Neural plasticity and concepts ontogeny∗

Alessio Plebe and Marco Mazzoneaplebe@unime.it, mazzonem@unict.it

Abstract

Neural plasticity has been invoked as a powerful argument against nativism.However, there is a line of argument, which is well exemplified by Pinker (2002)and more recently by Laurence and Margolis (2015a) with respect to concept na-tivism, according to which even extreme cases of plasticity show important innateconstraints, so that one should rather speak of “constrained plasticity”. Accordingto this view, cortical areas are not really equipotential, they perform instead differ-ent kinds of computation, follow essentially different learning rules, or have a fixedinternal structure acting as a filter for specific categories of inputs. We intend to an-alyze this argument, in the light of a review of current neuroscientific literature onplasticity. Our conclusion is that Laurence and Margolis are right in their appeal toinnate constraints on connectivity – a thesis that is nowadays welcome to both na-tivists (Mahon and Caramazza, 2011) and non-nativists (Pulvermuller et al, 2014)– but there is little support for their claim of further innate differentiation betweenand within cortical areas. As we will show, there is instead strong evidence that thecortex is characterized by the indefinite repetition of substantially identical compu-tational units, giving rise in any of its portions to Hebbian, input-dependent plas-ticity. Although this is entirely compatible with the existence of innate constraintson the brain’s connectivity, the cerebral cortex architecture based on a multiplicityof maps correlating with one another has important computational consequences,a point that has been underestimated by traditional connectionist approaches.

1 IntroductionThe nativism debate has reached nowadays a significant degree of sophistication. Onthe one hand, very strong versions of nativism have been substantially abandoned,as is the case for Fodor’s radical concept nativism or earlier versions of Chomsky’snativism, due to theoretical arguments (Karmiloff-Smith, 1992; Elman et al., 1996;Cowie, 1999; Prinz, 2002; Weiskopf, 2008), to a greater appreciation of the power ofstatistical learning (Reali and Christiansen, 2005; Romberg and Saffran, 2010; Per-fors et al., 2011; Wonnacott, 2013), and to the substantial evidence of plasticity in thebrain (Churchland, 1988; Kolb, 1995; Buonomano and Merzenich, 1998; Huttenlocher,2002; Møller, 2006; Bermudez-Rattoni, 2007; Stiles et al., 2012). On the other hand,∗The final version of this article will appear in Synthese, please do not quote from this version without

authors’ permission.

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much is known today about the intrinsic, genetic factors that shape not only subcorticalstructures but also neocortex (O’Leary et al., 2007; Roubertouxs et al., 2010; Ruben-stein and Rakic, 2013b), and scholars on the empiricist side are now very careful toavoid identification with naıve, “Tabula Rasa” formulations of this position. A niceillustration of such conciliatory attitude is (Quartz, 2003, p.34), who prefers to evenabandon the term “empiricism” and to state instead his strategy “in terms of not beingstrongly innate”.

However, some nativists apparently think that the step backwards on the part ofempiricists is not sufficient and that a stronger stand on nativism is still in order. A re-cent paper by Laurence and Margolis (2015a) (from now on, L&M) takes this stand byfrontally challenging the argument from neural plasticity, in the line of Pinker (2002).We think that their line of argument deserves careful consideration in that it gives theopportunity to make some important clarifications about what the current evidenceabout neural plasticity actually shows. Specifically, we intend to argue that argumentsof that sort cannot be used to stretch the boundaries of what is innate far beyond whatis conceded by sophisticated empiricists such as Quartz, and that the current evidencespeaks in favour of a rather clear point of equilibrium between the two sides of the dis-pute. This point of equilibrium actually requires acknowledging some constraints onplasticity, as proposed by L&M, but not of the kind that would be required if a robuststand on nativism had to be taken. We will especially focus on concept nativism, whichis the main concern of L&M’s paper. The proposal defended here is that the point ofequilibrium relative to concept nativism is something very close to sophisticated em-piricism a la Quartz (2003), and in fact we essentially aim to make more precise hisclaim that the innate constraints justified by the actual evidence do not speak in favourof “static restrictions to a fixed hypothesis space” (idem: 34), which is what would berequired instead if (strong) concept nativism were correct. In practice, we take a brainstructure to be innate not only in case it is fixed from the beginning, but also if there isa predetermined developmental course leading to it, such that its dependence on inputsis a case of triggering rather than learning. Our claim is that even with this notion inmind, there is little support for concept nativism.

In practice, the plan of the paper is as follows. First (§2), we summarize the sort ofevidence that L&M make an appeal to, as well as their interpretation of that evidenceas showing innate constraints on plasticity, both in terms of “functional connectivity”and (local) “computational structure”. Second, we survey current neuroscientific ev-idence about neural plasticity (§§3 and 4). Third, we turn back to L&M’s argumentson the basis of that evidence (§5). Specifically, we will argue that there is no suffi-cient basis for their assumption of a local computational structure leading to a fixed orpredetermined hypothesis space. On the other hand, we will argue that the insistenceon innate functional connectivity in the brain has become common ground betweenthe two parties, and that this is not by chance: although innate connectivity is indeedstructure innately superimposed on brain’s ability to plastically adjust to external in-puts, connections constrain the functional specialization of brain areas essentially as afunction of their respective inputs, and therefore connectivity is far from favouring thesort of concept nativism that L&M have in mind.

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2 The argument of Laurence and Margolis (2015a)In support of concept nativism, L&M provide what they call an argument “from neuralwiring”. Their presentation of the argument is divided into two parts. In the first part,they aim to show that even in the most striking examples of plasticity, which make na-tivist views look hopeless, the evidence can be reinterpreted so as to show that changesin the brain are less flexible and open-ended than they may appear at a first look. Inthe second part, they consider evidence that directly supports, in their view, the nativistperspective. However, the general strategy underlying the two parts is largely similar.It is based on the claim that the brain is not indefinitely equipotential; on the contrary,plasticity would be strongly constrained by innate factors so that the function of mostbrain areas is essentially left unchanged even when important changes occur. In short,L&M insist that the evidence only supports a conclusion of “constrained plasticity”.Let us now consider the two parts of the “neural wiring” argument in turn.

2.1 Striking plasticity and its interpretationAs examples of striking plasticity, L&M cite the case of EB and the very well knownstudies of Sur and collaborators on ferrets with rerouted retinal projections.

EB is a boy who had his left cerebral hemisphere removed when he was two yearsold and yet recovered near-to-normal linguistic skills in a few years. However, accord-ing to L&M, the crucial point is that the areas of his right hemisphere involved in therecovery of linguistic abilities are not scattered in unpredictable ways. On the con-trary, as shown by a comprehensive functional magnetic imaging (fMRI) study of EB(Danelli et al., 2013), those areas are homologues of the left-hemisphere areas knownto be involved in linguistic tasks and actually activated in control subjects. The con-clusion of the fMRI study is in fact that “the overall neurofunctional architecture ofEB’s right hemispheric language system mirrors a left-like linguistic neural blueprint”(Danelli et al., 2013, p.225). This first example already shows the general line of ar-gument that is further developed in the rest of the paper. The claim is that the wayin which cognitive abilities are distributed across the brain is not haphazard; it showsinstead the pressure of innate constraints.

This view is explored in much more detail with reference to the example of therewired ferrets. Sur and collaborators (Roe et al., 1990; Sharma et al., 2000; von Melch-ner et al., 2000; Sur and Leamey, 2001) conducted anatomical experiments in ferrets,rerouting the normal connections from the eye to the primary visual area, so that theyreached instead the auditory cortex. As a result, the auditory cortex was enabled tomake visual distinctions. This is reputed to be one of the most extreme cases of plas-ticity, although, following Pinker (2002), L&M seem not much impressed by it. Theyrecall in fact three objections already raised by Pinker.

First, they claim that, in spite of changes in the cortical maps that are directlyaffected by the rerouted inputs, areas involved in further downstream processing (rep-resentations of location, direction of motion, speed etc.) seem little changed in theirfunction. In fact, however, we know of no evidence for reduced changes downstream ofA1 in comparison with A1 itself. L&M refer to Majewska and Sur (2006), but the onlysuch claim that we found in that paper is, on the contrary, about upstream connections:

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“Rewiring appears not to alter the structure of thalamo-cortical arbors” (p.328). Thedramatic adaptation of A1 to visual functions, despite the modest changes in its over-all thalamo-cortical connections, is strong support for a major role of the intracorticalplasticity that will be described in depth in §3.3. This is a perfect example of how dis-missing cortical adaptation only because there are no evident changes in connectivitymay cause mistaken neglect of plasticity at the local microcircuit level. The acquisi-tion of visual responses like orientation selectivity in A1 has been ascertained thanks toextremely complex methods. There is no reason to exclude functional changes down-stream to A1, even if empirical demonstrations are not easy at all to come by.

Let us suppose, however, that L&M are justified in assuming that downstream ar-eas are scarcely affected by changes in A1. This first argument is related to a secondone, according to which the fact that a primary sensory area is so malleable does notimply that higher cognitive processes cannot have innately dedicated brain areas. Asa matter of fact, both arguments depend on a common factor: the relative distancefrom the inputs and the consequent integrative function of the areas involved. It iswell established that cortical areas are progressively more integrative as a function oftheir distance from sensory inputs (and motor outputs). Thus, the (alleged) fact thatchanges in A1 due to abnormal inputs do not reflect in complete transformation of thefunction of downstream areas might very likely depend on their being more integrativeareas, which receive inputs from different sensory (and possibly motor) sources. Aswe will see, this is entirely consistent with the argument from connectivity proposedby Mahon (2015); Mahon and Caramazza (2011) according to which cortical areas arespecialized as a consequence of the patterns of inputs they are exposed to. If this isthe case, the function of (more) integrative areas might not be significantly modifiedby changing the modality of one of their input pathways because of the constraintsexercised by the overall pattern of connectivity, which is mostly unchanged. Such hy-pothesis might explain why areas located downstream of the primary auditory one areresistant to changes, and why higher cognitive processes might be little malleable aswell. But then, the point is not that the function of more integrative areas is innatelyfixed irrespective of which inputs they receive; the point is rather that, since that func-tion depends on a complex pattern of inputs, a much more complex input substitutionthan that involved in the ferrets example would be required for a change to obtain. Itis worth to add that integrative functions in higher cortical areas often rest on multipledifferent lower level sources, with a degree of redundancy, that allows partial func-tioning in case one of the sources becomes lesioned. It is the case, for example, ofdepth estimation, which relies on a range of visual features such as eye accomodationand convergence, binocular disparity, motion parallax, shading, just to mention a few(Palmer, 1999, pp.203–249).

A third objection raised by Pinker (2002) and echoed by L&M is that, even with re-gard to the auditory area directly affected by the rewiring, one is not forced to concludethat complete plasticity has been shown. An alternative possibility is that the changein function was made possible by general high-level likenesses between the computa-tions occurring in hearing and vision. In other words, the primary auditory area wouldnot be innately predisposed to process auditory (versus visual) inputs; it would be pre-disposed instead to process stimuli from any perceptual modality provided that thosestimuli have the right structure. For instance, L&M suggest, soundmakers with dif-

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ferent pitches might be treated like objects in different locations and sharp changes inpitch might be treated like motions in space. Given such similarities in structure, theyargue, what has been proved is not that the relevant cortical area can process any kindof inputs, but only that it can be tuned to different inputs within the constraints of itsinnate predispositions. In line with this suggestion, L&M appeal to the “metamodalhypothesis” put forth by Pascual-Leone and Hamilton (2001), according to which sen-sory areas are capable of processing sensory information from differing modalities,although there may be preferences for a given modality over the others. When thepreferred input is not available, then the brain may switch to the next best fit.

What this defensive argument presupposes is something like a fixed (or at leasta predetermined) structure inscribed in sensory areas, such that only patterns of datacompatible with that structure can establish in those areas and then change only thedetails. A first thing to note is that, in comparison with the “striking” (as L&M label it)evidence of plasticity, this is a rather vague speculation. There is presently no evidencethat plasticity is constrained in such a way: Pinker and L&M just invite us to considerthe possibility that a common fixed (or predetermined) structure exists, based on theobvious fact that commonalities in structure can always be found at a convenient levelof abstraction. This speculation is somehow given an empirical flavour by an appeal tothe metamodal hypothesis: but, as we show in §5.1, a closer look at the literature is farfrom encouraging for that line of argument, since there is no clear claim about whichcommon patterns are to be found, or even which sensory modalities are supposed tobe interchangeable. Moreover, as we will see in the next sections, the actual evidenceis far from supporting the underlying assumption. As a matter of fact, there is nosignificative evidence that cortical areas have a local unchangeable structure acting asa filter for specific categories of inputs; on the contrary, the general law of the cortexseems to be a substantial uniformity throughout any portion of it (see below, §4.2), withself-organization (§3.3) as the basic functional principle that exploits plasticity at theneural level and equipotentiality of cortical areas.

In sum, as far as examples of striking plasticity are concerned L&M propose twocounterarguments: first, the plasticity of cortical areas is constrained by their patterns ofconnectivity; second, although sensory areas can process inputs from different modal-ities they might have nevertheless an innately fixed underlying structure. As we willshow further, while the former argument is correct but of little help to strong nativists,the latter is not supported by current evidence.

2.2 Direct evidence for constrained plasticityThe modest purpose of this sub-section is to show that what L&M offer as direct evi-dence for constrained plasticity does not add much to their arguments against strikingplasticity.

A first line of argument concerns what they refer to as neural structural organi-zation. It is based on the case of mutant mice whose brains were unable to releaseany neurotransmitters and thus were wholly prevented from both synaptic transmissionand experience-driven neural development. In his study on these “knockout” mice,Verhage et al. (2000, p.866) concluded that, despite such dramatic impairment, “theirbrains were assembled correctly”. From this, L&M draw in turn the conclusion that

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many features of even the fine-grained structure of the brain can develop without anysensory input. However, the claim that the early assembly of the brain into macroscopicregions1is only marginally affected by sensory inputs is not particularly controversial(but see §4 for qualifications). As we said above, there is consensus that cortical areashave innate patterns of connections.

The second line of argument concerns the functional organization of the cortex withspecial regard to cases of crossmodal plasticity. As we said above, innate connectivityexplains how cortical areas acquire their functional specialization, due the nature of theinput conveyed by their connections. Thus, it is no surprise that, when cortical areasthat usually process sensory information from an impaired modality take input fromanother sensory modality, they change their function while areas located downstreamare little affected by this change. This may simply depend on the fact that the overallpattern of inputs for downstream areas is unchanged for the most part. However, allthe examples offered by L&M with regard to functional organization are based on thegeneral consideration that downstream components of the visual cortex and relatedbrain areas have the same functional specificity in the congenitally blind as in sightedindividuals.

In sum, the conclusion of this line of argument can be brought back to the previousclaim that the plasticity of any cortical area is constrained by its pattern of connectivity.Thus, in what follows we will focus in turn on this claim (let us call it the connectivitythesis) and on the other one proposed as a counterargument to striking plasticity, thatis, the claim that cortical areas may have a fixed, or predetermined, innate structure(the local structure thesis).

In the next two sections we will survey the most recent research on neural plasticity(§3) and cortical connectivity (§4). In section §5, on the basis of this survey, we willturn to a general discussion of the local structure and the connectivity theses.

3 Neural plasticityNeural plasticity is a key feature of the brain, expressing an astonishing power to forgethe primate neocortex, and that of humans in particular, thanks to a number of complexmechanisms, still largely unknown today. However, the impressive amount of studiesin the last half of a century have provided us with a partial picture, that we discussin this section, showing that the current state of the art on plasticity does not warrantclaims of concept nativism.

Neural plasticity comes in several different forms, and it has been investigated un-der a variety of perspectives. Therefore, it is useful to review some of those distinc-tions available in the literature, and to devise a classification of plasticity appropriatefor the purpose of picking up the accounts most relevant for crafting neural representa-tions, and to verify the degrees of flexibility and known constraints, for each different

1For a better idea of the scale level of brain areas reported as similar between mutant and control mice,in Fig. 3 (A and B) of the study of Verhage et al the marked areas are: the cortex (as a whole), the cerebellaranlage, the tectum, the lateral and medial ganglionic eminence, and the brainstem. A scale far larger thanthat relevant in the concept nativism discussion.

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account. For more general and comprehensive overviews see (Kolb, 1995; Bermudez-Rattoni, 2007).

The historical milestones of the term “plasticity” referred to the neural systems havebeen traced by Berlucchi and Buchtel (2009), giving credit to William James (1890)for its first introduction, followed shortly by Santiago Ramon y Cajal (1894).

With respect to the different perspectives under which neural plasticity has beenstudied, in the late 1960s neuroplasticity became an important domain of investigationfor the clinical implications of the changes in neurons of adult brains. Still today this isone of the prevalent perspective on neural plasticity (Møller, 2006; Lovden et al., 2010;Fuchs and Flugge, 2014). A different interest on plasticity arouse in the 1980s, withthe establishment of developmental neuroscience, and by 1981 a new section “Devel-opment and Plasticity” was introduced in the Society for Neuroscience (Mason, 2009).The focus was clearly on the role of plasticity in the ontogeny of the brain, and inthe early development after birth (Huttenlocher, 2002; Blumberg et al., 2010; Ruben-stein and Rakic, 2013a). During the last decades a further perspective on plasticity hasgrown, for its huge implications in the use of microelectronic devices in substitution ofsensory deprivations (Steeves and Harris, 2013; Sakurai, 2014), in cortical adaptationswhere the missing modality is conveyed by another sense (Proulx, 2010), or by thesame one (Fallon et al., 2009; Born et al., 2015).

What is shared by all those perspectives is the emphasis on plasticity as an exten-sive change in the state of the nervous system, especially in response to a drastic, andoccasional, event, like a brain injury. Even if the amount of studies produced underthose perspectives has been impressively useful for a general understanding of plas-ticity, with regard to the problem at hand the situation is radically different. Conceptformation is a daily business, that involves neural plasticity as the ordinary, continuousway of working of the brain, which undergoes constant changes triggered by the flowof events. There are indeed large streams of research focusing on the ordinary cogni-tive role of plasticity too, for example the studies concerned with plasticity in memoryformation (Squire and Kandel, 1999; Bermudez-Rattoni, 2007; Bontempi et al., 2007).

3.1 Taxonomies of plasticityLet us turn now on how “plasticity” has been defined and classified, in relation tothe central nervous system. An influential framework for the use of the term was es-tablished by Jacques Paillard (1976), and gained a renewed interest after the recenttranslation by Will et al. (2008). Paillard proposed four classes of changes in neuralorganization, that reflect at behavioral level, based on the abstract notions of a struc-ture S (the basic internal and sensorial connectivity), a function F , and an operatingtrajectory t (the dynamic processing of S leading to F). The function F for Paillard isgenerally the product of the system and requires some purpose in relation with its upperinterface. If the level of the system is the whole organism its interface is the externalenvironment, in all other cases it is the “supersystem” of which the system at stake is acomponent. Given a structure S that in stable condition produces a function F0 throughthe trajectory t0, the possible changes are as follows:

1. variabilite, when t0 is preserved, but the obtained function is F ′ ∈ [F0±φ ] with

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φ a small random perturbation;

2. flexibilite, when t0 is changed in tc but the final function F0 is preserved, thechange in operating trajectory can be a compensation to some error;

3. variations systematiques, when the final function F ′ = F0 + k is deviated fromthe original by a systematic bias k, like in the case when the system is not ableto cope with large errors in its operating trajectory;

4. plasticite, when a deviation from trajectory t0 into t1 leads to a novel function F1.

Paillard argued that only the last type of change should be properly named plasticity,and he added that such change should result from a modification of the connections, orfrom the substitution of the components of the structure. After having clarified whatkind of changes pertain to the definition of plasticity, Paillard went further to classifyplasticity according to when a change appears in time, establishing the following tax-onomy:

1. plasticite evolutive, involving structural mutations of the genome in achievingthe change;

2. plasticite genetique, concerning the structural malleability of the system duringits epigenesis;

3. plasticite adaptative, corresponding to the capacity of the fully developed systemto change its own structure and to expand its behavioral repertoire.

With regard to the conceptual architecture, obviously it is the first kind of plasticitythat would be the one privileged by concept nativists, but it has little or nothing to dowith “neural” plasticity, in that it does not directly involve any neural mechanisms. Onthe contrary, mechanisms pertaining to the neural functioning are in place primarily inthe last form of plasticity, and in part in the second form too.

Kolb and Gibb (2014) have recently confirmed a classification into three types, firstproposed by Greenough et al. (1987), on the basis of how experience is involved intriggering change:

1. experience-expectant plasticity, when a brain system requires a specific type ofexperience in order to develop, like the presence of both eyes in the developmentof ocular dominance columns;

2. experience-independent plasticity, when the development of a brain structure isonly partially specified genetically, but it nevertheless depends not on externalexperience but instead on the internal activities on axons of a starting roughstructure, like in the development of the lateral geniculate nucleus visual con-nections;

3. experience-dependent plasticity, when changes affect neuronal ensembles thatare already present in the brain, in response of experience.

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As we will repeat in §4, every brain development is the result of an interplay be-tween genetic specifications and internal/external experience, the taxonomy of Kolb& Gibb is based on the relative weights of the two factors, with genetic determi-nation dominating in experience-expectant plasticity, and playing a marginal role inexperience-dependent plasticity. Inside this taxonomy, experience-expectant plastic-ity is the closest to the notion of constrained plasticity in L&M, and in general to thenativist approach. In this perspective, when a new concept is learned, it happens thatexperiences pertaining to it trigger the development of a brain structure, already sup-posed to be in charge of representing the concept. In other words, without the expectedexperiences the concept will not have its neural representation, but once the experiencebecomes available, the brain changes that give rise to the representation are constrainedto take the same expected format. However, the only example of experience-expectantplasticity provided by Kolb and Gibb is the formation of alternate columns in the visualarea V1 on condition that inputs from both eyes are present. It should be emphasizedthat the specific content of those inputs is irrelevant for the structure of V1. One markof the “expectation” of the experience is the existence of a critical period, during whichit is necessary to be exposed to a specific type of experience for a neural structure to de-velop correctly. A critical - or sensitive - period (for a discussion on the difference seeColombo, 1982) has been suggested for language acquisition in humans, but languageclearly encompasses a number of different processes. Available cases of deprivationof exposure to language are, fortunately, extremely rare (Curtiss, 1977). In any case,the aspects of language possibly affected by a critical period of experience are onlyat phonetic and syntactic level, no influence is reported on vocabulary and semanticprocessing (Newport et al., 2001; Kuhl, 2010). Thus, (even conceding that such anec-dotal and indirect evidence is sufficient to show innate “expectation” for phonetics orsyntax, a thesis that is nowadays strongly debated) there is no evidence of cases ofexperience-expectant plasticity involving concept-specific predispositions anywhere inthe cortex.

The strict requirements imposed by Paillard to the concept of plasticity had themerit to prevent a loose usage of the term, that was a source of confusion, althoughthe restriction to changes in connectivity or in individual components was ruling outfundamental forms of plasticity, that were just emerging at the time of his writings, likethe long lasting potentiation of synaptic transmission (LTP) (Bliss and Lømo, 1973).What is still alive today of Paillard’s arguments is the distinction of plasticity dependingon whether it involves structural changes or not, with both kinds deserving the statusof neural plasticity (May, 2011):

1. functional plasticity, when experience produces lasting changes in patterns ofneural activities in a brain structure, without observable changes in connectivity;

2. structural plasticity, when changes produced by experience are related with rewiringof neural connectivity.

This nomenclature is far from being uniform in the literature, for example sometimesfunctional plasticity is used to mean the functional relevance of a plasticity which isstructural in kind (Lledo et al., 2006), conversely structural plasticity has been used toaddress synaptic modifications responsible for functional plasticity, in the sense above

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defined (Schuster et al., 1996). Strictly speaking no change is possible in the responsesof neural circuits which does not correspond to some organic modification. In fact,as pointed out by Holtmaat and Svoboda (2009), in between changes at the level ofsynaptic strength only, and at the level of neural connection rearrangement, there is acontinuum of mechanisms such as appearing and disappearing of boutons and dendriticspines.

We think a better taxonomy of plasticity could be a mechanistic one, based onwhich organic components become involved in functional changes, at different levels.One account of plasticity approaching these criteria can be found in the landmark paperof Buonomano and Merzenich (1998), who consider three levels:

1. synaptic plasticity, addressing changes at single synapse level;

2. cellular conditioning, addressing changes at single neuron level;

3. representational plasticity, addressing changes in distributed neural responsesparticipating to specific domains of representations.

Their taxonomy was related to the different methodologies of analysis, for examplesynaptic plasticity studies were generally conducted in slice preparations, while exper-iments on representational plasticity were conducted inducing peripheral dennervationor intensive behavioral training. We propose here a modified taxonomy, tuned to ourpurposes of discussing plasticity in concepts formation:

1. synaptic plasticity, addressing changes at single synapse level;

2. intra-map plasticity, addressing internal changes at the level of a single corticalmap;

3. inter-map and extracortical plasticity, addressing changes on a scale larger thana single cortical map.

In the next part of this section we will discuss in detail synaptic plasticity, and intra-map plasticity. An operational definition of cortical map together with a rationale forsingling a class of intra-map plasticity will be given shortly. Inter-map and extracorticalplasticity is closely related with connectivity, therefore it will be dealt with in §4, whereits role in the formation of concepts will be discussed.

3.2 Synaptic plasticitySynaptic plasticity is at the core of the computational power of every neural circuit.The capacity of a single neuron to modulate its output in response to the graduation ofinputs at its dendrites is rather limited. Within a short time sampling, the only possi-bility for the neuron is to emit a single action potential, which cannot be modulated inamplitude, or not. Given a large enough time window the response of the neuron canbe graduated only through spike frequency. The synapse is the site where it is possibleto greatly refine the modulation of response signals, since the same action potential atthe presynaptic side can induce a postsynaptic depolarization graduated by two ordersof magnitude, depending on the synaptic efficiency, which in turn depends on several

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physiological factors, like the number of synaptic vesicles ready for release and theirprobability to undergo exocytosis (Katz, 1971). This fine-grained modulatory capacitywould be worthless without the possibility of tuning it purposefully. From a mathe-matical point of view, ideally, sectioning a network of neurons in the brain, isolatingits input and output connections, would produce a huge number of possible transferfunctions between inputs and outputs, thanks to the degrees of freedom of the synapticstrengths between all internal neural connections. Synaptic plasticity is the way of se-lecting one among all the possible functions, in reaction to the experienced patterns ofactivity.

How the history of input patterns functionally relates to current neural function,is still far from being comprehensively understood, and extremely hard to investigate.A well known intuition, long before the possibility of empirical investigations, is dueto Donald Hebb (1949), predicting an increase in the synaptic efficiency followingrepeated coincidences in the timing of the activation of both presynaptic and postsy-naptic neurons. The most studied physiological model of plasticity resembling Hebb’slaw is long-term potentiation (LTP) (Bliss and Lømo, 1973; Artola and Singer, 1987;Bliss and Collingridge, 1993; Bear and Kirkwood, 1993). The best known molecularmechanism for LTP relies upon N-methyl-D-aspartate (NMDA) receptors.

Hebb was right, but he envisaged just one among the many ways of changing synap-tic strengths. Long-term depression (LTD) is the converse process to LTP and resultsin a long lasting decrease in synaptic efficacy. The main cause of synaptic weakeningis deprivation of presynaptic or postsynaptic activities (Ito, 1989; Zhuo and Hawkins,1995). LTD has been also observed in synapses with NMDA receptors (Crozier et al.,2007). While most research on LTP concerns excitatory synapses, it has been recentlydemonstrated in cortical GABAergic inhibitory neurons too (Hensch, 2005).

LTP and LTD are far from encompassing the full range of plasticity at synapticlevel. An intriguing more complex form of plasticity, known as spike-timing-dependentplasticity (STDP) induces synaptic potentiation when action potentials occurs in thepresynaptic cell a few milliseconds before those in the postsynaptic site, whereas theopposite temporal order results in long-term depression (Levy and Steward, 1983;Markram et al., 1997; Feldman, 2000). Recent studies (Paille et al., 2013) suggest thatGABAergic circuits might govern the polarity of STDP, operating as a Hebbian/anti-Hebbian switch. As speculated by Markram et al. (2011), STDP is the mechanism ofplasticity that better explains the capacity of the mind to establish causal relationshipsbetween events in the outside world. It complements Hebb’s law, by ruling out apparentassociations of coincidental events, if the event supposed to be the cause did not occurin advance. During the last two decades STDP has been demonstrated in many differ-ent types of synapses, in several animals, and from striatum to cortex, recent reviewsin (Feldman, 2012; Markram et al., 2012).

A young but important field of research in synaptic plasticity concerns constraintsthat the brain apply to synaptic modifications. LTP, LTD, and STDP, can alter a neu-ron’s input dramatically, pushing its behavior into an unstable regime. The mecha-nisms by which risks like this are prevented, and the proper balance between excitationand inhibition in neural circuits is maintained, are collected under the name of homeo-static plasticity (Turrigiano and Nelson, 2004; Watt and Desai, 2010; Turrigiano, 2011).Homeostasis is the result of an overlap of several independent mechanisms. Synaptic

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scaling is the process in which neurons monitor changes in their own main activity,and uniformly increase or decrease their postsynaptic strengths, without changing theirrelative weights. Thus, the overall computational function, that relies on differences insynaptic strengths, is not disrupted. A different way of regulating the overall activity isby modulating the intrinsic excitability of a neuron, without affecting the postsynapticstrengths.

3.3 Intra-map plasticityThe mechanisms of synaptic changes discussed above are ubiquitous in the brain, andin animals ranging from insects to mammals. At a higher level, turning to analyze howthe effects of synaptic plasticity combine at the level of neural circuits, we restrict ourdiscussion to the cortex, for two reasons. First, it is well acknowledged that conceptsare represented mainly inside cortical circuits. Second, it is the most investigated partof the brain, from several points of view, plasticity included.

The plasticity we are referring to here is the modification of a single cortical cir-cuit, by means of synaptic dynamics, mainly due to changes in efficiency. We useloosely the term “map” to identify a portion of the cortex whose neurons cooperate toa specific function. We borrow the concept from Mountcastle (1957), as a portion ofthe somatosensory cortex where along the 2-dimensional surface the firing of neuronssignals the occurrence of a stimulus on the same sensorial area, and he speculated thatit might be a architectural principle of the cortex, confirmed shortly after by Hubel andWiesel (1959). This notion of “cortical maps” has the merit of providing the empiricalcriteria for identifying a portion of the cortex, functionally unified as a specific neu-ral circuit and consistently responding to contiguous sensorial stimulation. Of coursethe same criteria of contiguity cannot apply to non sensorial cortical areas. In generalwe can say that a “map” in the cortex is identified by lawful topological relation be-tween the surface of cortex and a relevant aspect of the representational structure. Inthe case of higher cortical areas the represented domain has a complicated topologi-cal structure that is often hard to relate with the simple two-dimensional structure ofthe cortical map. We will say more about the parceling of the cortex in §4, here wecontent ourselves with loosely using “map” to the only purpose of addressing portionsof the cortex where plasticity operates on a local scale. Even within this modest pur-pose, assuming a specific “map” scale for plasticity would be problematic, if corticalconnectivity spanned over a continuum between distant areas and the space betweenneighbor cells. However, there is a consistent literature showing that horizontal axonalprojections, supported mainly by pyramidal cells, together with other no-pyramidal,like basket cells, cluster on a typical range, between 200µm and 1.0 mm (see reviewand citations in Nieuwenhuys et al., 2008, ch.5), a scale of two orders of magnitudebelow that of inter-map and extracortical connections. In the horizontal axonal lengthdistribution the upper tail from the main cluster may well reach higher values, up to 3.0mm, typically in the intrinsic patchy connections within sensorial areas (Gilbert andWiesel, 1983, 1989; Aronoff et al., 2010). In the cat, the distribution of orientation-specific excitatory lateral connections peaks at 100µm with a tail up to 1.3 mm in area17, and peaks at 200µm with a tail up to 1.8 mm in area 18 (Kisvarday et al., 1997,Fig.9-10). These distributions are consistent with the increase of integration in upper

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visual areas, and in visual area V5 of rhesus monkeys horizontal connections up to10 mm have been found (Ahmed et al., 2011). As we will see, these longer lateralconnections seem to undergo intra-map plasticity as well.

Obviously plasticity in maps of the sensorial cortex is easier to observe, and thefirst empirical evidence of intra-map plasticity was gathered from somatosensory andvisual maps. A well known behavioral modification related to the continuous plasticityof sensorial cortex is perceptual learning (Fahle and Poggio, 2002). It is the long-term enhanced performance on a perceptual task, as result of repeated experiences.By training monkeys on a tactile task in which adjacent digits received simultaneousstimulation, Wang et al. (1995) found that in the primary somatosensory cortex (S1)several neurons developed receptive fields integrating those digits only. In the case ofvision, it has been observed that perceptual learning is correlated with plasticity (atleast) in areas V1 and V4. Perceptual learning is studied in experiments where subjectsare rewarded for their improvements in perception, since synaptic plasticity appearsto be mostly controlled by global neuromodulatory systems, so that only the featuresthat are important to the perceptual task are learned (Roelfsema et al., 2009; Sasakiet al., 2010). A form of perceptual learning observed in primary visual cortex, whichis independent of rewards, is the so-called stimulus-selective response potentiation,that results in increases in the response of V1 to a specific visual stimulus throughrepeated viewing, and is, at least in rodents, supported by NMDA receptors (Cookeand Bear, 2013). Hebbian plasticity supported by NMDA receptors has been recentlydemonstrated in primary visual cortex of primates too (Huang et al., 2014). In theprimary auditory cortex (A1) of gerbils, Ohl et al. (2001) observed changes in stimulusrepresentation after learning two categories of linearly rising and falling frequency-modulated tones.

Perceptual learning is dominant for odors too, even if this sensory modality lacks aprimary cortical area. The old view, still held by Pinker (2002; see below §5), of odorcoding as hardwired by molecular selectivity of olfactory receptor neurons, is at oddswith evidence of higher-order cognitive processes for odor quality coding and catego-rization, taking place in the posterior piriform and in the orbitofrontal cortex (Howardet al., 2009; Su et al., 2009; Gottfried, 2010). Using fMRI in an olfactory paradigmof perceptual learning in human subjects, Li et al. (2006) found that prolonged expo-sure to a target odorant enhanced perceptual differentiation for odorants in both qualityand categorization, reflected in increased responses in piriform cortex and orbitofrontalcortex.

Conversely to perceptual learning, intra-map plasticity is also driven by sensorialdeprivation. We will discuss in §4 reorganizations at larger scale deriving from com-plete loss of a sensorial modality. A well explored paradigm for inducing modest sen-sorial deprivation is by trimming or plucking a subset of whiskers in rodents. This isa fundamental source of sensory information, represented in S1 by cell clusters, calledbarrels. Intracortical map plasticity produces a depression of responses to deprivedwhiskers and potentiation of responses to spared whiskers, and is supported by NMDAreceptors (Fox, 2002).

Several recent studies have addressed the overall cortical modifications resultingfrom intra-map plasticity, encompassing changes beyond synaptic strengths, like form-ing or breaking of synapses, growth and re-routing of axonal branches. The overall

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picture is that experience affects the cortex in all these forms, driven by mechanismslike those seen in §3.2, at a typical scale of 200÷300µm, and a temporal scale of daysor weeks (Stettler et al., 2006; Butz et al., 2009; Marik et al., 2010; Caroni et al., 2012;Cheetham et al., 2014).

Less data are available concerning the development of long-ranging horizontal con-nections. Lateral connections subserving ocular dominance in the primary visual cor-tex of the cat exhibits rapid plasticity in a critical period of early postnatal development(Trachtenberg and Stryker, 2001). Conversely, in human visual cortex long-range hori-zontal connections develop later and slower than intracolumnar connections (Burkhal-ter et al., 1993). This fact is in agreement with functional evidence of the late matura-tion in humans of visual abilities subserved by long-range connections, such as spatialintegration (Kovacs et al., 1999), and sensitivity to contextual interactions of orientedstimuli (Hou et al., 2003).

Given the abundance of plasticity mechanisms, and their complex interaction at thelevel of cortical circuits, the current picture is scattered, with much still to be discov-ered. However, the amount of evidence available today suggests that many similar-ities exist across cortical maps in expressing plasticity. As summarized by Feldman(2009, p.34) “Experience- and training-induced cortical plasticity occur with commonfunctional components in S1, V1, and A1, which may reflect common cellular mecha-nisms.”

Several theoretical models have been proposed to account for how the basic mecha-nisms of synaptic plasticity interacts at the level of cortical maps, thus producing the va-rieties of response functions, some of which have just been listed. One of the first, andmost influential, was based on the mathematical framework of self-organization. Theterm was introduced by Ashby (1947) in cybernetics, and later formalized by Haken(1978), with the aim of providing a unified mathematical quantitative description ofa range of phenomena occurring in physics, chemistry, and biology, in cases wherea global ordering emerges from complex local interactions. The first attempts to usethe mathematical framework of self-organization for describing neural phenomena areattributed to von der Malsburg (1973); Willshaw and von der Malsburg (1976), who ad-dressed the organization of maps in the visual cortex. There are three key mechanismsin cortical circuits that match with the premises of self-organization:

1. small signal fluctuations might be amplified, this is a direct effect of the non-linear behavior of neurons;

2. there is cooperation between fluctuations, in that excitatory lateral connectionstend to favor the firing of other connected neurons, and LTP reinforces synapsesof neurons that fire frequently in synchrony;

3. there is competition as well, in that inhibitory connections can lower the firingrate of groups of cells at the periphery of a dominant active group, and synaptichomeostasis compensates for the gain in contribution from more active cells, bylowering the synaptic efficiency of other afferent cells.

In the cortical model devised by von der Malsburg the activity xi of each neuron i was

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computed by the following system of differential equations:

∂

∂ txi(t) = −αixi(t)+ ∑

j∈Ci

wi j f (x j(t))+ ∑j∈Ai

wi ja j(t) (1)

f (xi(t)) =

{xi(t)−θi if xi(t)> θi

0 otherwise(2)

where Ci is the set of cortical neurons with lateral connections to the cell i, and Aiis the set of all afferent axons, each carrying a signal a(t). wi j are the synaptic effi-ciency between cell presynaptic j and postsynaptic i, and are modified by an amountproportional to the presynaptic and postsynaptic signals, in the case of coincidencesof activity. Periodically all wi j leading to the same cortical cell i are renormalized,resulting in competition, in that some synapses are increased at the expense of others.

The source of afferents, in such process of self-organization, can be the externalscene seen by the eyes, but also spontaneous activity generated by the brain itself (Mas-tronarde, 1983). Equations like those in (1), explain different kinds of organization inthe visual system ranging from retinotopy, ocular dominance, to orientation sensitivity(von der Malsburg, 1995).

More recently, a simpler formulation has been proposed (Miikkulainen et al., 2005),that takes into account the following key features of cortical circuits:

1. the intercortical connections of inhibitory and excitatory types;

2. the afferent connections, of thalamic nature, or incoming from lower corticalareas;

3. the organization on two dimensions of neural coding;

4. the reinforcement of synaptic efficiency by Hebbian learning;

5. homeostatic compensation of neural excitability.

This theoretical formulation has demonstrated the possibility of LTP and home-ostasis in the formation of orientation, ocular dominance, and direction preferences inthe primary visual cortex (Bednar and Miikkulainen, 2006; Stevens et al., 2013), thedevelopment of whisker direction maps in rat barrel cortex (Wilson et al., 2010), andthe formation of selectivity to angles in visual area V2 (Plebe, 2007, 2012).

Several different theoretical models on how the cortex can develop functions usingthe basic synaptic plasticity mechanisms have been proposed (Eliasmith and Anderson,2003; Deco and Rolls, 2004; Ursino and La Cara, 2004). All these simulations, likethose previously mentioned based on self-organization, are neurocomputational model,and as such are necessarily crude simplifications with respect to the extreme complexityof intra-map plasticity, and certainly cannot be taken as definitive answer to the issuesat stake. However, the models above referenced are a small selection of neurocom-putational model of cortical development, based on the model-mechanism-mappingconstraint, introduced by Kaplan (2011); Kaplan and Craver (2011), for ascertainingwhich models give explanation of the modeled neural system. This constraint is met

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when the variables in the model correspond to identifiable components, activities, andorganizational features of the target mechanism that produces the phenomenon, and de-pendencies posited among these variables in the model correspond to causal relationsamong the components of the target mechanism. Under these assumptions, computa-tional models are not just mere descriptions of the phenomena, but gain a degree ofexplanatory power (Piccinini, 2007), and, for the case of intra-map plasticity, the listedmodels provide a support for the fact that the underlying principles at synaptic levelwould suffice in forming fundamental functions at the level of cortical maps.

4 Cortical connectivityThe overall connectivity of the brain, cortex included, is clearly a necessary prereq-uisite for its possibility to work, and therefore to have concepts. On the other hand,the basic development of connectivity takes place early, before the period infants ac-quire most of their concept, when mechanisms of ordinary plasticity, seen in the pre-vious section, become dominant. The main realization to emerge from the tremendousprogress in developmental neurobiology over the past three decades (Blumberg et al.,2010; Braddick et al., 2011; Rubenstein and Rakic, 2013a,b), is that mature brain con-nectivity results from a deep interaction between genetic factors and the experience ofthe individuals. This currently shared view is well summarized by Stiles (2011, p.20):“at least for the development of the brain, attempts to categorize neurodevelopmen-tal events as the product of nature or nurture cannot succeed because the fundamentalprocesses of brain development at every level require the interaction of nature and nur-ture”. Although the molecular cues derived from gene expression, and all other factorsincluding environmental experiences, always work in concert to guide neural connec-tivity, the relevance of their respective roles varies largely across time and locations ofthe brain development. For example, in the organization of the midbrain the role of adetailed series of gene expression is dominant (Nakamura, 2013).

The cortex is the part of the brain where genetic determination seems less domi-nant. With the exception of H-2Z1 transgene, which marks the granular parts of mouseS1 (Cohen-Tannoudji et al., 1994), no cortical area-specific genes have been found.Instead, cortical areas are driven by the expression of a unique subset of genes, eachof which is also expressed in other areas. Moreover, each layer of the cortex has itsown unique profile of gene expression. An intriguing fact is that the sharp boundariesin anatomical and functional characteristics across cortical areas do not correspond toany sharp transition in the graded expressions of the transcription factors in the progen-itors zones, either the cortical ventricular zone, the subventricular zone, or the corticalplate (O’Leary et al., 2007, 2013). This graded expression of transcription factorstransduces positional information during the early generation of cortical proto-areas,which progressively attract afferents from the appropriate thalamic nuclei, a first stepin the subsequent creation of processing cortical circuits (Sur and Rubenstein, 2005;Rakica et al., 2009). In one of the most detailed genetic study of human brains, Hawry-lycz et al. (2012) analyzed expressions of about 30,000 genes on 900 brain sites in twoindividuals. A first remarkable result is the high homogeneity across the entire cortex,in contrast to sharp and complex differential relationships between extracortical brain

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areas. Noteworthy is the absolute lack of hemispheric differences in transcript distribu-tions, despite the well known lateralization of cortical functions. Distinctive exceptionto this genetic uniformity of the cortex is the visual area V1, and distinctive featuresare also identified for the primary sensorimotor cortex and the temporal pole.

The basic plan of four primary areas in the cortex: V1 (visual), A1 (auditory), S1(somatosensory), M1 (motor), is found in almost all mammals, with rare modificationsof its general geometrical scheme, and its connections with corresponding four nucleiin the thalamus (Krubitzer, 1995; Krubitzer and Kaas, 2005; Alfano and Studer, 2013).Still within the four primary areas the development is influenced by individual experi-ences, in a way that largely affects the adult areas. In humans, the visual area V1 canchange in size 3-fold within the normal population (Dougherty et al., 2003).

The arealization beyond the primary areas is highly differentiated among mammals,and in humans, like in most primates, higher-order areas undergo extended periods ofpostnatal maturation, therefore are much more influenced by experience. In a longi-tudinal study Gogtay et al. (2004) measured cortical gray matter in thirteen childrenbetween the age of 4–21 years. They found that higher-order cortices mature only aftersomatosensory and visual areas, and the phylogenetically oldest cortical regions, likethe piriform and the entorhinal cortex, mature early.

In the visual cortex of marmoset monkeys Bourne and Rosa (2006) found that areashigher than V1 develop later, with the sole exception of the middle temporal area MT,which is also a phylogenetically old area, found in all primates, discovered and studiedfor decades by Zeki (1974, 2015). In humans the homologous of MT is area V5, andits old phylogenetic origin is probably related to its early development, and a stronggenetic determination of its connectivity. In infants, when higher visual areas are notyet developed, global motion responses arise from area V5, while at later age it isdominated by more medial areas such as V3/V3A and V6 (Wattam-Bell et al., 2010).In blinds area V5 preserves the motion discrimination function, using sounds (Bednyet al., 2010).

In sum, the way environmental factors interact with genetic cues in building cor-tical connections clearly involves neural plasticity. Further aspects relevant for thediscussion on concept ontogeny will be reviewed below.

4.1 Neural activity and cortex developmentThe plasticity named experience-independent by Kolb and Gibb (2014) (see §3.1) playsan important role in the prenatal formation of cortical connections, and is in fact basedon experiences too, but of a peculiar kind: electrical patterns generated by spontaneousneural activity. In the fetal brain a series of electrical patterns have been observed,with delta waves from 0.3 to 2 Hz dominating at mid-gestation, followed by delta-brush during the second half of gestation, 8-25 Hz spindle-like activity that may last upto 10 seconds, a remarkably similar set of waves had been found in neonatal rodentstoo (Khazipov and Buzsaki, 2010; Khazipov et al., 2013). Signals to the somatosensorycortex are probably triggered by sensory feedback initiated by spontaneous movements,while those to the visual cortex are generated in the network of retinal ganglion andamacrine cells (Meister et al., 1991), and those to the auditory cortex are released by

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the Kolliker’s organ, a transient structure of the developing cochlea (Tritsch et al.,2007).

First insights into the role of intrinsic neural activity come from the study of oculardominance, the segregation of thalamic projections by eye within V1, into a series ofalternating stripes. The classic experiments of Hubel and Wiesel (1963); Wiesel andHubel (1965), demonstrating the role of visual experience in the formation of oculardominance, were replicated in the period before eye opening, showing how internalactivity has a vicarious role in the early organization of V1 (Katz and Shatz, 1996).Today it seems increasingly apparent that in the development of most visual circuitsexternal sensations are first substituted by intrinsic neural activity (Ackman and Crair,2014). Spontaneous retinal waves may explain, for example, findings by Ko et al.(2014) of neighboring neurons in the primary visual cortex of dark-reared mice, morelikely to be connected to one another if responding preferentially to same orientedstimuli. A first theoretical question concerning the function of spontaneous neuralactivity is whether it plays a permissive role, simply enabling other developmental cues,or an instructive role, guiding directly the arrangement of connections (Crair, 1999).Several studies where the specific effect of the “content” of neuronal activity has beenevaluated point to the latter answer. In order to unambiguously distinguish whetherretinal activity acts in a passive way to promote cell survival and neurite outgrowth invisual maps, or in an instructive way through specific spatiotemporal patterns, Xu et al.(2011) used genetic manipulations of spontaneous retinal waves in mice. As a result, awide range of spontaneous retinal activity with same firing levels of the natural waves,but different spatiotemporal patterns, were not able to produce normal circuits. Using asimilar technique, Zhang et al. (2011) found that forcing spontaneous retinal waves tobe synchronous in the two eyes, ocular dominance was prevented, and even retinotopywas also dramatically perturbed.

More problematic is to give a mechanistic explanation of the effects of neural activ-ity on the cortex development, what is sure is that it can’t escape the general rule statedat the beginning of this section: neural activity does not play in isolation, but in con-cert with molecular cues derived from gene expression (Crowley and Katz, 2002), andthe mechanisms of this interaction are not yet understood. More precisely, the obscureside is how neural activity interacts in structural developmental steps like cell migra-tion, axonal path finding, and the establishment of new synaptic connections. Severaltheoretical proposals, reviewed in (van Ooyen, 2001), point to the sort of competitivemechanisms already encountered in self-organization of cortical maps. On the otherhand, there are clear evidence that neural activity, both as internally generated, and asexperience-dependent shortly after birth, involves some of the ubiquitous features ofsynaptic plasticity seen in §3.2.

Spike-timing-dependent plasticity has been found to play a relevant role in circuitdevelopment in the period across late prenatal spontaneous neural activity, and earlyexternal experiences, at least in three areas: in the development of visual responses ofXenopus laevis, in the formation of retinotopic and ocular dominance maps in mam-malian V1, and postnatal development of rodent S1 cortex, a review in (Shulz and Feld-man, 2013). Neural activity has also a crucial role in the development of GABAergicinterneurons in the cortex. An unexpected and intriguing fact is that in the develop-ing cortex, GABA-releasing neurons are excitatory, as a result of a high intracellular

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concentration of Cl−, but activity progressively triggers a chloride-extruding system,and GABA begins to exert its conventional inhibitory action (Ben-Ari, 2002). Dur-ing a restricted time window in their excitatory stage, GABAergic neurons themselvesbecome generators of patterned activity, producing a waveform called giant depolariz-ing potential (GDP). This cascade of behaviors is instructive in sculpting the corticalcircuitry at small scale, avoiding the dangerous instability of contemporary excitatoryand inhibitory cells under development (Ben-Ari et al., 2007). Synaptic scaling, seenin §3.2, has also been demonstrated to be induced by activity in the early developmentof rodent visual cortex (Desai et al., 2002), and acts as an early counterbalance to theoverall increase in mean activity in the developing cortical circuits.

There is a different, more drastic, reorganization in the direction of reducing cor-tical connectivity during development, by a process of programmed cell death (PCD).It is the metabolically active, biochemically regulated loss of cells, at various stagesof the development, in many invertebrate and vertebrate species. There are severaldistinct types of programmed cell death, that subserve a number of functions, fromsexual dimorphism to removal of cells and tissues that serve a transient physiologicalor behavioral function, a recent review in (Oppenheim et al., 2010). The function ofinterest in cortical connectivity is the activity-regulated survival of subpopulations ofcells, selected by their sustained involvement in the responses of the developing cir-cuit to internal or external experiences. Direct evidence of this role for programmedcell death ranges from the increase of death in the rat olfactory bulb by loss of olfac-tory input (Zou et al., 2004) to the neuron survival in the avian auditory nuclei (Harrisand Rubel, 2006). One of the most striking example of the necessity of neural activ-ity for a selective survival during PCD, is the observation that in mutant mice lackingneurotransmitter secretion, there is a massive and rapid PCD of virtually all neurons(Verhage et al., 2000). An indirect evidence of activity-dependent PCD in humans wasfound by Homae et al. (2010). They monitored connectivity changes from the neonatalperiod to the age of 6 months, by examining the patterns of spontaneous fluctuationsin brain activity at resting state. Homologous regions across the two hemispheres, andwithin hemispheres, increase their connectivity in the temporal, parietal, and occipitalregions, while the opposite happens in the frontal regions. Frontoposterior connectiv-ity decreases from the neonatal period to the age of 3 months and increases from theage of 3 months to the age of 6 months. As discussed by Homae et al the increases inconnections are likely the result of activity driven development, while the decreases incorrelations reflect a form of pruning, like programmed cell death.

4.2 Is the cortex uniform?One of the aspects of the cortex often reported as outstanding is its amazing unifor-mity, compared with its huge extension, and the range of different functions carried outby its areas. The invariance of its basic microstructure was already observed and de-scribed by its first investigators (Ramon y Cajal, 1906; Brodmann, 1909). It has beingrepeatedly mentioned over the years as one of its most remarkable feature, like in thefollowing citations: “The mammalian cerebral neocortex can learn to perform a widevariety of tasks, yet its structure is strikingly uniform” (Marr, 1970, p.163); “Neurobio-logical studies have shown that cortical circuits have a distinctive modular and laminar

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structure, with stereotypical connections between neurons that are repeated throughoutmany cortical areas” (Haeusler et al., 2009, p.73); “The neocortex is the brain structuremost commonly believed to give us our unique cognitive abilities. Yet the cellular orga-nization of the neocortex is broadly similar not only between species but also betweencortical areas” (Harris and Shepherd, 2015, p.170). However, this common assump-tion of cortical uniformity has been also brought into question, for instance Marcuset al. (2014, p.551) ask “What would it mean for the cortex to be diverse rather thanuniform?”. We take now a closer look at this issue. Let first note that the claim forthe uniformity of cortical structure is intrinsically problematic, because asserts a qual-itative property without any explicit metric by which the claim can be evaluated. Ofcourse the cortex is not uniform down to the molecular level like a glass layer, but itsuniformity may still be judged remarkably in comparison to other areas of the nervoussystem, such as the diverse nuclei in the brainstem. We will now evaluate the unifor-mity of specific spatial dimensions and scales separately, comparing radial variationwith intralaminar variation.

First observed by Berlin (1858), the regular repetition of the radial profile of thecortex is its best investigated kind of uniformity. Cytoarchitectonic and myeloarchitec-tonics methods allowed early neuroscientists to reveal the delineation of the six corticallayers (Ramon y Cajal, 1906; Brodmann, 1909; Vogt and Vogt, 1919; von Economo andKoskinas, 1925), later confirmed by pigmentoarchitectonics (Braak, 1974). The mainvariation is found in the fourth layer, because the population of spiny stellate cells, fedprimarily by thalamic fibers, is abundant in all sensorial areas, while less so in motorareas. At a finer level, large pyramidal cells are more abounding in layer III in theinferofrontal area, while in layer V in the somatomotor cortex, and less common inareas like the retrosplenial lateral region. Despite these local variations, the basic lay-ered structure is preserved. The different extent of layers IV and V has been used byvon Economo and Koskinas (1925) for a broad classification of the cortex into granu-lar, typical of sensorial areas, and agranular, such as the motor areas. During the lastdecades few other systematic variations in the laminar structure have been observed.Limbic areas, located at the edges of the cortex as a ring above the corpus callosum,either lack layer 4 or have a rudimentary layer 4 and are poorly myelinated. There isa progressive transition from limbic adjacent areas with well developed layer 4, andBarbas (2015) introduced new distinctions in this progression, from agranular to dys-granular, progressing to eulaminate I and eulaminate II, the full developed laminarstructure.

The first attempt to assess intralaminar uniformity of the cortex is due to Rockelet al. (1980), who counted about 110 neurons in sections of 30µm diameter of cortex,either in motor, somatosensorial, frontal, parietal, or temporal areas, across animalssuch as mice, cats, monkeys, and humans. The only exception is always to be foundin the primary visual cortex, with a count of about 270 neurons. Later on, doubts wereraised concerning whether their stereological methods were technically flawed (Rakic,2008). Since 1980, methods for quantitative studies of cell number improved greatly,and spatial resolution of current stereology extends to submicroscopic scales. An alter-native method has been introduced, isotropic fractionator (Herculano-Houzel and Lent,2005), which is much quicker and therefore allows measurements on larger popula-tions. Using this method a large dataset of neuron counts across 29 mammalian species,

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for the cortex, the cerebellum, and the rest of brain, has been generated (Herculano-Houzel et al., 2015). At the finer scale of cortical areas, Herculano-Houzel et al. (2008)reported results quite different from Rockel et al., with twofold or even threefold vari-ation in neural density across cortical regions. Despite the lower spatial resolution, inprinciple the isotropic fractionator method should offer an accuracy similar to that ofstereology, however there might be discrepancies for the difficulty in identifying aver-age neuronal surface densities in areas where the thicknesses or volume densities vary(Srinivasan et al., 2015). In a replication of the direct count performed by Rockel et al.using state-of-the-art high resolution stereology, Carlo and Stevens (2013) confirmedthe same uniformity of count for the same species and cortical areas. The absolutenumber of neurons they found is 14% less than the number reported in the 1980 study,but the constancy of counts across the cortex was confirmed. It is interesting to ob-serve that models fitting data from isotropic fractionator, even if depicting a cortex lessuniform than predicted in Carlo and Stevens (2013), confirm a systematic variation ona single brain axis, aligned primarily on the caudal-rostral direction, and slightly frommedial to lateral, with more neurons in the primary sensorial areas compared to motorand frontal areas (Cahalane et al., 2012; Charvet et al., 2015).

One may argue that the evidence for cortex uniformity above reviewed is purely interms of similar quantitative structure, and may not be so relevant in terms of its neuron-level behavior. We will now review two different perspectives, from neuroanatomicstructural statistics, and from computational considerations.

One of the most investigated neuroanatomical features of cortical neurons is thenumber or spines in the dendritic trees of pyramidal cells. It appears to increase con-siderably from sensorial areas to prefrontal cortex, and this change is more markedin humans than in other primates (Elston, 2003; Elston et al., 2011). This data is inagreement with the well known protracted development of the prefrontal cortex, whichis traceable postnatally well into the third decade (Gogtay et al., 2004; Shaw et al.,2008), and is unparalleled in comparison with other primates. The unique growth ofdendritic structure in pyramidal cells of the prefrontal cortex, the core of our conceptualsystem, bestows a powerful potential for plastic changes on them. Karbowski (2014)reviewed and compared a number of other neuroanatomical features of the cortex, inmammals ranging from human to dolphin, and again found a remarkable invarianceacross species and across regions. In humans the length of postsynaptic density is of0.38µm with a standard deviation of only 0.04µm, and the synaptic density has a meanof 5×1011cm−3 with standard deviation 0.3. Note that this last figure does not contrastwith data from Elston and collaborators, since the higher number or spines of pyrami-dal cells in the prefrontal cortex is counterbalanced by the reduced neural density inthis area. The ratio of excitatory to inhibitory synapses is highly invariant even acrossspecies, with average 0.83 and standard deviation 0.03.

A long-standing research has attempted to relate the amazing similarities in neocor-tical circuit organization, with its computational “core” properties. The first attempt inthis direction was made by Marr (1970), who proposed a “fundamental neural model”of cortical microcircuits. His approach was to start from a mathematical formulation ofthe general problem of classifying input signals, to develop an equivalent representa-tion using neuron-like units, and then to fit the sketched model with the variety of cellsin a cortical column. This attempt was both too ambitious and too far from empirical

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reality, and as a result was almost totally neglected. Nearly two decades later Shep-herd (1988) proposed a model, which was at the same time much simpler but moreclosely related to the physiology of the cortex, based on an abstraction of the corticalpyramidal neuron as an integrator of all excitatory inputs at the spines of its dendriticbranches, further modulated by the excitatory and inhibitory inputs along the apicalshaft and into the soma. Two of these essential pyramidal neurons are arranged assuperficial and deep representatives, together with a spiny stellate excitatory and twoinhibitory essential neurons. Independently, Douglas et al. (1989) proposed a modelthat was quite similar to Shepherd’s, and called it the “canonical microcircuit” of thecortex. It is made up by the combination of three abstract neurons, two excitatory andone inhibitory, with each conceived as the average contribution of a larger number ofcells in the column that belong to the same class of neuron. One of the two excita-tory virtual cells represents the deep pyramidal population (of layers V and VI), theother represents the superficial pyramidal population (layers II and III) together withthe spiny stellate cells of layer IV.

A more complex circuit, based on empirical data, was proposed by Nieuwenhuys(1994), including, in addition to pyramidal and inhibitory neurons, cells like basket,bipolar and chandelier. A more theoretical proposal by Carandini and Heeger (2012)identify the process of normalization (dividing the responses of neurons by the summedactivity of a pool of neurons) as the most probable “canonical” computation in the cor-tex. All solutions reviewed so far were intended as canonical circuital organization ofthe entire cortex, but in fact were mostly tuned to sensorial areas. Recently researchersare starting to address the more refined picture of the cortex, with its differences be-tween sensorial, limbic, and prefrontal cortex, described above. Beul and Hilgetag(2015) sketched preliminary ideas for a canonical circuit suitable for the agranular cor-tex. Miller (2016) remarked that a canonical computation should account for the fun-damental difference between sensory areas, where the processing is strongly relatedto driving sensory stimuli, and motor and frontal areas, capable of generating theirown activity more spontaneously. The canonical computation has been also used as anevolutionary criterium for an explanation of the uniformity of the cortex. Accordingto Bosman and Aboitiz (2015), the strong regularity in the cortical microcircuit, withthe balanced interplay between excitation and inhibition, represents the basis of thecompartmentalized oscillatory dynamics, which may ground inferential coding com-putational capacities.

All the results reviewed above are not giving a definitive answer about the unifor-mity of the cortex in terms of its neuron-level behavior, but provide a support in thisdirection. We believe that an appropriate synthesis is given by the term adopted byHarris and Shepherd (2015), characterizing the remarkable contrast between the uni-formity of the cortex and the wide range of different functions: “serial homology”. Itis the same term used in physiology for the similarity in the organization of differentstructures within a single organism. For example, bones are very similar in their cellu-lar structure, but differ depending on sizes and mechanical properties, like in a femuror a phalange.

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4.3 Crossmodal plasticityCrossmodal plasticity refers to a class of abnormal developments in primary corticalareas, when following the loss of sensory inputs, neurons become responsive to sensorymodalities different from their original one (Karlen et al., 2010). The most dramaticcortical reorganizations are in consequence of a congenital sensory loss, or very earlyin development, although even in adults significant modifications are observed (Kaas,1997). Most of the research in this field is carried out by inducing sensorial depri-vation in animals. Even if results display large variations, for example with auditoryrecruitment in V1 of 6% of neurons in the cat, 63% in hamsters, 29% in the opossumwith an additional 26% of somatosensory recruitment, the overall picture is of an im-pressive crossmodal plasticity in primary areas (Karlen et al., 2006). In humans mostinformation on crossmodal plasticity comes from studies in congenitally blind indi-viduals. A wealth of neuroimaging studies have shown their occipital cortex activatedduring tactile and auditory tasks, see Collignon et al. (2013) for literature citations.More intriguing, there is evidence of engagement of the occipital cortex in languageprocessing (Burton et al., 2002, 2012), but restricted to congenital blinds only (Bednyet al., 2012). Recently, crossmodal plasticity shows up in the reverse condition too,with evidence of visual processes co-opted in the auditory cortex of congenitally deafindividuals. Weak but consistent representations of visual field location was found inpatterns of neural activity in auditory cortex of congenitally deaf but not hearing humansubjects (Almeida et al., 2015). Crossmodal plasticity can be seen as an extreme formof activity-dependent plasticity, seen in §4.1: the initial exuberance of neural connec-tions provides transient connections to the primary cortical areas from several regions,that are not preserved by the subsequent programmed cell death, if not supported byactivity (Innocenti and Price, 2005).

The most striking examples of cross-modal plasticity are the famous rewiring ex-periments, in which retinal axons of ferrets are connected at birth to the medial genicu-late nucleus, which relays the signals to A1 instead of V1. This abnormal connectivityinduced a functional reorganization of A1, that enabled visual behavior in the animals(Roe et al., 1987, 1990). A main question raised by this visual perception is how thetransformation in A1 occurs. Either A1 and V1 are so similar that the change in sen-sory input has not been so significant, or cross-modal plasticity is powerful enoughto mold A1 small-scale circuitry to function, partially, as V1. (Gao and Pallas, 1999)gave a precise answer, demonstrating that A1 deeply changes its normal organizationacross a major tonotopic axis, into a periodical, symmetrical array of orientation-tunedclusters of neurons, resembling that of V1. Using optical imaging Sharma et al. (2000)analyzed the arrays of orientation selective domains in the rewired A1, comparing withnormal V1. Several statistical parameters were found surprisingly similar, for examplethe amplitude of the selected angles were of 8.5o in A1 and 8.6o in V1, and the vectormagnitude of the orientation was nearly identical. The most significant difference wasin the size of the orientation domains, larger in A1 than in V1.

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5 Discussion of L&M’s argumentsOn the basis of our comprehensive survey of recent research on neural plasticity, letus now turn back to L&M’s arguments for concept nativism. Our overall strategy isto consider first what we have called the local structure thesis, that is, the assumptionthat cortical areas may have a fixed (or predetermined) innate structure. After havingshown that such assumption has little support, we intend to show that the connectivitythesis alone does not support any commitment to strong conceptual nativism.

5.1 The local structure thesisIs there any basis for the assumption that cortical areas have fixed, or predetermined,internal structures acting as a filter for specific categories of inputs? Pinker (2002)provides an extensive argument to this effect, which is further developed by L&M. Ac-cording to Pinker, even Sur in his research on rewired ferrets somehow acknowledgesthat the reorganization of auditory areas for the purpose of vision is made possible bya common structure between visual and auditory inputs. Pinker quotes the followingpassage:

If the normal organization of central auditory structures is not altered, or at leastnot altered significantly, by visual input, then we might expect that some operationssimilar to those we observe on visual inputs in operated ferrets be carried out aswell in the auditory pathway in normal ferrets. In other words, the animals withvisual inputs induced into the auditory pathway provide a different window onsome of the same operations that should occur normally in auditory thalamus andcortex. (Sur, 1989, p.45) (emphasis ours)

Pinker’s interpretation of these words is that there are commonalities between au-ditory and visual inputs, so that the change in modality does not affect the nature ofthe operations involved: “The mind treats soundmakers with different pitches as if theywere objects at different locations, and it treats jumps in pitch like motions in space.This means that some of the analyses performed on sights may be the same as the anal-yses performed on sounds, and could be computed, at least in part, by similar kinds ofcircuitry” (Pinker 2002: 96). More generally, Pinker’s (2002: 97) conclusion is that“the cortex has an intrinsic structure that allows it to perform certain kinds of compu-tation”. In the same vein, as we saw, L&M cite the metamodal hypothesis according towhich cortical areas have internal structures that cause preferences as to the inputs tobe processed.

However, this idea that cortical areas have different structures allowing differentkinds of computation is not significantly supported by current evidence, and surely thatis not what Sur (1989) had in mind. If we look at the larger context of his previous quo-tation, we find that what he refers to as “the same operations” normally occurring inauditory thalamus and cortex, and preserved in visual rewiring, are nothing but a gen-eral principle of self-organization based on spatiotemporal coactivation and the relatedsynaptic modification:

We suggest that, in auditory cortex of operated animals, the response features thatdepend specifically on the two-dimensional nature of visual input indicate a form

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of adaptive self-organization in cortex. [. . . ] The mechanism behind such or-ganization or adaptation might generally involve spatiotemporal coactivation insubsets of the visual input along with lateral inhibition, enabling modification ofsynaptic efficacy between presynaptic elements and restricted groups of postsy-naptic neurons or sets of postsynaptic elements. (Sur1989: 46; emphases ours)

Consistent with this view, a few lines before the passage quoted by Pinker, Sur empha-sized the deep structural uniformity of different parts of the brain:

The notion that different parts of sensory thalamus or neocortex share basic com-monalities is not new (Lorente de No 1938: Mountcastle 1978: Shepherd 1979).Indeed, there is an impressive similarity of cell types in different laminae and ofinterlaminar connections in different areas of sensory neocortex (e.g., Jones 1984).In particular, intrinsic projections of cells in different laminae are remarkably simi-lar in primary visual cortex (Gilbert and Wiesel 1979; Ferster and Lindstrom 1983)and primary auditory cortex (Mitani et al. 1985). In the thalamus, Jones (1985) hasemphasized the similarity of cell types and ultrastructural organization in differentsensory nuclei. (Sur 1989: 45)

In other words, Sur recalls here the same kind of evidence we reviewed above (§4.2)about the uniformity of structure and functioning through different cortical areas (anddifferent thalamic nuclei). The cortex is formed by the repetition of substantially iden-tical computational units, be they neurons or - more likely - canonical microcircuits.To be sure, there are a number of structural specificities. The primary visual area has,as we saw, a much larger density of neurons (§3.3) and therefore a greater computa-tional power than other parts of the cortex. The morphology of the primary auditoryand primary visual areas is quite different, with the former being elongated and almostlinear and the latter bidimensional. And, of course, the normal connectivity and the re-lated pattern of inputs are different. But none of this changes the fact that any corticalarea is governed by the same general principle of organization (uniformity of struc-ture) and computation (input-dependent plasticity, detection of patterns of regularity).There is no evidence that any specific similarity between the patterns of representationin different cortical areas - such as the one pointed out by Pinker between soundmakerswith different pitches and objects at different locations - is in fact hardwired. On thecontrary, all the available evidence suggests that structural similarities of this sort re-sult from plastic adjustment of undifferentiated computational units to environmentalregularities.

Interestingly, as to the metamodal hypothesis, Pascual-Leone and Hamilton (2001)do not make any clear proposal about which structural predispositions and preferenceswould be hardwired in sensory cortices. The only suggestion we find in the literaturecomes from Proulx et al. (2014). They try to spell out the metamodal assumption that“functional brain areas might be best defined by the computations they carry out ratherthan by their sensory-specific processing role” (p.16) by proposing that primary audi-tory cortex might be specialized in processing temporal information and primary visualcortex in processing spatial information. But this is exactly what is put into question bythe rewiring experiments of Sur and collaborators, where the primary auditory area isreadjusted to process spatial, not temporal, information. Therefore, the metamodal hy-

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pothesis appears to be too vague and controversial, to say the least, to provide supportto the claim of innate local structure.

There is another argument proposed by Pinker, which might provide reasons tothink that different cortical areas perform different kinds of computations. This isbased on the claim that the Hebbian principle, according to which neurons that firetogether wire together, does not apply everywhere in the cortex. In Pinker’s (2002: 93)words:

Fire-together-wire-together is a trick that solves a particular kind of wiring prob-lem: connecting a surface of receptors to a maplike representation in the cortex.The problem is found not just in the visual system but in other spatial senses suchas touch. [. . . ] Even the auditory system may use the trick [. . . ]. But the trick maybe useless elsewhere in the brain. The olfactory (smell) system, for example, wiresitself by a completely different technique.

However, there is no empirical support for this claim. To be sure, long-term po-tentiation - the learning process more strictly modeled on Hebb’s rule - is just oneof the neural mechanisms actually found in the cortex. As we showed in §3.2, wemust also consider long-term depression (an anti-Hebbian mechanism), spike-timing-dependent plasticity, and homeostatic plasticity (normally referred to as non-Hebbianmechanisms). But there is no reason to think that the coexistence of these mechanismsimplies a fixed hypothesis space for some cortical areas. More specifically, there isno reason to think, first, that non-Hebbian mechanisms are concentrated in specificcortical areas so that they would exhibit a different kind of computation with respectto the normal “fire-together-wire-together trick”. In particular, as we saw in §3.3 thisis not the case for the olfactory system. And second, there is no reason to think thatnon-Hebbian mechanisms are distributed within any cortical area in such a way thatspecific patterns of activity, and therefore specific kinds of computation, emerge in it.On the contrary, we insist, uniformity of distribution within and across areas is the gen-eral law of the cortex, also with respect to Hebbian and non-Hebbian mechanisms. Asa consequence, the computations executed in the cortex may be Hebbian at the scaleof populations of neurons even if it is not always so at the scale of single neurons, asnoted by Bush and Mainen (2015, p.10):

While not strictly Hebbian at the single cell level (as experimental data indicates[. . . ]) this scheme may be Hebbian at the population level, i.e. synaptic connec-tions increase in strength based on coincident activity of the cluster in which theyare embedded.

It should be noted that the above considerations apply equally well to the hypothe-sis of a fixed initial structure and to the hypothesis of an innate developmental programapt to produce the same structure, whenever exposed to the proper environmental scaf-folding. First, the balance between considerations in favor and against uniformity inthe brain (and especially in the cortex) is such that we have a number of models basedon the uniformity assumption, while there is virtually no model based on the oppositeassumption of non-uniform structures – irrespective of whether these are conceived asfixed or predetermined structures. Second, the changes in cortical areas due to rewiring

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reported by Sur can be seen as the result of a developmental course, and therefore theysuggest that there is no fixed structure and no innate developmental program preventingthose areas from being shaped as a result of the received inputs. As we saw, the spec-ulation about common abstract structures – be they fixed or predetermined – as in themetamodal hypothesis has no clear ground. Alternatively, one might speculate aboutthe existence of a multiplicity of innate developmental programs for the auditory cor-tex, which stay dormant until a specific class of inputs triggers one of them. However,once extended to flexibly adapt to diverse categories of inputs, the innate develop-mental program becomes hardly distinguishable from the general principle of corticalplasticity previously described. This is the road taken, for example, by Ryder (2004) inhis SINBAD (Set of INteracting BAckpropagating Dendrites) theory of semantic rep-resentations supported by cortical pyramidal cells. His idea is that the ability of thecortex to build representations of the external world results from an innate program,designed by evolution to fulfill the proper function (in the sense of Millikan, 1984) ofyielding reliable correlations of external sources, whose survival value is given by thepredictive capabilities of the cortex. Leaving the evolutionary explanation aside, thisnotion of innate developmental program collapses to our account of general plasticity,and it is certainly not what L&M and Pinker have in mind.

To be sure, what Ryder offers us, as we said, is not a developmental program inthe sense implicitly invoked by L&M’s appeal to the metamodal hypothesis. Generallyspeaking, on the basis of the current evidence one cannot entirely exclude in principlethe possibility that such innate predispositions will turn out to exist after all. However,Ryder’s model shows that the very idea of innate developmental programs for specificdomains may be a slippery slope, if it is true that the evidence of cortical plasticity im-poses on those alleged programs an ever growing degree of generality. In practice, wehave suggested that L&M’s attempt to dismiss the evidence of plasticity by appealingto commonalities between (for instance) visual and auditory inputs is underspecified inthe literature (see our previous considerations on the metamodal hypothesis) and risksvacuity, since any two patterns may be similar at some appropriate level of abstraction.In sum, though the existence of developmental programs is possible in principle, inthe face of the evidence of plasticity this appears as a defensive move, and not a verypromising one. On the other hand, our direct assessment of the literature on synapticplasticity shows, at the very least, that the evidence of cortical uniformity is such tosuggest a number of proposals based on Hebbian learning, while the sparse evidencepointing in the opposite direction has not led to any specific proposal about innatepredispositions.

All this said, we agree that when concept acquisition is at stake, the environmentcannot be taken as a passive source of experience, and notions like niche constructionand scaffolded social learning (Odling-Smee et al., 2003) play a fundamental role.However, we argue that it is the richly structured environment that constrains corticaldevelopment in the proper way, in our advanced cognitive abilities such as reading,writing, or mathematics. It is problematic to conceive innate developmental programsfor any cultural form of cognition (Menary, 2014).

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5.2 The connectivity thesisAs we have just seen, there is little ground for the belief that different cortical areaseither perform different kinds of computation, or follow essentially different learningrules, or have a fixed internal structure acting as a filter for specific categories of inputs.What about the other claim, that the plasticity of cortical areas is constrained by theirinnate patterns of connectivity? This claim is basically correct - although it shouldbe taken with caution since connectivity is not wholly impervious to experience (§4).However, this is hardly support for strong concept nativism, as we are going to show.

A first remark is that the insistence on connectivity is not limited to nativists. Animportant example is Pulvermuller et al. (2014), which provides a general frameworkfor neurobiological explanation in cognitive neuroscience based on correlation learn-ing, and therefore on a basically Hebbian approach. This notwithstanding, they ar-gue that associative learning alone cannot explain mirror neuron activity as proposedby Heyes (2010), with the argument that such activity also “requires preset specificcortico-cortical connectivity between relevant sensory and motor areas, which providethe substrate for such mirror learning” (Pulvermuller et al., 2014, p.578). Similarly,they propose that brain connectivity plays a crucial role in the emergence of languageabilities:

The left perisylvian cortex, where correlated motor and sensory activity are presentduring articulation of words, is more strongly connected by way of dorsal long-distance connections through the arcuate fascicle in humans compared with non-human monkeys, and it tends to be more strongly developed in the left hemispherethan in the right. (Pulvermuller et al., 2014, p.583)

This might be crucial for the learning of correlations between articulatory move-ments and auditory input patterns in infant babbling (idem: 578); but also the subse-quent building of a huge vocabulary that sets human languages apart from all knownanimal communication systems may critically depend on the availability of rich fron-totemporal connectivity (idem: 583). More generally, the thesis of the paper is that “anexplanation of semantic topography and category specificity [in the brain in general] ispossible in terms of correlation learning mechanisms in association with corticocorti-cal connectivity” (idem: 585; emphasis ours).

On the other hand, nativists such as Mahon and Caramazza (2011) appeal to con-nectivity as an argument for the thesis that “different semantic domains are processedby distinct, dedicated mechanisms”. As Mahon (2015) puts it:

The core aspect of this proposal is that connectivity is what is innate and whatdrives domain-specificity. In other words, the domain-specificity of a given regionis not driven (only) by organizational principles expressed over information localto that region [for this, see our arguments in §5.1], but by the broader network ar-chitecture in which that region is embedded [. . . ]. Thus, for instance, the regionsthat exhibit specificity for tools (medial fusiform gyrus) do so because that regionhas (by hypothesis) privileged connectivity with motor-relevant structures that areinvolved in actually manipulating objects. Thus, the high-level visual representa-tions for tools ’come to live’ in those regions of the visual system that are already

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connected up with other regions of the brain that process motor-relevant informa-tion about tools. Similarly, the argument would be that faces are represented inregions of high-level visual cortex that have privileged connectivity to regions ofthe brain that process affective information.

It is important to recognize that, as we noticed earlier, the assumption that con-nectivity is largely innate is not particularly controversial. Most of all, in Mahon andCaramazza’s proposal connectivity appears to constrain the specificity of cortical areasas a function of the content of other connected areas. In the examples above, speci-ficity for tools depends on the fact that certain portions of the cortex are connected to- amongst others - motor-relevant structures, and specificity for faces depends on thefact that certain portions of the cortex are connected to - amongst others - areas thatprocess affective information. But then, specificity depends on the inputs that an areais exposed to. Consider again the experiments conducted by Sur on ferrets. By theconnectivity argument, primary auditory cortex can be said to be specialized for hear-ing because of its connectivity with - amongst others - certain portions of the thalamus.Should the connectivity change, the function will change as well. But this occursbecause the rewiring causes a change in the received inputs. In other words, the con-nectivity argument is entirely compatible with the empiricist claim that cortical areasspecialize as an effect of the received inputs. This is also proved by the fact that, whenMahon and Caramazza (2011) list the categories that appear to have domain-specific,dedicated areas in the brain, they mention - besides faces, animals, tools, places, andbody parts - written words as well. Given how recently literacy has been prevalent,

there is no motivation for presuming specialization of function to be innatelypresent for printed words in the human brain. Yet, because there are regions thatare consistently specialized for printed words, the expectation would be that thisspecialization is driven by connectivity between the ventral stream and regions ofthe brain involved in linguistic processing. (Mahon and Caramazza 2011: 102)

But then, the argument that innate connectivity drives domain-specificity appliesalso when cortical areas are clearly not designed by evolution for the categories theyactually come to process. Domain-specificity simply results from the fact that con-nectivity determines the inputs and these in turn shape the area. To be sure, some ofthe categories for which there is domain-specific location in the brain may have beenimportant in our evolutionary history, and as a consequence there may have been evo-lutionary pressure towards establishing or strengthening certain connections. For oneexample, for all we know the arcuate fascicle might have evolved under the pressureof language use. But we should clearly distinguish between two claims: it is one thingto say that “there are innately dedicated neural circuits for the efficient processing of alimited number of evolutionarily motivated domains of knowledge” (Mahon and Cara-mazza 2011: 97; emphasis ours); it is quite another to claim that innate connectivitydrives domain-specificity. The latter claim has much wider application than the former:it also applies to a variety of categories with no evolutionarily relevant history, whichnevertheless have domain-specific locations in the brain simply because of the inputsconveyed to those areas. But, most importantly, even for evolutionarily motivated do-mains of knowledge, innate connectivity shapes cortical areas simply as a function of

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the inputs it conveys. Let us suppose, for example, that faces actually constitute suchan evolutionarily motivated domain. By hypothesis, the fusiform face area processesfaces because it receives inputs from areas that process affective information. Shouldthe inputs change, its function will be different, too.

In sum, nativists such as Caramazza and Mahon are right in pointing out that con-nectivity is innate and that it drives domain-specificity, but there is a good reason whynon-nativists such as Pulvermuller and colleagues appeal to the same argument: theargument is entirely compatible with the empiricist claim that domain-specificity isinput-driven.

There is a final point related to connectivity that is worthy of consideration. In theliterature on nativism, it is usual to associate empiricism with connectionism. As Pinker(2002: 74) puts it, “some modelers from the school of cognitive science called connec-tionism suggest that generic neural networks can account for all of human cognition”.Thus, in the chapter where he defends nativism from its enemies, Pinker discusses con-nectionism and extreme plasticity in continuity with each other. In this context, theinsistence on connectivity may acquire new meaning. Pinker acknowledges that, sincein the brain there is nothing but neural computations, some kind of neural networksmust in the end manage to duplicate whatever the mind can do. Specifically, “a sys-tem assembled out of beefed-up subnetworks could escape all the criticisms. But thenwe would no longer be talking about a generic neural network!” (Pinker 2002: 82).In other words, Pinker’s view is that modellers have to make choices which stand forthe innate constraints they pretend to do without. As Wellman (2014: 118) observes,nativists generally assume that both the brain and computational models must havebuilt-in structures: “For computational learning, the top-down, knowledge-rich struc-tures come from the computer program’s designer. For mental modules, the designeris evolution”.

Now, we gave reasons to reject the claim of built-in structures at the scale of cor-tical areas, while this claim appeared to be largely correct with regard to connectivitybetween areas. In this sense, Pinker’s appeal to a system of subnetworks, in the placeof a generic neural network, is indeed appropriate and brings with it significant com-putational consequences. In other words, the fact that traditional connectionist modelsconsist in one single network imposes structural limitations on what can be modeled.An interesting example is the emergence of abstract representations from experience.This has been a crucial issue in the recent debate between connectionism and Bayesianmodels (see McClelland et al. (2010) versus Griffiths et al. (2010)). Bayesian modelsassume that hierarchical representations on multiple levels of abstraction can be ex-tracted at once from the same experiential inputs, while connectionist models can only“implicitly capture representations like hierarchically structured taxonomies” (Griffithset al. 2010: 359). To be sure, Bayesian models are mathematical devices with no closecorrespondence to brain structure. However, there is a family of neural network archi-tectures that are built so as to simulate a hierarchy of cortical maps, with a number ofbiologically realistic features. In such simulated maps, representations at different lev-els of abstraction can emerge by way of input-driven self-organization. In (Plebe et al.,2010) the model of self-organizing cortical maps introduced by (Miikkulainen et al.,2005) and described above in §3.3 is applied in simulating visual areas V1, V2, VOand LOC, and the auditory areas A1 and STS, demonstrating various aspects of lexical

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categorization, like fast mapping (Swingley, 2010). Garagnani et al. (2000) used a sim-ilar basic model of cortical maps with lateral excitatory and inhibitory connections, inreproducing with a finer degree the auditory pathway, including A1, auditory belt andparabelt areas, inferior prefrontal, premotor and primary motor areas, in an experimentof word learning based on action-perception correlation.

Fuster (2001, 2008) has influentially emphasized that the entire cortex is organizedin hierarchical manner, with levels of representation that are progressively more inte-grative and abstract. This cannot be done within a single neural network, it requiresinstead a multiplicity of areas with different degrees of reciprocal accessibility, inPinker’s words, “a system assembled out of [. . . ] subnetworks”. In the same vein,Pulvermuller et al. (2014: 586-7) propose that “the key to human cognition lies inthe connections between cognitive systems, in their structural linkage and functionalinteraction”. The complex architecture of the cortex, with its intricate system of short-and long-distance connections between areas and maps, is a largely innate machinery,and this complex structure is crucial in order to account for the brain’s computationalpower.

However, this is no support for concept nativism, insofar as cortical domain-specificityfor faces, tools, animals or whatever depends on which inputs are conveyed to the rel-evant cortical areas.

6 ConclusionsL&M warned that “plasticity has become a catchall term”, and in this work we triedto take their words of advice seriously, by analyzing different ways of changing inneural circuits caught by the term “plasticity”, particularly in the cortex, in the light ofcurrent neuroscience. It turned out that the most common forms of plasticity, in placeevery day of our live and constitutive of concepts formation, addressed in §3.2 and§3.3, are those not taken into consideration by L&M. Their discussion is entirely basedon extreme forms of plasticity only, as resulting from congenital sensory deprivations,or animal experiments. Most of all, their focus on extreme plasticity is instrumentalin arguing that even in those cases plasticity is much more constrained than usuallybelieved. This argumentative strategy is somewhat indirect, insofar as the existence ofrigid constraints and local specificities in cortical areas is inferred by considerations ofwhat does not change in cases of extreme plasticity. However, much is known aboutthe ordinary mechanisms of plasticity both at the synaptic level and at the level ofcortical areas, thus we chose to take a more direct route by providing a survey of thatevidence. The results strongly support the view that the actual innate predispositionsand constraints concern morphology and connectivity in the first place, while thereseems to be little ground for the claim that different cortical areas have distinct prewiredlocal structures, as is proposed by concept nativism. The cortex is largely characterizedby uniformity of structure and functioning both within and between its different areas.The sparse evidence pointing in the opposite direction is far from being strong enoughto suggest a consistent model based on non-uniformity of computation, and definitelythere is no such model able to compete with Hebbian models of the cortex. In sum,L&M do not provide a convincing argument for the thesis that plasticity is constrained

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by fixed or predetermined conceptual structures in cortical areas: the evidence for suchstructures is, at the very least, quite weak. Once this is made clear, the argument fromconnectivity – according to which even extreme plasticity is constrained by patternsof connectivity – loses much of its strength, in that it only implies that the function ofcortical areas is shaped by their received patterns of inputs, a conclusion that is verywelcome to non-nativists.

Nevertheless, the insistence of concept nativists on connectivity has the merit ofcalling attention to the importance of the brain’s computational architecture, basedon a multiplicity of maps that are correlated with one another, a point that has beenunderestimated by traditional connectionist approaches.

Notice that a range of arguments, in addition to those here discussed, has beenpresented in support of concept nativism. Conversely, other considerations in favorof concept empiricism can be found elsewhere (Prinz, 2002; Sirois et al., 2008; Prinz,2012; Forest, 2014). We have limited ourselves to neural plasticity, showing that evi-dence cannot be marshaled in favor of concept nativism.

ReferencesAckman, J. B. and Crair, M. C. (2014). Role of emergent neural activity in visual map develop-

ment. Current Opinion in Neurobiology, 24:166–175.

Ahmed, B., Cordery, P. M., McLelland, D., Bair, W., and Krug, K. (2011). Long-range clusteredconnections within extrastriate visual area V5/MT of the rhesus macaque. Cerebral Cortex,22:60–73.

Alfano, C. and Studer, M. (2013). Neocortical arealization: Evolution, mechanisms, and openquestions. Developmental Neurobiology, 73:411–447.

Almeida, J., He, D., Chen, Q., Mahon, B. Z., Zhang, F., Gonclves, O., Fang, F., and Bi, Y. (2015).Decoding visual location from neural patterns in the auditory cortex of the congenitallydeaf. Psychological Science, 26:1771–1782.

Aronoff, R., Matyas, F., Mateo, C., Ciron, C., Schneider, B., and Petersen, C. C. (2010). Long-range connectivity of mouse primary somatosensory barrel cortex. European Journal ofNeuroscience, 31:2221–2233.

Artola, A. and Singer, W. (1987). Long term potentiation and NMDA receptors in rat visualcortex. Nature, 330:649–652.

Ashby, W. R. (1947). Principles of the self-organizing dynamic system. The Journal of generalpsychology, 37:125–128.

Barbas, H. (2015). General cortical and special prefrontal connections: Principles from structureto function. Annual Review of Neuroscience, 38:269–289.

Bear, M. and Kirkwood, A. (1993). Neocortical long term potentiation. Current Opinion inNeurobiology, 3:197–202.

Bednar, J. A. and Miikkulainen, R. (2006). Joint maps for orientation, eye, and direction prefer-ence in a self-organizing model of V1. Neurocomputing, 69:1272–1276.

Bedny, M., Konkle, T., Pelphrey, K., Saxe, R., and Pascual-Leone, A. (2010). Sensitive period fora multimodal response in human visual motion area MT/MST. Current Biology, 20:1900–1906.

32

Bedny, M., Pascual-Leone, A., Dravida, S., and Saxe, R. (2012). A sensitive period for languagein the visual cortex: Distinct patterns of plasticity in congenitally versus late blind adults.Brain and Language, 122:162–170.

Ben-Ari, Y. (2002). Excitatory actions of gaba during development: The nature of the nurture.Nature Reviews Neuroscience, 3:728–739.

Ben-Ari, Y., Gaiarsa, J.-L., Tyzio, R., and Khazipov, R. (2007). GABA: A pioneer transmitterthat excites immature neurons and generates primitive oscillations. Physiological Reviews,87:1215–1284.

Berlin, R. (1858). Beitrag zur Structurlehre der Grosshirnwindungen. PhD thesis, MedicinischenFakultat zu Erlangen.

Berlucchi, G. and Buchtel, H. (2009). Neuronal plasticity: historical roots and evolution ofmeaning. Nature Reviews Neuroscience, 192:307–319.

Bermudez-Rattoni, F., editor (2007). Neural plasticity and memory : from genes to brain imag-ing. CRC Press, Boca Raton (FL).

Beul, S. F. and Hilgetag, C. C. (2015). Towards a ’canonical’ agranular cortical microcircuit.Frontiers in Neuroanatomy, 8:165.

Bliss, T. and Collingridge, G. (1993). A synaptic model of memory: long-term potentiation inthe hippocampus. Nature, 361:31–39.

Bliss, T. and Lømo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentatearea of the anaesthetized rabbit following stimulation of the perforant path. Journal ofPhysiology, 232:331–356.

Blumberg, M. S., Freeman, J. H., and Robinson, S., editors (2010). Oxford Handbook of Devel-opmental Behavioral Neuroscience. Oxford University Press, Oxford (UK).

Bontempi, B., Silva, A., and Christen, Y., editors (2007). Memories: Molecules and Circuits.Springer-Verlag, Berlin.

Born, R., Trott, A. R., and Hartmann, T. S. (2015). Cortical magnification plus cortical plasticityequals vision? Vision Research, 111:161–169.

Bosman, C. A. and Aboitiz, F. (2015). Functional constraints in the evolution of brain circuits.Frontiers in Neuroscience, 9:303.

Bourne, J. A. and Rosa, M. G. (2006). Hierarchical development of the primate visual cortex,as revealed by neurofilament immunoreactivity: Early maturation of the middle temporalarea (MT). Cerebral Cortex, 16:405–414.

Braak, H. (1974). On the structure of the human archicortex. i. the cornu ammonis. a Golgi andpigment architectonic study. Cell Tissue Research, 152:349–383.

Braddick, O., Atkinson, J., and Innocenti, G. M., editors (2011). The developing brain: From de-velopmental biology to behavioral disorders and their remediation. Cambridge UniversityPress, Cambridge (UK).

Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirmrinde. Barth, Leipzig.

Buonomano, D. V. and Merzenich, M. M. (1998). Cortical plasticity: From synapses to maps.Annual Review of Neuroscience, 21:149–186.

Burkhalter, A., Bernardo, K. L., and Charles, V. (1993). Development of local circuits in humanvisual cortex. Journal of Neuroscience, 13:1916–1931.

Burton, H., Sinclair, R. J., and Agato, A. (2012). Recognition memory for Braille or spokenwords: An fMRI study in early blind. Brain, 1438:22–34.

33

Burton, H., Snyder, A. Z., DIamond, J., and Raichle, M. E. (2002). Adaptive changes in early andlate blind: A fMRI study of verb generation to heard nouns. Journal of Neurophysiology,88:3359–3371.

Bush, P. C. and Mainen, Z. F. (2015). Columnar architecture improves noise robustness in amodel cortical network. PLoS ONE.

Butz, M., Worgotter, F., and van Ooyen, A. (2009). Activity-dependent structural plasticity.Brain Research Reviews, 60:287–305.

Cahalane, D. J., Charvet, C. J., and Finlay, B. L. (2012). Systematic, balancing gradients inneuron density and number across the primate isocortex. Frontiers in Neuroanatomy, 6:28.

Carandini, M. and Heeger, D. (2012). Normalization as a canonical neural computation. NatureReviews Neuroscience, 13:51–62.

Carlo, C. N. and Stevens, C. F. (2013). Structural uniformity of neocortex, revisited. Proceedingsof the Natural Academy of Science USA, 110:719–725.

Caroni, P., Donato, F., and Muller, D. (2012). Structural plasticity upon learning: regulation andfunctions. Nature Reviews Neuroscience, 13:478–490.

Charvet, C. J., Cahalane, D. J., and Finlay, B. L. (2015). Systematic, cross-cortex variation inneuron numbers in rodents and primates. Cerebral Cortex.

Cheetham, C. E., Barnes, S. J., Albieri, G., Knott, G. W., and Finnerty, G. T. (2014). Pansynapticenlargement at adult cortical connections strengthened by experience. Cerebral Cortex,24:521–531.

Churchland, P. M. (1988). Perceptual plasticity and theoretical neutrality: a reply to Jerry Fodor.Philosophy of Science, 55:167–187.

Cohen-Tannoudji, M., Babinet, C., and Wassef, M. (1994). Early determination of a mousesomatosensory cortex marker. Nature, 368:460–463.

Collignon, O., Dormal, G., and Lepore, F. (2013). Building the brain in the dark: Functional andspecific crossmodal reorganization in the occipital cortex of blind individuals. In Steevesand Harris (2013), pages 114–137.

Colombo, J. (1982). The critical period concept: Research, methodology, and theoretical issues.Psychological Bulletin, 91:260–275.

Cooke, S. F. and Bear, M. F. (2013). How the mechanisms of long-term synaptic potentiation anddepression serve experience-dependent plasticity in primary visual cortex. Philosophicaltransactions of the Royal Society B, 369:20130284.

Cowie, F. (1999). What’s Within? Nativism Reconsidered. Oxford University Press, Oxford(UK).

Crair, M. C. (1999). Neuronal activity during development: permissive or instructive? CurrentOpinion in Neurobiology, 9:88–93.

Crowley, J. C. and Katz, L. C. (2002). Ocular dominance development revisited. Current Opinionin Neurobiology, 12:104–109.

Crozier, R. A., Wang, Y., Liu, C.-H., and Bear, M. F. (2007). Deprivation-induced synapticdepression by distinct mechanisms in different layers of mouse visual cortex. Proceedingsof the Natural Academy of Science USA, 104:1383–1388.

Curtiss, S. (1977). Genie – A Psycholinguistic Study of a Modern-Day Wild Child. AcademicPress, New York.

34

Danelli, L., Cossu, G., Berlingeri, M., Bottini, G., Sberna, M., and Paulesu, E. (2013). Is alone right hemisphere enough? neurolinguistic architecture in a case with a very early lefthemispherectomy. Neurocase: The Neural Basis of Cognition, 19:209–231.

Deco, G. and Rolls, E. (2004). A neurodynamical cortical model of visual attention and invariantobject recognition. Vision Research, 44:621–642.

Desai, N. S., Cudmore, R. H., Nelson, S. B., and Turrigiano, G. G. (2002). Critical periods forexperience-dependent synaptic scaling in visual cortex. Nature Neuroscience, 5:783–789.

Dougherty, R. F., Koch, V. M., Brewer, A. A., Fischer, B., Modersitzki, J., and Wandell, B. A.(2003). Visual field representations and locations of visual areas V1/2/3 in human visualcortex. Journal of Vision, 3:586–598.

Douglas, R. J., Martin, K. A., and Whitteridge, D. (1989). A canonical microcircuit for neocor-tex. Neural Computation, 1:480–488.

Eliasmith, C. and Anderson, C. H. (2003). Neural Engineering Computation, Representation,and Dynamics in Neurobiological Systems. MIT Press, Cambridge (MA).

Elman, J. L., Bates, E., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., and Plunkett, K. (1996).Rethinking innateness A Connectionist Perspective on Development. MIT Press, Cam-bridge (MA).

Elston, G. N. (2003). Cortex, cognition and the cell: New insights into the pyramidal neuron andprefrontal function. Cerebral Cortex, 13:1124–1138.

Elston, G. N., Benavides-Piccione, R., Elston, A., Manger, P. R., and DeFelipe, J. (2011).Pyramidal cells in prefrontal cortex of primates: marked differences in neuronal structureamong species. Frontiers in Neuroanatomy, 5:2.

Fahle, M. and Poggio, T., editors (2002). Perceptual Learning. MIT Press, Cambridge (MA).

Fallon, J. B., Irvine, D. R. F., and Shepherd, R. K. (2009). Neural prostheses and brain plasticity.Journal of Neural Engineering, 6:065008.

Feldman, D. E. (2000). Timing-based LPT and LTD at vertical inputs to layer II/III pyramidalcells in rat barrel cortex. Neuron, 27:45–56.

Feldman, D. E. (2009). Synaptic mechanisms for plasticity in neocortex. Annual Review ofNeuroscience, 32:33–55.

Feldman, D. E. (2012). The spike-timing dependence of plasticity. Neuron, 75:556–571.

Forest, D. (2014). Neuroconstructivism: A developmental turn in cognitive neuroscience? InWolfe, C. T., editor, Brain Theory – Essays in Critical Neurophilosophy, pages 68–87.Palgrave Macmillan, London.

Fox, K. (2002). Anatomical pathways and molecular mechanisms for plasticity in the barrelcortex. Neuroscience, 111:799–814.

Fuchs, E. and Flugge, G. (2014). Adult neuroplasticity: More than 40 years of research. NeuralPlasticity, 2014:ID541870.

Fuster, J. M. (2001). The prefrontal cortex–an update: Time is of the essence. Neuron, 30:319–333.

Fuster, J. M. (2008). The Prefrontal Cortex. Academic Press, New York. fourth edition.

Gao, W.-J. and Pallas, S. (1999). Cross-modal reorganization of horizontal connectivity inauditory cortex without altering thalamocortical projections. Journal of Neuroscience,19:7940–7950.

35

Garagnani, M., Wennekers, T., and Pulvermuller, F. (2000). Recruitment and consolidation ofcell assemblies for words by way of hebbian learning and competition in a multi-layerneural network. Cognitive Computation, 1:160–197.

Gilbert, C. D. and Wiesel, T. N. (1983). Clustered intrinsic connections in cat visual cortex.Journal of Neuroscience, 3:1116–1133.

Gilbert, C. D. and Wiesel, T. N. (1989). Clustered intrinsic connections in cat visual cortex.Journal of Neuroscience, 9:2432–2442.

Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., Nugent III,T. F., Herman, D. H., Clasen, L. S., Toga, A. W., Rapoport, J. L., and Thompson, P. M.(2004). Dynamic mapping of human cortical development during childhood through earlyadulthood. Proceedings of the Natural Academy of Science USA, 101:8174–8179.

Gottfried, J. A. (2010). Central mechanisms of odour object perception. Nature Reviews Neuro-science, 11:628–641.

Greenough, W. T., Black, J. E., and Wallace, C. S. (1987). Experience and brain development.Child Development, 58:539–559.

Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., and Tenenbaum, J. B. (2010). Probabilisticmodels of cognition: exploring representations and inductive biases. Trends in CognitiveSciences, 14:357–364.

Haeusler, S., Schuch, K., and Maass, W. (2009). Motif distribution, dynamical properties, andcomputational performance of two data-based cortical microcircuit templates. Journal ofPhysiology – Paris, 21:1229–1243.

Haken, H. (1978). Synergetics – An Introduction, Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry and Biology. Springer-Verlag, Berlin, second edition.

Harris, J. and Rubel, E. (2006). Afferent regulation of neuron number in the cochlear nucleus:Cellular and molecular analyses of a critical period. Hearing Research, 216–217:127–137.

Harris, K. D. and Shepherd, G. M. (2015). The neocortical circuit: themes and variations. NatureNeuroscience, 18:170–181.

Hawrylycz, M. J., Lein, E. S., Guillozet-Bongaarts, A. L., and et al., E. H. S. (2012). An anatom-ically comprehensive atlas of the adult human brain transcriptome. Nature, 489:391–399.

Hebb, D. O. (1949). The Organization of Behavior. John Wiley, New York.

Hensch, T. K. (2005). Critical period plasticity in local cortical circuits. Nature Reviews Neuro-science, 6:887–888.

Herculano-Houzel, S., Catania, K., Manger, P. R., and Kaas, J. H. (2015). Mammalian brains aremade of these: A dataset of the numbers and densities of neuronal and nonneuronal cells inthe brain of glires, primates, scandentia, eulipotyphlans, afrotherians and artiodactyls, andtheir relationship with body mass. Brain, Behavior and Evolution, 86:145–163.

Herculano-Houzel, S., Collins, C. E., Wong, P., Kaas, J. H., and Lent, R. (2008). The basicnonuniformity of the cerebral cortex. Proceedings of the Natural Academy of ScienceUSA, 34:12593–12598.

Herculano-Houzel, S. and Lent, R. (2005). Isotropic fractionator: A simple, rapid method forthe quantification of total cell and neuron numbers in the brain. Journal of Neuroscience,25:2518–2521.

Heyes, C. (2010). Where do mirror neurons come from? Neuroscience & Biobehavioral Re-views, 34:575–583.

36

Holtmaat, A. and Svoboda, K. (2009). Experience-dependent structural synaptic plasticity in themammalian brain. Nature Reviews Neuroscience, 10:647–658.

Homae, F., Watanabe, H., Otobe, T., Nakano, T., Go, T., Konishi, Y., and Taga, G. (2010). De-velopment of global cortical networks in early infancy. Journal of Neuroscience, 30:4877–4882.

Hou, C., Pettet, M. W., Sampath, V., Candy, T. R., and Norcia, A. M. (2003). Development ofthe spatial organization and dynamics of lateral interactions in the human visual system.Journal of Neuroscience, 23:8630–8640.

Howard, J. D., Plailly, J., Grueschow, M., Haynes, J.-D., and Gottfried, J. A. (2009). Odorquality coding and categorization in human posterior piriform cortex. Nature Neuroscience,12:932–938.

Huang, S., Rozas, C., Trevino, M., Contreras, J., Yang, S., Song, L., Yoshioka, T., Lee, H.-K.,and Kirkwood, A. (2014). Associative hebbian synaptic plasticity in primate visual cortex.Journal of Neuroscience, 34:7575–7579.

Hubel, D. and Wiesel, T. (1959). Receptive fields of single neurones in the cat’s striate cortex.Journal of Physiology, 148:574–591.

Hubel, D. and Wiesel, T. (1963). Single-cell responses in striate cortex of kittens deprived ofvision in one eye. Journal of Neurophysiology, 26:1003–1017.

Huttenlocher, P. R. (2002). Neural plasticity – The Effects of Environment on the Developmentof the Cerebral Cortex. Harvard University Press, Cambridge (MA).

Innocenti, G. M. and Price, D. (2005). Exuberance in the development of cortical networks.Nature Reviews Neuroscience, 6:955–965.

Ito, M. (1989). Long-term depression. Annual Review of Neuroscience, 12:85–102.

James, W. (1890). The Principles of psychology. Holt, Rinehart and Winston, New York.

Kaas, J. H. (1997). Plasticity of sensory and motor maps in adult mammals. Annual Review ofNeuroscience, 14:137–167.

Kaplan, D. M. (2011). Explanation and description in computational neuroscience. Synthese,183:339–373.

Kaplan, D. M. and Craver, C. F. (2011). Towards a mechanistic philosophy of neuroscience.In French, S. and Saatsi, J., editors, Continuum Companion to the Philosophy of Science,pages 268–292. Continuum Press, London.

Karbowski, J. (2014). Constancy and trade-offs in the neuroanatomical and metabolic design ofthe cerebral cortex. Frontiers in Neural Circuits, 8:9.

Karlen, S. J., Hunt, D. L., and Krubitzer, L. (2010). Cross-modal plasticity in the mammalianneocortex. In Blumberg et al. (2010), pages 357–374.

Karlen, S. J., Kahn, D., and Krubitzer, L. (2006). Early blindness results in abnormal corticocor-tical and thalamo cortical connections. Neuroscience, 142:843–858.

Karmiloff-Smith, A. (1992). Beyond modularity: A developmental perspective on cognitivescience. MIT Press, Cambridge (MA).

Katz, B. (1971). Quantal mechanism of neural transmitter release. Science, 173:123–126.

Katz, L. and Shatz, C. (1996). Synaptic activity and the construction of cortical circuits. Science,274:1133–1138.

Khazipov, R. and Buzsaki, G. (2010). Early patterns of electrical activity in the developingcortex. In Blumberg et al. (2010), pages 161–177.

37

Khazipov, R., Colonnese, M., and Minlebaev, M. (2013). Neonatal cortical rhythms. In Ruben-stein and Rakic (2013a), pages 131–153.

Kisvarday, Z. F., Toth, E., Rausch, M., and Eysel, U. T. (1997). Orientation-specific relationshipbetween populations of excitatory and inhibitory lateral connections in the visual cortex ofteh cat. Cerebral Cortex, 7:605–618.

Ko, H., Mrsic-Flogel, T. D., and Hofer, S. B. (2014). Emergence of feature-specific connectivityin cortical microcircuits in the absence of visual experience. Journal of Neuroscience,34:9812–9816.

Kolb, B. (1995). Brain plasticity and behavior. Lawrence Erlbaum Associates, Mahwah (NJ).

Kolb, B. and Gibb, R. (2014). Searching for the principles of brain plasticity and behavior.Cortex, 58:251–260.

Kovacs, I., Kozma, P., Feher, A., and Benedek, G. (1999). Late maturation of visual spatialintegration in humans. Proceedings of the Natural Academy of Science USA, 96:12204–12209.

Krubitzer, L. (1995). The organization of neocortex in mammals: are species differences reallyso different? Trends in Neuroscience, 8:408–417.

Krubitzer, L. and Kaas, J. H. (2005). The evolution of the neocortex in mammals: how isphenotypic diversity generated? Current Opinion in Neurobiology, 15:444–453.

Kuhl, P. K. (2010). Brain mechanisms in early language acquisition. Neuron, 67:713–727.

Laurence, S. and Margolis, E. (2015a). Concept nativism and neural plasticity. In Laurence andMargolis (2015b).

Laurence, S. and Margolis, E., editors (2015b). The Conceptual Mind: New Directions in theStudy of Concepts. MIT Press, Cambridge (MA).

Levy, W. and Steward, O. (1983). Temporal contiguity requirements for long-term associativepotentiation/depression in the hippocampus. Neuroscience, 8:791–797.

Li, W., Luxenberg, E., Parrish, T., and Gottfried, J. A. (2006). Learning to smell the roses:Experience-dependent neural plasticity in human piriform and orbitofrontal cortices. Neu-ron, 52:1097–1108.

Lledo, P.-M., Alonso, M., and Grubb, M. S. (2006). Adult neurogenesis and functional plasticityin neuronal circuits. Nature Reviews Neuroscience, 7:179–193.

Lovden, M., Backman, L., Lindenberger, U., Schaefer, S., and Schmiedek, F. (2010). A theoret-ical framework for the study of adult cognitive plasticity. Psychological Bulletin, 136:659– 676.

Mahon, B. Z. (2015). Missed connections: A connectivity constrained account of the represen-tation and organization of object concepts. In Laurence and Margolis (2015b).

Mahon, B. Z. and Caramazza, A. (2011). What drives the organization of object knowledgein the brain? the distributed domain-specific hypothesis. Trends in Cognitive Sciences,15:97–103.

Marcus, G. F., Marblestone, A., and Dean, T. (2014). The atoms of neural computation. Science,346:551–552.

Marik, S. A., Yamahachi, H., McManus, J. N. J., Szabo, G., and Gilbert, C. D. (2010). Axonaldynamics of excitatory and inhibitory neurons in somatosensory cortex. PLoS Biology,8:1–16.

38

Markram, H., Gerstner, W., and Sjostrom, P. J. (2011). A history of spike-timing-dependentplasticity. Frontiers in Synaptic Neuroscience, 3:4.

Markram, H., Gerstner, W., and Sjostrom, P. J. (2012). Spike-timing-dependent plasticity: acomprehensive overview. Frontiers in Synaptic Neuroscience, 4:2.

Markram, H., Lubke, J., Frotscher, M., and Sakmann, B. (1997). Regulation of synaptic efficacyby coincidence of postsynaptic APs and EPSPs. Science, 275:213–215.

Marr, D. (1970). A theory for cerebral neocortex. Proceedings of the Royal Society of LondonB, 176:161–234.

Mason, C. (2009). The development of developmental neuroscience. Journal of Neuroscience,29:2735–12747.

Mastronarde, D. N. (1983). Correlated firing of retinal ganglion cells: I. spontaneously activeinputs in X- and Y-cells. Journal of Neuroscience, 14:409–441.

May, A. (2011). Experience-dependent structural plasticity in the adult human brain. Trends inCognitive Sciences, 15:475–482.

McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg,M. S., and Smith, L. B. (2010). Letting structure emerge: Connectionist and dynamicalsystems approaches to cognition. Trends in Cognitive Sciences, 14:348–356.

Meister, M., Wong, R., Daylor, D., and Shatz, C. (1991). Synchronous bursts of action potentialsin ganglion cells of the developing mammalian retina. Science, 252:939–943.

Menary, R. (2014). Neural plasticity, neuronal recycling and niche construction. Minds andLanguage, 29:286–303.

Miikkulainen, R., Bednar, J., Choe, Y., and Sirosh, J. (2005). Computational maps in the visualcortex. Springer-Science, New York.

Miller, K. D. (2016). Canonical computations of cerebral cortex. Current Opinion in Neurobi-ology, 37:75–84.

Millikan, R. G. (1984). Language, thought, and other biological categories: New foundationsfor realism. MIT Press, Cambridge (MA).

Møller, A. R., editor (2006). Neural Plasticity and Disorders of the Nervous System. CambridgeUniversity Press, Cambridge (UK).

Mountcastle, V. (1957). Modality and topographic properties of single neurons in cats somaticsensory cortex. Journal of Neurophysiology, 20:408–434.

Nakamura, H. (2013). Area patterning of the mammalian cortex. In Rubenstein and Rakic(2013b), pages 45–60.

Newport, E., Bavelier, D., and Neville, H. J. (2001). Critical thinking about critical periods:Perspectives on a critical period for language acquisition. In Dupoux, E., editor, Language,Brain, and Cognitive Development: Essays in honor of Jacques Mehler, pages 481–502.MIT Press, Cambridge (MA).

Nieuwenhuys, R. (1994). The neocortex. Anatomy and Embryology, 190:307–337.

Nieuwenhuys, R., Voogd, J., and van Huijzen, C. (2008). The Human Central Nervous System.Springer-Verlag, Berlin.

Odling-Smee, F. J., Laland, K. N., and Feldman, M. W., editors (2003). Niche Construction:The Neglected Process in Evolution. Princeton University Press, Princeton (NJ).

Ohl, F. W., Scheich, H., and Freeman, W. J. (2001). Change in pattern of ongoing cortical activitywith auditory category learning. Nature, 412:733–736.

39

O’Leary, D. D., Chou, S.-J., and Sahara, S. (2007). Area patterning of the mammalian cortex.Neuron, 56:252–269.

O’Leary, D. D., Stocker, A., and Zembrzycki, A. (2013). Area patterning of the mammaliancortex. In Rubenstein and Rakic (2013b), pages 61–85.

Oppenheim, R. W., Milligan, C., and Sun, W. (2010). Programmed cell death during nervoussystem development: Mechanisms, regulation, functions, and implications for neurobehav-ioral ontogeny. In Blumberg et al. (2010), pages 76–107.

Paillard, J. (1976). Reflexions sur l’usage du concept de plasticite en neurobiology. Journal dePsychologie Normale et Pathologique, 1:33–47.

Paille, V., Fino, E., Du, K., Morera-Herreras, T., Perez, S., Kotaleski, J. H., and Venance, L.(2013). GABAergic circuits control spike-timing-dependent plasticity. Journal of Neuro-science, 33:9353–9363.

Palmer, S. (1999). Vision Science – Photons to Phenomenology. MIT Press, Cambridge (MA).

Pascual-Leone, A. and Hamilton, R. (2001). The metamodal organization of the brain. Progressin Brain Research, 134:427–445.

Perfors, A., Tenenbaum, J. B., and Regier, T. (2011). The learnability of abstract syntacticprinciples. Cognition, 1418:306–338.

Piccinini, G. (2007). Computational modeling vs. computational explanation: Is everything aTuring Machine, and does it matter to the philosophy of mind? Australasian Journal ofPhilosoph, 85:93–115.

Pinker, S. (2002). The Blank Slate: The Modern Denial of Human Nature. Penguin, New York.

Plebe, A. (2007). A model of angle selectivity development in visual area V2. Neurocomputing,70:2060–2066.

Plebe, A. (2012). A model of the response of visual area V2 to combinations of orientations.Network: Computation in Neural Systems, 23:105–122.

Plebe, A., Mazzone, M., and De La Cruz, V. M. (2010). First words learning: A cortical model.Cognitive Computation, 2:217–229.

Prinz, J. (2002). Furnishing the Mind – Concepts and Their Perceptual Basis. MIT Press,Cambridge (MA).

Prinz, J. (2012). Beyond Human Nature – How Culture and Experience Shape the Human Mind.Norton & Co, New York.

Proulx, M. J. (2010). Synthetic synaesthesia and sensory substitution. Consciousness and Cog-nition, 19:501–503.

Proulx, M. J., Brown, D. J., Pasqualotto, A., and Meijer, P. (2014). Multisensory perceptuallearning and sensory substitution. Neuroscience and Biobehavioral Reviews, 41:16–25.

Pulvermuller, F., Garagnani, M., and Wennekers, T. (2014). Thinking in circuits: toward neuro-biological explanation in cognitive neuroscience. Biological Cybernetics, 108:573–593.

Quartz, S. R. (2003). Toward a developmental evolutionary psychology: Genes, development,and the evolution of the human cognitive architecture. In Scher, S. and Rauscher, F., editors,Evolutionary Psychology – Alternative Approaches, pages 185–210. Kluwer, Dordrecht(NL).

Rakic, P. (2008). Confusing cortical columns. Proceedings of the Natural Academy of ScienceUSA, 34:12099–12100.

40

Rakica, P., Ayoub, A. E., Breunig, J. J., and Dominguez, M. H. (2009). Decision by division:making cortical maps. Trends in Neuroscience, 32:291–301.

Ramon y Cajal, S. (1894). The croonian lecture: La fine structure des centres nerveux. Proceed-ings of the Royal Society of London, 55:444–468.

Ramon y Cajal, S. (1906). In DeFelipe, J. and Jones, E. G., editors, Cajal on the Cerebral Cortex:an Annotated Translation of the Complete Writings. Oxford University Press, Oxford (UK).1988.

Reali, F. and Christiansen, M. H. (2005). Uncovering the richness of the stimulus: Structuredependence and indirect statistical evidence. Cognitive Science, 29:1007–1028.

Rockel, A., Hiorns, R., and Powell, T. (1980). The basic uniformity in structure of the neocortex.Brain, 103:221–244.

Roe, A. W., Garraghty, P., Esguerra, M., and Sur, M. (1990). A map of visual space induced inprimary auditory cortex. Science, 250:818–820.

Roe, A. W., Garraghty, P., and Sur, M. (1987). Retinotectal W cell plasticity: experimentallyinduced retinal projections to auditory thalamus in ferrets. Soc. Neurosci. Abst., 13:1023.

Roelfsema, P. R., van Ooyen, A., and Watanabe, T. (2009). Perceptual learning rules based onreinforcers and attention. Trends in Cognitive Sciences, 14:64–71.

Romberg, A. R. and Saffran, J. R. (2010). Statistical learning and language acquisition. WileyInterdiscip Rev Cogn Sci, 1:906–914.

Roubertouxs, P. L., Jamon, M., and Carlier, M. (2010). Brain development: Genes, epigeneticevents, and maternal environments. In Blumberg et al. (2010), pages 51–75.

Rubenstein, J. L. R. and Rakic, P., editors (2013a). Comprehensive developmental neuroscience:neural circuit development and function in the healthy and diseased brain. Academic Press,New York.

Rubenstein, J. L. R. and Rakic, P., editors (2013b). Comprehensive developmental neuroscience:Patterning and Cell Type Specification in the Developing CNS and PNS. Academic Press,New York.

Ryder, D. (2004). SINBAD neurosemantics: A theory of mental representation. Minds andMachines, 19:211–240.

Sakurai, Y. (2014). Brain-machine interfaces can accelerate clarification of the principal myster-ies and real plasticity of the brain. Frontiers in Systems Neuroscience, 8:104.

Sasaki, Y., Nanez, J. E., and Watanabe, T. (2010). Advances in visual perceptual learning andplasticity. Nature Reviews Neuroscience, 11:53–60.

Schuster, C. M., Davis, G. W., Fetter, R. D., and Goodman, C. S. (1996). Genetic dissection ofstructural and functional components of synaptic plasticity. ii. fasciclin ii controls presy-naptic structural plasticity. Neuron, 17:655–667.

Sharma, J., Angelucci, A., and Sur, M. (2000). Induction of visual orientation modules in audi-tory cortex. Nature, 404:841–847.

Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., Greenstein, D.,Clasen, L., Evans, A., Rapoport, J. L., Giedd, J. N., and Wise, S. P. (2008). Neurodevelop-mental trajectories of the human cerebral cortex. Journal of Neuroscience, 28:3589–3594.

Shepherd, G. M. (1988). A basic circuit for cortical organization. In Gazzaniga, M. S., editor,Perspectives on Memory Research, pages 93–134. MIT Press, Cambridge (MA).

41

Shulz, D. and Feldman, D. (2013). Spike timing-dependent plasticity. In Rubenstein and Rakic(2013a), pages 155–181.

Sirois, S., Spratling, M., Thomas, M. S. C., Westermann, G., Mareschal, D., and Johnson, M. H.(2008). Preecis of neuroconstructivism: How the brain constructs cognition. Behavioraland Brain Science, 31:321–356.

Squire, L. and Kandel, E. (1999). Memory: from Mind to Molecules. Scientific AmericanLibrary, New York.

Srinivasan, S., Carlo, C. N., and Stevens, C. F. (2015). Predicting visual acuity from the structureof visual cortex. Proceedings of the Natural Academy of Science USA, 112:7815–7820.

Steeves, J. K. and Harris, L. R., editors (2013). Plasticity in sensory systems. Cambridge Uni-versity Press, Cambridge (UK).

Stettler, D. D., Yamahachi, H., Li, W., Denk, W., and Gilbert, C. D. (2006). Axons and synapticboutons are highly dynamic in adult visual cortex. Neuron, 49:877–887.

Stevens, J.-L. R., Law, J. S., Antolik, J., and Bednar, J. A. (2013). Mechanisms for stable,robust, and adaptive development of orientation maps in the primary visual cortex. JNS,33:15747–15766.

Stiles, J. (2011). Brain development and the nature versus nurture debate. In Braddick et al.(2011), pages 3–22.

Stiles, J., Reilly, J. S., Levine, S. C., Trauner, D. A., and Nass, R. (2012). Neural plasticityand cognitive development: insights from children with perinatal brain injury. OxfordUniversity Press, Oxford (UK).

Su, C.-Y., Menuz, K., and Carlson, J. R. (2009). Olfactory perception: Receptors, cells, andcircuits. Cell, 139:45–59.

Sur, M. (1989). Visual plasticity in the auditory pathway: Visual inputs induced into auditorythalamus and cortex illustrate principles of adaptive organization in sensory systems. In Dy-namic Interactions in Neural Networks: Models and Data, pages 35–52. Springer-Verlag,Berlin.

Sur, M. and Leamey, C. A. (2001). Development and plasticity of cortical areas and networks.Nature Reviews Neuroscience, 2:251–262.

Sur, M. and Rubenstein, J. L. R. (2005). Patterning and plasticity of the cerebral cortex. Science,310:805–810.

Swingley, D. (2010). Fast mapping and slow mapping in children’s word learning. LanguageLearning and Development, 6:179–183.

Trachtenberg, J. T. and Stryker, M. P. (2001). Rapid anatomical plasticity of horizontal connec-tions in the developing visual cortex. Journal of Neuroscience, 21:3476–3482.

Tritsch, N. X., Yi, E., Gale, J. E., Glowatzki, E., and Bergles, D. E. (2007). The origin ofspontaneous activity in the developing auditory system. Nature, 450:50–56.

Turrigiano, G. G. (2011). Too many cooks? intrinsic and synaptic homeostatic mechanisms incortical circuit refinement. Annual Review of Neuroscience, 34:89–103.

Turrigiano, G. G. and Nelson, S. B. (2004). Homeostatic plasticity in the developing nervoussystem. Nature Reviews Neuroscience, 391:892–896.

Ursino, M. and La Cara, G.-E. (2004). Comparison of different models of orientation selectivitybased on distinct intracortical inhibition rules. Vision Research, 44:1641–1658.

42

van Ooyen, A. (2001). Competition in the development of nerve connections: a review of models.Network: Computation in Neural Systems, 12:R1–R47.

Verhage, M., Maia, A. S., Plomp, J. J., Brussaard, A. B., Heeroma, J. H., Vermeer, H., Too-nen, R. F., Hammer, R. E., van den Berg, T. K., Missler, M., Geuze, H. J., and Sudhof,T. C. (2000). Synaptic assembly of the brain in the absence of neurotransmitter secretion.Science, 287:864–869.

Vogt, C. and Vogt, O. (1919). Allgemeine Ergebnisse unserer Hirnforschung. Journal Psychol.Neurol., 25:279–461.

von der Malsburg, C. (1973). Self-organization of orientation sensitive cells in the striate cortex.Kybernetic, 14:85–100.

von der Malsburg, C. (1995). Network self-organization in the ontogenesis of the mammalianvisual system. In Zornetzer, S. F., Davis, J., Lau, C., and McKenna, T., editors, An Intro-duction to Neural and Electronic Networks, pages 447–462. Academic Press, New York.(Second Edition).

von Economo, C. and Koskinas, G. N. (1925). Die Cytoarchitektonik der Hirnrinde des erwach-senen Menschen. Springer-Verlag, Berlin.

von Melchner, L., Pallas, S. L., and Sur, M. (2000). Visual behaviour mediated by retinal pro-jections directed to the auditory pathway. Nature, 404:871–876.

Wang, X., Merzenich, M. M., Sameshima, K., and Jenkins, W. M. (1995). Remodelling ofhand representation in adult cortex determined by timing of tactile stimulation. Nature,378:71–75.

Watt, A. J. and Desai, N. S. (2010). Homeostatic plasticity and STDP: keeping a neuron’s coolin a fluctuating world. Frontiers in Synaptic Neuroscience, 2:5.

Wattam-Bell, J., Birtles1, D., Nystrom, P., von Hofsten, C., Rosander, K., Anker, S., Atkinson,J., and Braddick, O. (2010). Reorganization of global form and motion processing duringhuman visual development. Current Biology, 20:411–415.

Weiskopf, D. A. (2008). The origins of concepts. Philosophical Studies, 140:359–384.

Wiesel, T. and Hubel, D. (1965). Binocular interaction in striate cortex of kittens reared withartificial squint. Journal of Neurophysiology, 28:1041–1059.

Will, B., Dalrymple-Alford, J., Wolff, M., and Cassel, J.-C. (2008). Reflections on the use ofthe concept of plasticity in neurobiology: Translation and adaptation by Bruno Will, JohnDalrymple-Alford, Mathieu Wolff and Jean-Christophe Cassel from J. Paillard, J Psychol1976. Behavioural Brain Research, 192:7–11.

Willshaw, D. J. and von der Malsburg, C. (1976). How patterned neural connections can be setup by self-organization. Proceedings of the Royal Society of London, B194:431–445.

Wilson, S. P., Law, J. S., Mitchinson, B., Prescott, T. J., and Bednar, J. A. (2010). Modeling theemergence of whisker direction maps in rat barrel cortex. PLoS ONE, 5:e8778.

Wonnacott, E. (2013). Learning: Statistical mechanisms in language acquisition. In Binder, P.and Smith, K., editors, The Language Phenomenon, pages 65–92. Springer-Verlag, Berlin.

Xu, H., Chen, M. F. Y. S. M. H., Zenisek, S. L. K. D., Zhou, Z. J., Tian, D. A. B. N., Picciotto,M. R., and Crair, M. C. (2011). An instructive role for patterned spontaneous retinal activityin mouse visual map development. Neuron, 71:1141–1152.

Zeki, S. (1974). Functional organization of a visual area in the posterior bank of the superiortemporal sulcus of the rhesus monkey. Journal of Physiology, 236:549–573.

43

Zeki, S. (2015). Area V5 – a microcosm of the visual brain. Frontiers in Integrative Neuro-science, 9:21.

Zhang, J., Ackman, J., Xu, H.-P., and Crair, M. C. (2011). Visual map development depends onthe temporal pattern of binocular activity in mice. Nature Neuroscience, 71:1141–1152.

Zhuo, M. and Hawkins, R. D. (1995). Long-term depression: A learning-related type of synapticplasticity in the mammalian central nervous system. Reviews in the Neurosciences, 6:259–277.

Zou, D., Feinstein, P., Rivers, A., Mathews, G., Kim, A., and Greer, C. (2004). Postnatal refine-ment of peripheral olfactory projections. Science, 304:1976–1979.

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