Portal de Periódicos da CAPES

From Functional Architecture to Functional Connectomics

2012; Cell Press; Volume: 75; Issue: 2 Linguagem: Inglês

10.1016/j.neuron.2012.06.031

1097-4199

R. Clay Reid,

Photoreceptor and optogenetics research

"Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex" by Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar reported several important discoveries: orientation columns, the distinct structures of simple and complex receptive fields, and binocular integration. But perhaps the paper's greatest influence came from the concept of functional architecture (the complex relationship between in vivo physiology and the spatial arrangement of neurons) and several models of functionally specific connectivity. They thus identified two distinct concepts, topographic specificity and functional specificity, which together with cell-type specificity constitute the major determinants of nonrandom cortical connectivity. Orientation columns are iconic examples of topographic specificity, whereby axons within a column connect with cells of a single orientation preference. Hubel and Wiesel also saw the need for functional specificity at a finer scale in their model of thalamic inputs to simple cells, verified in the 1990s. The difficult but potentially more important question of functional specificity between cortical neurons is only now becoming tractable with new experimental techniques. "Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex" by Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar reported several important discoveries: orientation columns, the distinct structures of simple and complex receptive fields, and binocular integration. But perhaps the paper's greatest influence came from the concept of functional architecture (the complex relationship between in vivo physiology and the spatial arrangement of neurons) and several models of functionally specific connectivity. They thus identified two distinct concepts, topographic specificity and functional specificity, which together with cell-type specificity constitute the major determinants of nonrandom cortical connectivity. Orientation columns are iconic examples of topographic specificity, whereby axons within a column connect with cells of a single orientation preference. Hubel and Wiesel also saw the need for functional specificity at a finer scale in their model of thalamic inputs to simple cells, verified in the 1990s. The difficult but potentially more important question of functional specificity between cortical neurons is only now becoming tractable with new experimental techniques. It is useful to approach the topic of synaptic connections in the cortex by considering three distinct types of specificity: topographic specificity (where you are), cell-type specificity (who you are), and functional specificity (what you do; Lee and Reid, 2011Lee W.C. Reid R.C. Specificity and randomness: structure-function relationships in neural circuits.Curr. Opin. Neurobiol. 2011; 21: 801-807Crossref PubMed Scopus (16) Google Scholar). Recent technical advances have accelerated progress in understanding cell-type and, to a lesser extent, functional specificity, but it is useful to begin with the better understood topic of cortical topography, or functional architecture. Building upon the revolutionary findings of Vernon Mountcastle, who in 1957 proposed that narrow vertical columns of neurons are the fundamental unit in cortical processing (Mountcastle, 1957Mountcastle V.B. Modality and topographic properties of single neurons of cat's somatic sensory cortex.J. Neurophysiol. 1957; 20: 408-434PubMed Google Scholar), Hubel and Wiesel introduced the term "functional architecture" to describe the relationship between anatomy and physiology in cortical circuits. A common textbook description of functional architecture is that receptive fields in a cortical column are all extremely similar. Instead, Hubel and Wiesel gave a more nuanced treatment of functional architecture in the visual cortex. They proposed that a cortical column can be very homogeneous for some receptive-field attributes, loosely organized for others, and even completely disorganized in yet other respects. One aspect of functional architecture in the cat visual cortex, the orientation column, is indeed monolithic. As Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar wrote, "It can be concluded that the striate cortex is divided into discrete regions within which the cells have a common receptive-field axis orientation." But the second aspect, ocular dominance, is more loosely organized in columns. As they said later in the paper, "While ... cells of different ocular dominance were present within single columns, there were nevertheless indications of some grouping" (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar). Ocular dominance columns were later found to be clearer and more distinct in the monkey (Hubel and Wiesel, 1968Hubel D.H. Wiesel T.N. Receptive fields and functional architecture of monkey striate cortex.J. Physiol. 1968; 195: 215-243Crossref PubMed Scopus (4418) Google Scholar). Finally, they found that retinotopic organization of a column is disorganized, so that "at this microscopic level the retinotopic representation no longer strictly holds" (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar). As Hubel and Wiesel pointed out, the fine-scale functional architecture of visual cortex, with its homogeneous orientation selectivity and disorganized retinotopy, might play an important role in information processing."At first sight it would seem necessary to imagine a highly intricate tangle of interconnexions in order to link cells with common axis orientations while keeping those with different orientations functionally separated ... The cells of each aggregate have common axis orientations and the staggering in the positions of the simple fields is roughly what is required to account for the size of most of the complex fields" (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar). It is crucially important to emphasize that Hubel and Wiesel did not intend functional architecture to be synonymous with the existence of distinct columns for orientation selectivity. It was instead a general construct to help understand the relationship between function and anatomy. The term functional architecture might be used to express simple ideas: neurons with the same preferred orientation clump together. But it also encompassed more complex ideas: a precise map for orientation combined with an imprecise map for retinotopy might help in the construction of complex receptive fields. Taken more generally, the concept of functional architecture provided a framework for linking the anatomy of a cortical circuit with the physiological transformations performed by that circuit. But the exact relationship between functional architecture, neural connections, and the physiological function of individual cells could only be speculated upon in 1962. Hubel and Wiesel could put forward their hierarchical models of simple and complex receptive fields in the cat (Figure 1), but these models were presented as conjecture: simple cells might create orientation selectivity by adding synaptic signals from lateral geniculate nucleus (LGN) cells whose receptive fields line up in a row; complex cells might generalize orientation selectivity by adding synaptic signals from simple cells tuned to a single orientation. But only recently is it becoming possible to study the detailed interrelationships between physiology and wiring diagrams at the single-cell level, a line of inquiry that has been given a new name, functional connectomics (a term that would have made Hubel and Wiesel shudder in 1962). The term connectomics has been used in several different ways since it first appeared 7 years ago. As originally defined by Sporns et al., 2005Sporns O. Tononi G. Kötter R. The human connectome: A structural description of the human brain.PLoS Comput. Biol. 2005; 1: e42Crossref PubMed Scopus (2131) Google Scholar, it is "a comprehensive structural description of the network of elements and connections forming the human brain," which could be considered either at a "macroscale [of] brain regions and pathways" or a "microscale [of] single neurons and synapses." On the one hand, there is the network of brain areas, as in the Human Brain Connectome Project, in which MRI is used to trace projection pathways (Van Essen et al., 2012Van Essen D.C. Ugurbil K. Auerbach E. Barch D. Behrens T.E. Bucholz R. Chang A. Chen L. Corbetta M. Curtiss S.W. et al.WU-Minn HCP ConsortiumThe Human Connectome Project: A data acquisition perspective.Neuroimage. 2012; (Published online February 17, 2012)https://doi.org/10.1016/j.neuroimage.2012.02.018Crossref Scopus (1274) Google Scholar). But there is also the field of synaptic networks between individual neurons, which is typified by the use of large-scale electron microscopy (EM) to study local networks (Lichtman and Sanes, 2008Lichtman J.W. Sanes J.R. Ome sweet ome: what can the genome tell us about the connectome?.Curr. Opin. Neurobiol. 2008; 18: 346-353Crossref PubMed Scopus (152) Google Scholar). Another potential source of confusion is that the word itself implies comprehensiveness, but it has also been used to describe studies of networks that are only sparsely reconstructed (Seung, 2011Seung H.S. Neuroscience: Towards functional connectomics.Nature. 2011; 471: 170-172Crossref PubMed Scopus (43) Google Scholar). It would therefore be useful to have a word that denotes the less exalted study of neural connectivity with modern tools. But in modern biology, very few "-ologies" are being coined, while a new "-omics" appears almost every month. So we are left with the term connectomics, a term that exemplifies the long-term aspirations of a field but that for now can also refer to rapidly improving anatomical methods for studying neural connections. Functional connectomics is a more specific term that describes studies of neuronal networks in which physiological measurements help us understand connections and vice versa (Seung, 2011Seung H.S. Neuroscience: Towards functional connectomics.Nature. 2011; 471: 170-172Crossref PubMed Scopus (43) Google Scholar). As such, it captures the ideas in the following quote from Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar:"At present we have no direct evidence on how the cortex transforms the incoming visual information. Ideally, one should determine the properties of a cortical cell, and then examine one by one the receptive fields of all the afferents projecting upon that cell. In the lateral geniculate, where one can, in effect, record simultaneously from a cell and one of its afferents, a beginning has already been made in this direction (Hubel and Wiesel, 1961Hubel D.H. Wiesel T.N. Integrative action in the cat's lateral geniculate body.J. Physiol. 1961; 155: 385-398Crossref PubMed Scopus (451) Google Scholar)" (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar). But in 1962, to study the cortex in this manner was virtually unimaginable, due to technical limitations."In a structure as complex as the cortex the techniques available would seem hopelessly inadequate for such an approach. Here we must rely on less direct evidence to suggest possible mechanisms for explaining the transformations that we find" (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar). Fortunately, in the ensuing 50 years, the techniques for measuring neural activity and for tracing synaptic connections have advanced considerably. From work over the past 25 years, primarily from cortical slices in vitro, we now have a detailed understanding of the overall architecture of cortical circuits: cell types and their laminar organization, dendritic and axonal morphology, and the outlines of a wiring diagram. The cellular biophysics of cortical neurons has been correlated to different cell types with great specificity (Sugino et al., 2006Sugino K. Hempel C.M. Miller M.N. Hattox A.M. Shapiro P. Wu C. Huang Z.J. Nelson S.B. Molecular taxonomy of major neuronal classes in the adult mouse forebrain.Nat. Neurosci. 2006; 9: 99-107Crossref PubMed Scopus (432) Google Scholar; Nelson et al., 2006Nelson S.B. Sugino K. Hempel C.M. The problem of neuronal cell types: a physiological genomics approach.Trends Neurosci. 2006; 29: 339-345Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar). The connections between neurons have also been well characterized with an increasing emphasis on the relationship between connectivity, cell types, and anatomy. There are now many examples of stereotyped connections between different neuronal types—excitatory neurons synapse onto the cell bodies of inhibitory neurons but avoid excitatory somata, chandelier cells form synapses exclusively onto the axon initial segments of pyramidal cells, and gap junctions are made between inhibitory neurons of a single class (reviewed in Brown and Hestrin, 2009Brown S.P. Hestrin S. Cell-type identity: a key to unlocking the function of neocortical circuits.Curr. Opin. Neurobiol. 2009; 19: 415-421Crossref PubMed Scopus (57) Google Scholar). Of course, the question of what defines a cortical cell type has not been settled (Nelson et al., 2006Nelson S.B. Sugino K. Hempel C.M. The problem of neuronal cell types: a physiological genomics approach.Trends Neurosci. 2006; 29: 339-345Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar; Ascoli et al., 2008Ascoli G.A. Alonso-Nanclares L. Anderson S.A. Barrionuevo G. Benavides-Piccione R. Burkhalter A. Buzsáki G. Cauli B. Defelipe J. Fairén A. et al.Petilla Interneuron Nomenclature GroupPetilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.Nat. Rev. Neurosci. 2008; 9: 557-568Crossref PubMed Scopus (1050) Google Scholar). In particular, when might differences in the functional properties of neurons, or their patterns of connections, be caused by unidentified distinctions between cell classes or patterns of gene expression? A great simplifying assumption has been that neurons of a given class are all equivalent. In this case, the only thing we need to know about a neuron is its class and anatomical location, for instance, a pyramidal cell at the bottom of layer 2/3 in primary visual cortex, and the anatomical extent of its dendrites and axons. If this were the case, we would only need to know the generic structure of the microcircuit, plus the range of in vivo functional properties of the afferents that impinge upon the circuit, to begin modeling its in vivo physiology. A corollary of this assumption—that cortical neurons of a given class are identical—is that connections between neurons are nonspecific, or random other than cell-type specificity. The strongest formulation of this idea has become known as Peters' Rule (Braitenberg and Schüz, 1998Braitenberg V. Schüz A. Cortex: Statistics and Geometry of Neuronal Connectivity.Second Edition. Springer, Berlin1998Crossref Google Scholar), "The distribution of synapses from various origins ... on the dendritic tree of any one neuron reflect[s] simply the availability of those presynaptic elements in the tissue ... Conversely, the postsynaptic partners of any axonal tree would simply reflect the distribution of the postsynaptic elements." Although this point of view was quite influential, it is becoming increasingly clear that connections between cortical neurons are far from random. Instead, there are several lines of evidence showing that connections between cortical neurons can be highly specific, both because of cell-type-specific connections as well as other, more poorly understood factors (Yoshimura et al., 2005Yoshimura Y. Dantzker J.L. Callaway E.M. Excitatory cortical neurons form fine-scale functional networks.Nature. 2005; 433: 868-873Crossref PubMed Scopus (453) Google Scholar; Song et al., 2005Song S. Sjöström P.J. Reigl M. Nelson S. Chklovskii D.B. Highly nonrandom features of synaptic connectivity in local cortical circuits.PLoS Biol. 2005; 3: e68Crossref PubMed Scopus (1021) Google Scholar; Perin et al., 2011Perin R. Berger T.K. Markram H. A synaptic organizing principle for cortical neuronal groups.Proc. Natl. Acad. Sci. USA. 2011; 108: 5419-5424Crossref PubMed Scopus (435) Google Scholar). In order to discuss structure in a cortical network, it is useful to consider three broad classes of specificity: topographic specificity, cell-type specificity, and functional specificity (Lee and Reid, 2011Lee W.C. Reid R.C. Specificity and randomness: structure-function relationships in neural circuits.Curr. Opin. Neurobiol. 2011; 21: 801-807Crossref PubMed Scopus (16) Google Scholar). Topographic specificity is seen, for instance, when axons respect a laminar boundary or a functional map, such as for retinotopy or preferred orientation (Mooser et al., 2004Mooser F. Bosking W.H. Fitzpatrick D. A morphological basis for orientation tuning in primary visual cortex.Nat. Neurosci. 2004; 7: 872-879Crossref PubMed Scopus (59) Google Scholar). If Peters' rule holds, then topographic specificity alone specifies the wiring diagram. Cell-type specificity, as discussed above, describes cases in which an axon has tropism for a given class of neurons found amidst a mixed local population. Functional specificity can be defined as any form of synaptic specificity that cannot be explained by axonal and dendritic topography, cell types, or perhaps even gene expression but instead must relate to the physiology of the pre- and postsynaptic cells. A more accurate term might therefore be local functional connectivity or even local epigenetic specificity. The three types of specificity are of course not perfectly delineated; they nonetheless serve as useful abstractions until we have a better understanding of molecular and activity-dependent influences on neuronal connectivity. The LGN is a particularly well-studied example in which topographic specificity plays some role, but functional specificity comes to dominate the local wiring diagram. The retinal input to the thalamus is one of the classic models for the segregation of inputs into both eye-specific layers and retinotopic maps. But even after topographic segregation of axonal arbors is complete, midway through development, there is further synaptic refinement (Tavazoie and Reid, 2000Tavazoie S.F. Reid R.C. Diverse receptive fields in the lateral geniculate nucleus during thalamocortical development.Nat. Neurosci. 2000; 3: 608-616Crossref PubMed Scopus (83) Google Scholar; Chen and Regehr, 2000Chen C. Regehr W.G. Developmental remodeling of the retinogeniculate synapse.Neuron. 2000; 28: 955-966Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar). At the end of development, there is a very specific network in which multiple overlapping axons make synaptic contact onto distinct and very specific targets. This was demonstrated in a serial-section EM study (Hamos et al., 1987Hamos J.E. Van Horn S.C. Raczkowski D. Sherman S.M. Synaptic circuits involving an individual retinogeniculate axon in the cat.J. Comp. Neurol. 1987; 259: 165-192Crossref PubMed Scopus (176) Google Scholar) that 25 years later remains the clearest anatomical illustration of functional specificity in central circuits. As discussed below, and as elaborated in an extraordinary review of the relationship between connectivity and visual function (Cleland, 1986Cleland B.G. The dorsal lateral geniculate nucleus of the cat.in: Pettigrew J.D. Sanderson K.S. Levick W.R. Visual Neuroscience. Cambridge University Press, London1986: 111-120Google Scholar), the mature wiring diagram between retina and LGN must have a crystalline underlying structure based on the geometric tiling of retinal receptive fields. The relationship between cortical wiring and visual function, however, is far more complicated. The generation of orientation-selective visual responses in the cortex is one of the classic problems in visual neuroscience. Neurons in the visual thalamus (the LGN) respond relatively indiscriminately to stimuli of different orientations, while their postsynaptic targets in the cortex can be exquisitely selective. In the first of their two models of function and connectivity, Hubel and Wiesel outlined how precise connections between thalamus and cortex could generate the orientation-selective responses of cortical simple cells (Figure 1A). In the most famous figure of the 1962 paper, they proposed that LGN cells whose receptive fields were arranged in a row converge onto a simple cell whose receptive field was elongated with the same orientation (Figure 1A). As it turned out, this class of model could be proven with 20th century electrophysiology. In the 1990s, evidence for this model accumulated (Chapman et al., 1991Chapman B. Zahs K.R. Stryker M.P. Relation of cortical cell orientation selectivity to alignment of receptive fields of the geniculocortical afferents that arborize within a single orientation column in ferret visual cortex.J. Neurosci. 1991; 11: 1347-1358PubMed Google Scholar; Reid and Alonso, 1995Reid R.C. Alonso J.M. Specificity of monosynaptic connections from thalamus to visual cortex.Nature. 1995; 378: 281-284Crossref PubMed Scopus (468) Google Scholar; Ferster et al., 1996Ferster D. Chung S. Wheat H. Orientation selectivity of thalamic input to simple cells of cat visual cortex.Nature. 1996; 380: 249-252Crossref PubMed Scopus (418) Google Scholar; Priebe and Ferster, 2012Priebe N.J. Ferster D. Mechanisms of neuronal computation in mammalian visual cortex.Neuron. 2012; 75 (this issue): 194-208Abstract Full Text Full Text PDF PubMed Scopus (132) Google Scholar). Two lines of evidence gave strong indirect support to the model without needing to examine individual connections from thalamus to cortex. First, Chapman et al., 1991Chapman B. Zahs K.R. Stryker M.P. Relation of cortical cell orientation selectivity to alignment of receptive fields of the geniculocortical afferents that arborize within a single orientation column in ferret visual cortex.J. Neurosci. 1991; 11: 1347-1358PubMed Google Scholar silenced the cortex so that action potentials from individual thalamic axons could be recorded. They found that in the ferret, LGN axons that projected to a single column had receptive fields that lined up in a row (Chapman et al., 1991Chapman B. Zahs K.R. Stryker M.P. Relation of cortical cell orientation selectivity to alignment of receptive fields of the geniculocortical afferents that arborize within a single orientation column in ferret visual cortex.J. Neurosci. 1991; 11: 1347-1358PubMed Google Scholar; see Jin et al., 2011Jin J. Wang Y. Swadlow H.A. Alonso J.M. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex.Nat. Neurosci. 2011; 14: 232-238Crossref PubMed Scopus (124) Google Scholar). Ferster et al., 1996Ferster D. Chung S. Wheat H. Orientation selectivity of thalamic input to simple cells of cat visual cortex.Nature. 1996; 380: 249-252Crossref PubMed Scopus (418) Google Scholar examined the same question from the standpoint of a single neuron rather than a single column. They found that in the cat, the orientation selectivity of a cortical neuron did not change when the cortex was silenced: thus, the orientation selectivity of the thalamic input alone matched that of the neuron in the unperturbed circuit. To examine the functional logic of individual connections between a pair of neurons, however, it was necessary to study their receptive fields and connections a pair at a time, as Hubel and Wiesel suggested. In the 1960s, this was possible within the LGN (Hubel and Wiesel, 1961Hubel D.H. Wiesel T.N. Integrative action in the cat's lateral geniculate body.J. Physiol. 1961; 155: 385-398Crossref PubMed Scopus (451) Google Scholar) but later it became possible for the thalamocortical projection with the technique of cross-correlation (see below; Tanaka, 1983Tanaka K. Cross-correlation analysis of geniculostriate neuronal relationships in cats.J. Neurophysiol. 1983; 49: 1303-1318PubMed Google Scholar; Reid and Alonso, 1995Reid R.C. Alonso J.M. Specificity of monosynaptic connections from thalamus to visual cortex.Nature. 1995; 378: 281-284Crossref PubMed Scopus (468) Google Scholar). The second model (for complex cells, Figure 1D) addressed the much more difficult problem of how intracortical circuitry might transform sensory information, although some progress with this model has been made with conventional electrophysiology (Alonso and Martinez, 1998Alonso J.M. Martinez L.M. Functional connectivity between simple cells and complex cells in cat striate cortex.Nat. Neurosci. 1998; 1: 395-403Crossref PubMed Scopus (195) Google Scholar). In Hubel and Wiesel's complex cell model (Figure 1D), a difficult problem, of receiving inputs from simple cells with one preferred orientation, was solved by the orientation column. It was transformed from a problem that might require fine-scale functional specificity to one that was solved by topography. To quote the key passage again, "At first sight it would seem necessary to imagine a highly intricate tangle of interconnexions in order to link cells with common axis orientations … [but] gathered together in discrete columns are the very cells we require to be interconnected in our scheme" (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar). Without orientation columns, in the mouse, it is necessary to imagine this "highly intricate tangle of interconnexions," a phrase that can serve as perhaps the best definition of functional specificity (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (8604) Google Scholar). Hubel and Wiesel's simple-cell model (Figure 1A) relies on functional specificity. To first approximation, in the cat visual cortex, the axons of on-center and off-center LGN cells are intermingled in layer 4 (but see Jin et al., 2011Jin J. Wang Y. Swadlow H.A. Alonso J.M. Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex.Nat. Neurosci. 2011; 14: 232-238Crossref PubMed Scopus (124) Google Scholar). Therefore, the precise arrangement of receptive fields of on- or off-center LGN inputs to a single simple cell cannot be explained simply by a random sampling of local thalamocortical axons (unless the number of LGN afferents to a simple cell are assumed to be unrealistically low; Ringach, 2004Ringach D.L. Haphazard wiring of simple receptive fields and orientation columns in visual cortex.J. Neurophysiol. 2004; 92: 468-476Crossref PubMed Scopus (66) Google Scholar). Hubel and Wiesel's complex cell model (Figure 1D), however, relies primarily on topographic specificity. Because of the functional architecture of the cat, in which orientation is homogeneous in a column but receptive fields are spatially scattered, complex cells can be built by indiscriminate pooling of local simple cells. This fundamental difference between the two models creates some difficulty in thinking about them. In particular, the existence of functional architecture confounds the two potential mechanisms of topographic specificity and functional specificity. For instance, in two species, there is strong evidence that topographic specificity, rather than (local) functional specificity, can help account for the generation of orientation specificity. In the ferret, as noted above, the LGN cells projecting to a single column have receptive fields that line up in a row whose orientation matches that of the local cortical neurons (Chapman et al., 1991Chapman B. Zahs K.R. Stryker M.P. Relation of cortical cell orientation selectivity to alignment of receptive fields of the geniculocortical afferents that arborize within a single orientation column in ferret visual cortex.J. Neurosci. 1991; 11: 1347-1358PubMed Google Scholar). Thus, cortical orientation selectivity can be achieved by nonspecific summation of the locally available afferents. In the tree shrew, there is a similar arrangement, except it is caused by anisotropic intracortical projection of axons. In the tree shrew, layer 4 neurons are not orientation selective, so orientation selectivity is generated first in layer 2/3 but otherwise the arrangement is similar