping towards a computational theory of percep­ HERRNSTEIN , R. I. (1979). Acquisition, generalization and discrimina­ tual categorization. His argument that selectionism can tion of a natural concept. Journal ofExperimental Psychology & Animal replace computationalism seems to us as misplaced as an Behavior Processes, 5, 116-129. argument that evolutionary theory could replace physiol­ HERRNSTEIN, R. J. (1985). Riddles ofnatural categorization. Philosophi­ cal Transactions of the Royal Society of London [BiologyJ, 308, ogy. Edelman ' s presentation is so confused and confus­ 129-144. ing that few readers will be convinced that a true theory JERNE, N. K. (1967). and learning: Selection versus instruc­ of brain function has been presented. Many readers may tion. In G. C. Quanon, T. Melnechuk, & F. O. Schmitt (Eds.), 1he nevertheless find aspects of the basic thesis of Neural Dar­ neurosciences: A study program (pp. 200-205). New York: RockefeIler University Press. winism to be highly intriguing. The need to provide an KITCHER, P. (1988). Marr's computational theory ofvision. explanation of the neural basis for the ability of an in­ of Science, 55, 1-24. dividual organism to adapt its behavior to its environment, KNUTH, D. E. (1969). 1heart ofcomputerprogramming: Vol. I. Fun­ as weIl as the need to account for adaptive plasticity in damental algorithms. Reading, MA: Addison-Wesley. the nervous system, is widely recognized. Darwinian evo­ KOSTYK, S., & GROBSTEIN, P. (1987). Neuronal organization under­ Iying visually elicited prey orienting in the frog: 1. Effects of vari­ lution is the only process in nature that we know of that ous unilateral lesions. Neuroscience, 21, 41-55. produces adaptive order without intelligent design. It thus MARR, D. (1982). Vision: A computational investigation into the hu­ seems natural to suggest that a similar process, operating man representation and processing of visual information. New York: within the brain of an individual organism, might adapt Freeman. MAYS, L. E., & SPARKS, D. O. (1980). Saccades are spatially, not the and actions of that organism to its environ­ retinocentrically coded. Science, 208, 1163-1165. ment. Edelman ' s book serves the valuable function of rais­ MERZENICH, M. M., KAAS, J. H., WALL, J. T., NELSON, R. J., ing these issues for a broad scientific audience for the first SUR, M., & FELLEMAN, D. J. (l983a). Progression of change fol­ time. Paradoxically, it seems likely that these ideas will lowing median nerve section in the conical representation of the hand ultimately add legitimacy to the very metaphors of com­ in areas 3b and 1 in adult owl and squirrel monkeys. Neuroscience, 10, 639-665. putationalism that Edelman is trying to undo, by suggest­ MERZENICH, M. M., KAAS, J. H., WALL, J. T., NELSON, R. J., ing ways by which brains, or computers, can arrive at SUR, M., & FELLEMAN, D. J. (l983b). Topographical reorganization adaptive function. of somatosensory conical areas 3b and I in adult monkeys following restricted deafferentation. Neuroscience, 8, 33-55. MERZENICH, M. M., NELSON, R. J., STRYKER, M. P., CYNADER, M., REFERENCES ScHOPPMAN, A., & ZooK, J. M. (1984). Somatosensory conical map changes following digit amputation in adult monkeys. Journal of Com­ ABU-MOSTAFA, Y. S., &0 PsALTIS, D. (1987). OpticaJ neural computers. parative Neurology, 224, 591-605. Scientific American, 256, 88-95. ROSENFIELD, 1. (1988). 1he invention of memory: A new view of the BARWW, H. B. (1988). Neuroscience: A new era? Nature, 331, 571. brain. New York: Basic Books. CHANGEUX, I. P., &0 DANCHIN, A. (1976). Selective stabilization of SEJNOWSKl, T. J., KOCH, C., & CHURCHLAND, P. S. (1988). 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