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For example, if a neuron was selective for ablack cross, but not a white cross, applica-tion of bicuculline disrupted this selectivityso that the neuron responded to both stim-uli (but not to others).

In a smaller number of cases, however,the preferred stimuli in the absence ofinhibition did not resemble the previouslypreferred stimulus in any clear way. Forexample, one neuron they recordedresponded well to only one picture (a toymonkey), but when bicuculline wasapplied, the neuron revealed selectiveresponses to six other stimuli, which bearlittle visual similarity, including picturesof a young man wearing glasses and of atomato. Importantly, several of the sixnewly effective stimuli were ones that asecond neuron, located nearby, respondedto in the absence of bicuculline.

Based on what is known about colum-nar organization in IT, one interpretationof these results is that bicuculline injectionsoften disrupted activity within a single col-umn. As neurons in a single columnrespond to features that usually differ alongdefined parameters, it is possible that neu-rons within a single column might inhibiteach other in such a way that they normallysharpen each other’s responses. Removinginhibition, therefore, would revealresponses to new objects along a relativelydefined parameter space. Cases whereblocking inhibition revealed responses toentirely new classes of objects might in turnreflect a block of inhibition between adja-cent columns, or the fact that some cells

improved by inhibition from nearbycolumns with a different orientation pref-erence9, but such detailed anatomical infor-mation is not available for IT.

Further experiments modeled on thosethat have been performed in V1 (includ-ing, for example, direct measurements ofexcitatory and inhibitory inputs11) shouldallow us to make distinctions betweenmodels of shape selectivity that includeintrinsic connections in IT to extend ourunderstanding of how selectivity for objectform is established in IT cortex.

1. Desimone, R., Albright, T. D., Gross, C. G. &Bruce, C. J. Neurosci. 4, 2051–2062 (1984).

2. Mishkin, M. Philos. Trans. R. Soc. Lond. B Biol.Sci. 298, 83–95 (1982).

3. Wang, Y. Fujita, I. & Murayama, Y. Nat.Neurosci. 3, 807–813 (2000).

4 Distler, C., Boussaoud, D., Desimone, R. &Ungerleider, L. G. J. Comp. Neurol. 334,125–150 (1993).

5. Erickson, C., Jagadeesh, B. & Desimone, R. inThe New Cognitive Neuroscience (ed.Gazzaniga, M. S.) 743–752 (MIT Press,Boston, 1999).

6. Fujita, I., Tanaka, K., Ito, M. & Cheng, K.Nature 360, 343–346 (1992).

7. Hubel, D. H. & Weisel, T. N. J. Physiol. (Lond.)160, 106–154 (1962).

8 Ferster, D. & Miller, K. D. Annu. Rev. Neurosci.23, 441–471 (2000).

9. Sillito, A. M., Kemp, J. A., Milson, J. A. &Berardi, N. Brain Res. 194, 517–520 (1980).

10. Braitenberg, V. & Schuz, A. Anatomy of theCortex (Springer, Berlin, 1991).

11. Ferster, D. J. Neurosci. 6, 1284–1301 (1986).

located near each another in IT can havevery different stimulus preferences(Jagadeesh, B. et al., Soc. Neurosci. Abstr. 24,594.13, 1998). A clear answer to this ques-tion will require more extensive experi-ments that carefully account for the extentof the bicuculline block. In V1, neurons in asingle cortical column receive most of theirinputs from neurons within the same corti-cal column10, and orientation selectivity is

Fig. 2. A comparison of therole of inhibition in V1 versusIT. In V1, orientation selectivityis first established by the geom-etry of afferent input from theLGN (not shown) but selectiv-ity in a particular neuron (c)may then be enhanced by inhibi-tion from nearby neurons tunedto orthogonal orientations (a, b);removal of the inhibition leads toa broadening of the orientationtuning curve of neuron c. In IT,neurons respond selectively toa subset of complex stimuli.When inhibition is disrupted,neurons lose some of thisselectivity and respond tosome stimuli that previouslydid not elicit a response.Often, objects that elicit aresponse in the presence ofbicuculline differ from the orig-inal object along a particularparameter, such as contrastpolarity (illustrated here).

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Optimizing coverage in thecortexAniruddha Das

Many features are mapped onto the primary visual cortex. A newstudy shows that these maps are arranged in a way that optimizesthe representation of each feature at each location in visual space.

Amy Center

As we look around our daily visual world,our ability to perceive different featuresseems obviously smooth with no localimbalances anywhere in our field of view.It is not the case, for example, that in onespot of our visual field we can distinguish

Aniruddha Das is at the Department ofNeurobiology, Rockefeller University, 1230 YorkAvenue, New York, New York 10021, USA.e-mail: das@rna.rockefeller.edu

red vertical objects more easily than greenhorizontal ones, with the reverse beingtrue at a neighboring spot. As with manyother aspects of the seemingly effortlessprocess of vision, it appears as though thiscouldn’t be otherwise. Only when we tryto understand the physiological under-pinnings of visual perception do we haveto stop and ask exactly what the layout ofthe relevant cortical circuitry must be inorder to provide this uniform coverage of

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nature neuroscience • volume 3 no 8 • august 2000 751

visual space and, further, how such cir-cuits develop into their mature form. Inthis issue of Nature Neuroscience, Swin-dale and coworkers1 present importantevidence that in the primary visual cor-tex, V1, the first stage of cortical process-ing of visual information, maximizinguniformity of coverage could be a guid-ing principle underlying the way that cor-tical circuitry is laid out and developedthrough infancy.

This issue of uniformity of coveragehas been studied extensively in V1—which is the first stage where such fea-tures as line orientation, direction ofmotion and disparity are extracted from avisual scene. Neurons in V1 are generallycharacterized by their receptive fields(RFs), circumscribed regions of spacewithin which simple visual stimuli drivethe neurons. The distribution of RFsacross V1 forms a (somewhat distorted)representation of visual space on the cor-tical surface. Within a RF, the stimulusthat induces the strongest response in thecorresponding neuron has aparticular constellation ofproperties: it is generally ashort line element of a partic-ular color, size and orientation,moving at a particular speed ina particular direction. Further,V1 neurons are activated todifferent extents by inputsthrough the left and right eye,a property known as oculardominance. Many of theseresponse properties—of ori-entation, direction, oculardominance, color, and so forthform continuous and periodicmaps over the V1 surface (Fig.1a) with, notably, the samescale of periodicity for everymap. Each neuron in V1 thusbelongs to multiple, overlaid,periodic maps in the differentresponse properties. This isbelieved to endow V1 withcomplete functional coverageof visual space, where everycortical area that ‘sees’ anypoint in space has equal num-bers of neurons devoted toeach representative combina-tion of response properties2.All points of a visual scene arethus analyzed uniformly byfunctionally equivalent mod-ules that extract the local ori-entation, direction, speed, etc.From the earliest years of dis-covering the existence of such

In the current paper, the authors havetaken an important first step of showing,empirically, that real experimentallyobtained maps from V1 are indeed opti-mally arranged with respect to each othersuch that any departure from the realmaps worsens the coverage. They first usedoptical imaging — a technique that allowsone to visualize the cortical activity in liv-ing animals — to obtain maps of orienta-tion, ocular dominance and spatialfrequency (a property akin to stimulussize) from cat V1. Because high-resolutionmaps of spatial position are not availableover large cortical areas, they assumed thatthe map of space is locally homogeneous.Next, they calculated the area of cortex,A(θ, k, E, x, y) that would be activated bya given stimulus of a particular orientation(θ), spatial frequency (k), eye dominance(E) and spatial position (x, y) by calculat-ing the cortical area that is the intersectionof the representation of each value (that is,θ, k, E, x and y) in its corresponding map(Fig 1a). If coverage were uniform, then

maps on V1, a further idea of optimalinterconnections has been very attractive,namely that these multiple maps may belocally interrelated in a manner thatallows for the shortest possible separa-tions between neurons of similar proper-ties, on the assumption that the cortexencodes stimuli through comparisonsbetween neighboring neurons expressingsimilar properties. If there were only twoindependent maps, each constrained tobe continuous, then we could optimallycombine exactly one cycle per map forevery region of space by superimposingthe maps so as to be locally orthogonal toeach other everywhere2 (Fig. 1b). Evenwith the multiple maps that are found onV1, many of the map features tend to belocally orthogonal to each other3,4 in amanner that, theoretically, does suggestoptimal coverage. Until the work bySwindale and colleagues1, however, thishypothesis of optimal coverage had notbeen tested through actual experimentalestimates of coverage.

Fig. 1. (a) Maps of the distribution of spatial position, orientation, ocular dominance and spatial frequency over agiven region of primary visual cortex (V1). The V1 region devoted to a particular (x, y) spatial region is indicated bythe red outline that incorporates the finite sizes of cortical receptive fields (RFs). The coverage is calculated asdescribed in the text for these maps and, again, after rotating or reflecting or shifting one or more of the maps withrespect to each other. (b) Two superimposed maps, constrained to be smooth and periodic, give both homogeneouscoverage and the shortest connections between neurons of similar properties when the maps are orthogonal toeach other. Consider two maps, for instance orientation (OR) and ocular dominance (OD), smooth and periodicwith similar spatial scales over cortex. Then superimpose them at right angles as shown. Every square region of cor-tex covering one period each in OR and OD then samples all possible combinations of the two parameters, withequal weight, thus giving homogeneous coverage. Next, consider any two nearby neurons ‘X’ less than one cycleaway from each other in the two paramenters. The geometric distance between the two neurons can be decompsedinto two orthogonal components, one along the axis of change in OR and the other along the axis of change in OD,giving the OR difference and OD difference, respectively, between the two neurons. Thus the physical distancebetween the two points on the cortical surface is exactly proportional to the Euclidean distance between the twopoints in the two-dimensional space of OR and OD. This is the smoothest way that neurons can be arranged suchthat neurons similar to each other in their responses are the shortest distance away from each other.

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752 nature neuroscience • volume 3 no 8 • august 2000

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this area (A) should be a constant inde-pendent of θ, k, E, x and y. They calculat-ed the variance of A over a representativeset of values for all parameters, and tookthis variance, normalized by the mean, asa measure of the departure from unifor-mity of coverage — the smaller the valueof this quantity (they refer to this as C′),the more homogeneous the coverage. Tosee how optimal the coverage was for themaps obtained, the authors then distort-ed the arrangement of maps by progres-sively rotating, mirror-reflecting or shiftingone or more maps with respect to the oth-ers. Each such relative motion introducedentirely new local interrelations betweenthe different maps at any cortical point,even though the total distributions of val-ues for each map remained the same. Typ-ically, they found that each step of relativemotion led to progressively increased val-ues of C′ over the original, implying thatany departure from the real maps wors-ened the coverage.

Such an experimental confirmation ofoptimal coverage has important conse-quences for theories of neural develop-ment. Neural circuits in V1 undergo anextensive process of refinement in theimmature brain, driven by patterned neur-al activity, before taking their adult form.The results of Swindale et al. give strongsupport to models of cortical developmentthat propose, as the principle behind neur-al refinement, mechanisms that optimizethe fitting of multiple maps on the two-dimensional cortical surface by minimiz-ing the axonal connection lengths betweenneurons of similar RF properties5.

The present results also suggest a num-ber of interesting directions in which theauthors’ exploration of coverage could beextended. The present authors have used

as those examined by Swindale and col-leagues, point to a dramatic expansion incell number between retina and V1.Unlike the retina, where each major classof retinal ganglion cells forms a sheet ofclose-packed RFs tiling visual space withlittle overlap8, V1 has a 100- to 1,000- foldmultiplicity in the number of neuronsrepresenting each point in space if theneurons were solely engaged in repre-senting the local orientation, direction,ocular dominance and other elementaryfeatures that arise de novo in cortex. Thisexpansion in cell number could beresolved by our growing understandingthat V1 cells are involved in many tasksbeyond just extracting local elementaryfeatures: V1 neurons combine these sim-ple elements to extract complex featuressuch as smooth contours or corners ortexture boundaries, are capable of modi-fying their responses as we get better atperceptual discrimination tasks, and evenadjust their responses depending on thecontext of a specific task9.

1. Swindale, N. V., Shoham, D., Grinvald, A.,Bonhoeffer,T. & Hübener, M. Nat. Neurosci. 3,822–826 (2000).

2. Hubel, D. H. & Wiesel, T. W. Proc. R. Soc.Lond. B 198, 1–59 (1977).

3. Obermayer, K. & Blasdel, G. G. J. Neurosci. 13,4114–4129 (1993).

4. Hübener, M., Shoham, D., Grinvald, A. &Bonhoeffer, T. J. Neurosci. 17, 9270–9284(1997).

5. Swindale, N. V. Network 7, 161–247 (1996).

6. White, L. E., Bosking, W. H., Wiliams, S. M. &Fitzpatrick, D. J. Neurosci. 19, 7089–7099(1999).

7. Das, A. & Gilbert, C. D. Nature 387, 594–598(1997).

8. Cleland, B. G., Levick, W. R. & Wässle, H. J. Physiol. (Lond.) 248, 151–171 (1975).

9. Gilbert, C. D. Physiol. Rev. 78, 467–485 (1998).

only ‘rigid body’ changes in the maps —where each map is shifted, rotated orreflected as a rigid object. But it is also pos-sible to imagine distortions of one mapwith respect to the other that treat the mapslike rubber sheets, locally stretching orcompressing them while maintaining theouter boundaries and the overall statisticsunchanged. Because the authors would liketo conclude that real maps are optimalwhen compared against any possible dis-tortion, it would be necessary to includesuch local distortions along with the rigidbody changes explored empirically by theauthors. Extending the range of relativechanges in maps to include these local dis-tortions should support and strengthen theauthors’ conclusions because local relativedistortions between different maps are like-ly to degrade the local relationships thatexist between the different maps, and thusworsen the uniformity of coverage overall.This should extend their results to obtain amathematically complete exploration of afull range of distortions.

Also, the authors have assumed that thebase map of space is uniform6, and that onthis map, the cortical area devoted to anypoint x, y is of uniform size independent ofits position. Some recent results suggest,however, that the map of space has period-ic local inhomogeneities mirroring singu-larities in the map of orientation7. Therefore,the intersection of the cortical area x, y withthe cortical area for orietation, ocular dom-inance or spatial frequency would be differ-ent from the intersection obtained byassuming a uniform map of x, y. It wouldbe valuable to know whether coverageimproves with such maps of retinal position.

Finally, estimates of the number of V1neurons required for optimal coverage ofthe elementary response properties, such

Mapping Drosophila olfactory axonsThe primary olfactory organs of Drosophila, the third antennal segment and the maxillarypalp, carry chemosensory bristles whose neurons project to glomeruli of the antennallobe, analogous to the vertebrate olfactory bulb. In mammals, neurons expressing thesame olfactory receptors converge onto the same glomerulus. Whether this is true forinsects is unclear: although individual glomeruli in bees respond to specific odors, themolecular structure of the insect olfactory system had not been explored. Chess andcolleagues (pages 780-785, this issue) now address this question by using transgenic fliesin which a marker gene is driven by the regulatory sequences that normally contrololfactory receptor gene expression. This allowed the authors to visualize the projections ofindividual olfactory neurons. Axons from neurons expressing a given olfactory receptorgene converged onto a small number of glomeruli, in a pattern that was invariant betweenindividuals. This stereotypical wiring pattern presumably underlies the formation of an odotopic map in the antennal lobe. Given thepower of Drosophila genetics, it should now be possible to determine how these connections are established and maintained.

Kalyani Narasimhan

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