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Receptive field properties and sensitivity to edges definedby motion in the postero-lateral lateral suprasylvian(PLLS) area of the cat

N. Robitaillea, F. Leporea, B.A. Bacona,b, D. Ellemberga,c, J.-P. Guillemota,d,⁎aCentre de Recherche en Neuropsychologie et Cognition, Département de Psychologie, Université de Montréal, Montréal, Québec, CanadabDepartment of Psychology, Bishop's University, Sherbrooke, Québec, CanadacDépartement de Kinésiologie, Université de Montréal, Québec, CanadadDépartement de Kinanthropologie, Université du Québec à Montréal, Montréal, Canada

A R T I C L E I N F O

⁎ Corresponding author. Département de KinCanada H3C 3P8. Fax: +1 514 343 5787.

E-mail address: guillemot.jean-paul@uqaAbbreviations: c./degree, cycle per degree;

PLLS, postero-lateral lateral suprasylvian; PM

0006-8993/$ – see front matter © 2007 Elsevidoi:10.1016/j.brainres.2007.10.031

A B S T R A C T

Article history:Accepted 10 October 2007Available online 22 October 2007

The present study investigated the spatial properties of cells in the postero-lateral lateralsuprasylvian (PLLS) area of the cat and assessed their sensitivity to edges defined bymotion.A total of one hundred and seventeen (117) single units were isolated. First, driftingsinusoidal gratingswere used to assess the spatial properties of the cells' receptive fields andto determine their spatial frequency tuning functions. Second, random-dot kinematogramswere used to create illusory edges by drifting textured stimuli (i.e. a horizontal bar) against asimilarly textured but static background. Almost all the cells recorded in PLLS (96.0%) werebinocular, and a substantialmajority of receptive fields (79.2%)were end-stopped.Most units(81.0%) had band-pass spatial frequency tuning functions and responded optimally to lowspatial frequencies (mean spatial frequency: 0.08 c./degree). The remaining units (19.0%)were low-pass. All the recorded cells responded vigorously to edges defined by motion. Thevast majority (96.0%) of cells responded optimally to large texture elements; approximatelyhalf the cells (57.3%) also responded to finer texture elements. Moreover, 38.5% of the cellswere selective to the width of the bar (i.e., the distance between the leading and the trailingedges). Finally, some (9.0%) cells responded in a transient fashion to leading and to trailingedges. In conclusion, cells in the PLLS area are low spatial frequency analyzers that aresensitive to texture and to the distance between edges defined by motion.

© 2007 Elsevier B.V. All rights reserved.

Keywords:ExtrastriateKinematogramPLLSSpatial frequencyTexture

1. Introduction

The visual system can quickly detect and identify objects innatural scenes, even though these scenes are usually composedof varied, complex and often degraded visual information.

anthropologie, Universit

m.ca (J.-P. Guillemot).cd/m2, Candela per meterLS, postero-medial latera

er B.V. All rights reserved

Perceiving an object as a distinct entity requires that the visualsystem segregate that object from the rest of the scene bydefining its boundaries or edges. For example, an object may besegregated from its background based on differences in lumi-nance, color, spatial disparity, texture, and direction of motion

é du Québec à Montréal, C.P. 8888, Succ. Centre-Ville, Montréal,

squared; Imp./s, impulses per second; MT, middle temporal area;l suprasylvian; RF, receptive field; S.D., standard deviation

.

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(Julesz, 1971; Nothdurft, 1993). One of the most salient cues forfigure–ground segregation is relative motion between an objectand itsbackground (Nothdurft, 1993). This isdemonstratedby thefact that several speciesof preyhaveevolveda “freezing” strategyso as to eliminate this cue and escape the detection of predators.

In cats, the cortical areas surrounding the lateral suprasyl-vian sulcus are known to be involved in the processing of mo-tion information (Lomber et al., 1996; Spear, 1991). A number ofstudies suggest that these areas are implicated in attentionshifts (Ogasawara et al., 1984;HardyandStein, 1988; Payne et al.,1996), speed discrimination (Pasternak et al., 1989), the integra-tion of complex motion (Rudolph and Pasternak, 1996), and thedetection of forms that are in motion (Kiefer et al., 1989; Krügeret al., 1993). The lateral suprasylvian cortex consists of six areas,each containing an independent retinotopic map of the visualfield (Palmer et al., 1978).

Research on the lateral suprasylvian cortex has largelyfocused on the postero-medial lateral suprasylvian area(PMLS). The receptive field properties of cells in PMLS havebeen extensively studied (Camarda and Rizzolatti, 1976; Hubeland Wiesel, 1969; Rauschecker et al., 1987a,b; Spear andBaumann, 1975; von Grünau et al., 1987; Zumbroïch et al.,1986), and the spatio-temporal properties of these cells havebeen well defined (Morrone et al., 1986; Zumbroïch andBlakemore, 1987; Zumbroïch et al., 1988; Guido et al., 1990).The role of PMLS cells with regards to motion perception hasbeen well established (Morrone et al., 1986; Blakemore andZumbroïch, 1987; Rauschecker et al., 1987a; von Grünau andFrost, 1983; Yin and Greenwood, 1992) and this area is usuallyconsidered homologous to area V5 (MT) of the macaque brain,the acknowledged motion center in primates (Payne, 1993).

Much less is known about the postero-lateral lateral su-prasylvian cortex (PLLS). It is generally accepted that this area,like PMLS, contributes to motion analysis but the specificresponses of cells in this area and the exact role they play inthis process remains poorly defined.

Zumbroïch et al. (1986) highlighted an important differencebetweenPLLSandPMLS. In thePLLSarea, theuppervisual field isover-represented and a large proportion of neurons extend theirreceptive fields into the ipsilateral visual field (up to 28 degreesfrom the vertical meridian). Furthermore, Rauschecker et al.(1987a) reported a centrifugal–centripetal organization of thereceptive fields' optimal direction in PLLS; they link this type oforganization with the analysis of expanding stimuli.

At the neuroanatomical level, the PLLS area receives bothdirect and indirect Y inputs from the dorsal lateral geniculatenucleus (Rauschecker et al., 1987b; Raczkowski and Rosenquist,1983). This Ypathway is associatedwith thedetection ofmotion(Stone et al., 1979; Khayat et al., 2000) and the coarse analysis ofform (Stone, 1983). PLLS neurons also share strong reciprocalconnections with the anterior ectosylvian area (Scannell et al.,1995), a higher-order cortical region that contributes to the a-nalysis of motion (Benedek et al., 1988; Scannell et al., 1996).Furthermore, PLLS cells send outputs to neurons in the super-ficial, the intermediate and the deep layers of the superiorcolliculus (Kawamura and Hashikawa, 1978; Niida et al., 1997;Brecht et al., 1998), a structure associated with ocular move-ments, fixation, and orienting behavior (Roucoux and Cromme-linck, 1976; Stein, 1978). Moreover, PLLS cells discharge aftervoluntary ocular movements (Komatsu et al., 1983), and

electrical stimulation of these cells provokes convergent ocularmovements (Toda et al., 2001).

One study systematically investigated the spatial andtemporal properties of cells in area PLLS (Zumbroïch andBlakemore, 1987). Based on a very small sample (13 cells), theyconcluded that PLLS cells are spatially similar to those of PMLS,but that they tend to prefer higher temporal frequencies.

Recently, Li et al. (2000) have shown that the vast majority(90.0%) of PLLS cells responded to optic flow patterns, althoughonly 20–25% of the cells were selective to certain types of opticflow stimuli (i.e., translation, rotation, or expansion–contrac-tion). This is consistentwith the report that themajority of cellsin the lateral suprasylvian cortex respond preferentially to opticflowmovies rather than toequivalent texturemovies (Kimetal.,1997). Moreover, Sherk et al. (1997) have shown that cells in thePLLS area respond preferentially to objects moving against anoptic flowmovie rather than to a bar moving against a uniformbackground. Together, these findings suggest that the PLLS areaconstitutes an intermediate stage of processing for optic flowfields. There is also indirect evidence suggesting that the PLLScortex is involved in figure–ground segmentation. The PLLS/PMLS border receives dense heterotopic callosal connectionsfrom the 17/18 border (Innocenti et al., 1995; Bressoud andInnocenti, 1999), and according to Innocenti et al. (1995), thesecallosal connections play a role in figure–ground segregation.

The contribution of motion-cues to figure–ground segrega-tion has mainly been investigated with two types of texture-based stimuli. The first, a static form, is defined solely by themotion of a subset of elements (visual noise or textons) withinfixed spatial coordinates (Frost, 1985; Gulyas et al., 1987, 1990).The second, similar to the stimuli used in the present study,consists of a subset of elements that move coherently within abackground of static elements. In this case, if the elementsbecome static, the form disappears and a completely uniformfield of dots is perceived. This type of motion-defined form canbe detected by neurons in the cat's dorsal lateral geniculatenucleus (Mason, 1976) and area 17 (Hammond and MacKay,1977), as well as in the monkey primary visual cortex (Bourneet al., 2002) and MT area (Olavarria et al., 1992; Marcar et al.,1995). A recent study from our laboratory demonstrated thatalmost all cells in area 19 of the cat respond to amotion-definedbar or to its edges (Khayat et al., 2000). Our results also showedthat texture density has an influence on figure–ground seg-mentation: cell responses increased as dot density decreased.

The first objective of the present study was to assess thespatial properties of the receptive fields of a large sample ofcells in area PLLS and to determine their spatial frequencytuning functions. The second and main objective was toinvestigate the role of PLLS cortex in figure–ground analysisbased onmotion cues. Random-dot kinematogramswere usedto create drifting edges consisting of a textured form (i.e., ahorizontal bar) against a similarly textured but static back-ground. The response rates of PLLS neurons to moving bars ofdifferent widths were measured as a function of dot size.

2. Results

One hundred and seventeen cells were isolated in area PLLS ofthe cat. Cells presenting unstable or erratic responses were

Fig. 1 – (A) Spatial distribution of receptive fields mappedin the PLLS area. Each point corresponds to the center of thereceptive field of the dominant eye. Most receptive fieldswere located in the upper quadrant of the contralateral visualfield but several straddled the ipsilateral visual field.(B) Relationship between receptive field size (degree2) and theazimuth position for 101 cells. Receptive field size shows asignificant positive correlation (r=0.4, p<0.001) to azimuthposition, as illustrated by the scatter plot and theregression line.

Fig. 2 – (A) Classification by receptive fields types of cellsrecorded in area PLLS. Most receptive fields had a spatialorganization of the end-stopped complex (Ch) and complex(C) type. A small proportion of cells (15.8%) had receptivefields of the end-stopped simple (Sh) type. No simple(S) receptive fields were found. (B) Ocular dominancedistribution of PLLS cells. Most cells were binocular andthere was a strong bias in favour of the contralateral eye.

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excluded from the analysis. The position, the limits, and thespatial organization of the receptive fields of 101 cells werecarefully determined using light/dark bars and drifting sinu-soidal gratings. Moreover, sensitivity to edges defined by mo-tion was successfully tested in all these cells.

2.1. Receptive field properties

Fig. 1A shows the azimuth and the elevation positions of thecenters of the receptive fields (RFs) defined through thedominant eye for 101 cells. Most (82.5%) of the RF centers werelocated in the upper visual field. Although all penetrations wereperformed in the right hemisphere, 25.7% of these RF centerswere located in the ipsilateral hemifield. Interestingly, in 59.4%of the cells, the RFs defined through the dominant eye touchedor straddled the vertical meridian. Nearly half (49.5%) of the RFcenters were located at an azimuth eccentricity of less than

5 degrees from the midline. The limit of the most distant RF inthe ipsilateral hemifield was 17.3 degrees from the verticalmeridian.

The relationshipbetweenthesizesof theRFsof thedominanteyeand their eccentricityon theazimuth isplotted inFig. 1B.Thereceptive fields were quite large (mean: 175.3 degrees2, S.D.:272.4 degrees2), ranging from 5.0 to 735 degrees2, although fornearly three-quarters (74.2%) of the cells, theywere smaller than200 degrees2. Of these relatively small RFs, 89% were within10 degrees of azimuth. However, RFs larger than 400 degrees2

were found at various eccentricities (0.8–20.2 degrees), and a fewcells having smaller RFs (≤200 degrees2) had their centers as faras 15degrees fromtheverticalmeridian.Nonetheless, therewasa significant (r=0.4, p≤0.01) relationship between RF size andazimuthal eccentricity: they tended to increase in size as dis-tance from the vertical meridian increased.

Fig. 3 – Six typical examples of spatial frequency tuningfunctions, established by stimulation of the dominant eye,of cells in the PLLS area. The stimuli were high contrast(50%) sinusoidal gratings drifting in the optimal direction.Each point corresponds to the mean firing rate (minus thebaseline rate) of the cell in response to a particular spatialfrequency. The three cells in (A) are band-pass while thethree cells in (B) are low-pass.

Fig. 4 – (A) Distribution of optimal spatial frequenciesestimated from the spatial frequency tuning functions ofthe 59 band-pass cells recorded in area PLLS. Almost allthe cells responded optimally to relatively low spatialfrequencies. (B) Distribution of spatial bandwidths of these59 band-pass cells; bandwidths approximate the range ofspatial frequencies to which the cells were selective. Thedistribution is quite broad and it is centered at approximately2 octaves. Note that a non-negligible proportion of cells(15.3%) showed a very narrow spatial bandwidth (≤1 octave).

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Fig. 2A presents the distribution of the spatial organizationof the RFs of 101 cells. They were assessed through the domi-nant eye and classified as complex, end-stopped complex,simple, end-stopped simple using the criteria of Hubel andWiesel (1962, 1965), Henry et al. (1967), Henry (1977) andSkottun et al. (1991). The results show that most (63.4%) had aspatial organization of the end-stopped complex type and20.8% of the complex type. A smaller proportion of cells (15.8%)had RFs of end-stopped simple type, and no simple types wereencountered. Therefore, more than three-quarters (79.2%) ofthe cells showed clear end-stopped inhibition.

Fig. 2B shows the distribution of ocular dominance. As canbe seen, almost all cells (96.0%) were binocular and the few

monocular cells could only be driven through the contralateraleye. Similarly, most of the binocular cells (88.2%) preferredcontralateral stimulation.

2.2. Spatial frequency tuning functions

Spatial frequency tuning functions were determined for 73cells by drifting sinusoidal gratings across the receptive fieldof the dominant eye. The majority of cells (81.0%) were clas-sified as band-pass. These types of cells responded moststrongly or optimally to a given spatial frequency and pre-sented a drastic attenuation in their response rates at higherand lower spatial frequencies. For example, as can be seen inFig. 3A, cell 50 has a very low optimal spatial frequency (0.02 c./

Fig. 5 – (A and B) Peristimulus time histograms representing the responses of two cells to kinematograms as a function ofpixel size (texture density) and bar width. These two cells vigorously responded to bars composed of larger pixels and theirresponse rate decreased (cell 5; B) or even disappeared (cell 23; A) when smaller pixels were used, regardless of bar width. In(B), even for the smallest pixel size, two peaks are discernable in the peristimulus time histograms for the larger bar widths(4 and 8 degrees). The first and second peaks correspond to responses to the leading edge and to the trailing edge, respectively.PSTH duration: 2 s.

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degree) while cells 104 and 115 have higher optimal spatialfrequencies (0.08 and 0.15 c./degree, respectively). The distri-bution of optimal spatial frequency responses for the 59 band-pass cells is shown in Fig. 4A. and ranged from 0.017 to 0.33 c./degree. The mean optimal spatial frequency was very low(0.08 c./degree; S.D.: 0.05 c./degree), with only 5.1% of the cellshaving a frequency higher than 0.16 c./degree. The optimalspatial frequency was negatively and significantly correlatedwith both RF azimuth eccentricity (r=−0.31, p≤0.05) and size(r=−0.28, p≤0.05). That is, the optimal spatial frequencieswerelower for large receptive fields situated at high eccentricities.

To assess the selectivity of the 59 band-pass cells, spatialbandwidths were calculated at half-amplitude of the optimalresponse. The bandwidths were relatively large, ranging from0.66 to 3.59 octaves, as shownby the typical examples presentedin Fig. 3A (cell 104: 1.34 octaves, cell 115: 1.7 octaves, cell 50:1.8 octaves). The spatial bandwidth distribution of the entiregroup of band-pass cells is presented in Fig. 4B. The meanbandwidth was relatively large (1.82 octaves; S.D.: 0.78 octaves),but similar to what was found in other extrastriate visual areas(area 19: Tanaka et al., 1987; Bergeron et al., 1998; Tardif et al.,1997; areaPMLS: ZumbroïchandBlakemore, 1987). Interestingly,the cells (15.3%) that had narrow bandwidths (≤1 octave)showed the strongest end-stopped inhibition. The spatialbandwidths were significantly correlated with eccentricity(r=0.4, p≤0.001) and receptive field size (r=0.27, p≤0.05). Indeed,they were lower for small receptive fields situated near thevertical meridian. Zumbroïch and Blakemore (1987) found, inthePMLSarea, a relationshipbetweenoptimal spatial frequencyand bandwidth. However, the present results do not show sucha relationship (r=−0.2, pN0.05).

The remaining cells (19%) had spatial frequency tuningfunctions that were classified as low-pass. These cells showedan attenuation of their response rates at high spatial frequen-cies and little or no attenuation at the lower end of the spatialfrequency spectrum (for example, see Fig. 3B, cells 87 and 88).However, it is not impossible that some of these cells couldhave been classified as band-pass. For example, cell 95 (Fig. 3B)was classified as low-pass because its response rate at thelowest spatial frequencywashigher thanhalf of its response atthe optimal spatial frequency. However, it is possible that itsresponse could have undergone a greater attenuation if lowerspatial frequencies had been tested.

2.3. Response to edges defined by motion

All 101 cells responded vigorously to at least one of the 20conditions tested (4 barwidths×5 pixel sizes). Fig. 5 presents theresponses of two cells to these different conditions. Fig. 5Ashows that cell 23 responded vigorously to bars made of largerpixels and that the response rate of this cell decreased, and evendisappeared, when smaller pixels were used to form the edgedefined by motion, regardless of the width of the bar. Thispattern of response was found in almost all of the cells in areaPLLS. Cell 5 (Fig. 5B) also showed an increasing response ratewhen larger pixels were used, regardless of thewidth of the bar.However, in contrast to cell 23, this cell also responded tokinematogramscomposedof finer (0.06 and0.12degrees) pixels.Furthermore, as shown by the shape of the peristimulus timehistogram (PSTH), a particular pattern of response to larger bars(4 or 8 degrees) was observed for the cell shown in Fig. 5B. Evenfor the smallest pixel size, two peaks in the peristimulus time

Fig. 6 – Three-dimensional response areas reconstructed fromcell responses to kinematograms. (A–F) Typical examples of cellsclassified according to their selectivity to bar width (four conditions) and pixel size (five conditions). The abscissa representspixel size and the ordinate represents bar width. The Z axis shows the normalized response rate (see text) for each of the 20conditions. The cell in (A) exemplifies themost commonpattern (35.6%) of response. This category of cells responded vigorouslyto all bar widths and their response rates increased with increasing pixel size. The remainder of the cells (38.5%) showedresponses that were tuned to bar width (B–F). The cell in (B) only responded to bar widths larger than 2 degrees. A third category(C) of cells only responded to barwidths that were smaller than 4 degrees. Another category of cells (D) responded preferentiallybar of intermediate widths (2 and 4 degrees). The two last categories (E, F) consist of cells that were highly selective to widths: In(E) the cell responded exclusively to the largest widths while in (F) it responded only to the smallest widths.

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histograms are discernable. The first peak corresponds to anincrease in firing rate in response to the leading edge of thekinematogram, and the second corresponds to an increase infiring rate in response to its trailing edge. For the smaller barwidths (1 and 2 degrees), only one peak is discernable. This typeof response was encountered in 8.9% of the cells.

Amajority of cells (74.1%) could be classified as belonging toone of six categories based on their response to the distancebetween the edges or, in other words, their response to barwidth (Fig. 6). All of these six categories of cells respondedmainly to kinematograms that were composed of the largestpixels (N0.5 degrees). Figs. 6A to F present normalizedresponses for each of the 20 conditions. The abscissa repre-sents pixel size and the ordinate represents bar width. The Z-axis shows the normalized response rate. The normalizedresponse rate for each condition was calculated by dividingresponse rate for that particular condition by the maximalfiring rate of the cell to motion defined edges. Results arereported as a percentage of the maximal response based on acolor chart (on the right of Fig. 6).

Fig. 6A shows themost commonpattern of responses.Morethan a third (35.6%) of the total cell sample vigorouslyresponded to all bar widths. The response rate of these cellsincreased with increasing pixel size. The remainder of thecells that could be classified (38.5% of the total cell sample)

showed responses that were specific, or tuned, to bar width(Figs. 6B to F). Fig. 6B shows a cell (representing 7.9% of thesample) that only responded to edges that were wider than2 degrees. Other cells (7.9%) only responded to bar widths thatwere smaller than 4 degrees (Fig. 6C). Cells represented inFigs. 6B and C responded preferentially to kinematogramscomposed of larger pixels when the stimuli had optimal barwidth. This pattern was also found for cell 117 (Fig. 6D; re-presenting 8.9% of the sample), except that this cell respondedpreferentially to bars having an intermediate width (2 and4 degrees). The last two categories of cells (Figs. 6E and F)consist of cells that were highly selective to bar width. Fig. 6Eshows cells (representing 9.9% of the sample) that respondedexclusively to the largest bar width, and Fig. 6F represents asmall proportion (3.9%) that responded exclusively to thesmallest bar width (Fig. 6F).

The condition that elicited the strongest firing rate in agiven cell represents that cell's optimal response profile.Fig. 7A shows the distribution of pixel sizes that producedoptimal responses. Nearly three-quarters (72.0%) of the cellsmaximally fired when stimuli were made of the largest pixels(1 degree). In addition, more than one-quarter of the cells(26.7%) responded preferentially to stimuli made of the se-cond-largest pixel size (0.5 degrees). A single cell respondedmaximally to stimuli made of the intermediate (0.25 degrees)

Fig. 7 – Distribution of the optimal pixel size (A) and of theoptimal bar widths (B) of PLLS cells stimulated by edgesdefined by motion. Almost all cells responded optimally tothe larger pixel sizes (0.5 and 1 degrees). A large proportion ofthe cells (42.7%) responded maximally to the largest(8 degrees) bar width but it is interesting to note that nearlyone-quarter of the cells (24.0%) responded maximally to thesmallest (1 degree) bar width.

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pixel size, and one cell (shown in Fig. 8B) responded weakly tokinematogramsmade of the two smallest pixel sizes (0.06 and0.12 degrees). This distribution highlights the pixel size effectpreviously described. A simple regression analysis of the rela-tionship between pixel size and either optimal spatial fre-quency or receptive field size did not reveal any significantcorrelations (r=−0.004, pN0.90; r=−0.11, pN0.3, respectively).

The distribution of responses to bar width is shown inFig. 7B. Although a large proportion of cells (42.7%) respondedmaximally to the largest (8 degrees) width, nearly one-quarterof them (24%) also respondedmaximally to the smallest width(1 degree). The remaining cells (33.3%) optimally responded tothe intermediate widths (2 and 4 degrees). The relationshipsbetween the preference for a particular bar width and otherreceptive field characteristics of the cells were assessed. The

correlation between bar width and optimal spatial frequencywas not significant (r=−0,085, pN0.60). The correlation be-tween bar width and receptive field size was not significant(r=−0.037, pN0.75). The correlation between bar width andbandwidths of band-pass cells was not significant (r=−0.076,pN0.64). Therefore, the present findings indicate that receptivefield sizes and spatial frequency tunings were not related tothe spatial characteristics of optimal edges defined bymotion.

More than one-quarter (25.9%) of the cells were excludedfrom the classification into six categories because they did notshow a distinct pattern of response to bar width and/or pixelsize. These cells either fired at a similar rate in nearly allconditions or rather presented a seemingly random pattern ofresponse across the different conditions. Fig. 8 presents theresponse profile of such a cell. First, this cell had a clear band-pass spatial frequency tuning function (Fig. 8A), with an op-timal response at 0.05 c./degree and a large bandwidth(3.35 octaves). Fig. 8B presents the cell's peristimulus timehistograms to the different kinematograms. No particularpattern of response appears, either to barwidth or to pixel size.This is more clearly illustrated in Fig. 8C, where a high rate ofresponse is seen for most conditions.

3. Discussion

The main objective of the present study was to investigatewhether PLLS neurons can be driven by stimuli composed ofedges that only become apparent when the constitutingelements are moving. The results indicate that these neuronsrespond very well to edges defined by motion, that they res-pond more vigorously when the texture composing the edgesis made of larger elements, and that a number of cells areselective to the spatial properties of the stimuli subtended bythe edges (i.e., the distance between the edges or the width ofthe horizontal bar). A corollary objective was to provide anextended description of the receptive field properties and ofthe spatial frequency tuning functions of PLLS cells.

3.1. Receptive field properties

In agreement with previous findings (Zumbroïch et al., 1986;Rauschecker et al., 1987a), the results of the present studyindicate that area PLLS shows an overrepresentation of theupper visual field and a substantial representation of theipsilateral visual field. However, there are some importantdifferences between our findings and those of Zumbroïch et al.(1986). The present study reports a lower mean RF size and astronger relationship between size and azimuth eccentricity.One likely explanation for these differences is that nearly halfof our RF centers were located within 5 degrees of the verticalmeridian, a region that generally contains smaller receptivefields.

We founda clear predominance of complex receptive fields,particularly of the end-stopped complex types. This finding isconsistent with that of von Grünau et al. (1987). We also foundin the present study that the majority of PLLS cells were bino-cular. This is in agreement with the results found in a numberof other studies (Rauschecker et al., 1987a,b; von Grünau et al.,1987).

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3.2. Spatial frequency tuning functions

The only other study that looked into the spatial frequencytuning of PLLS cells investigated the response properties of 13neurons (Zumbroïch and Blakemore, 1987). In comparison totheir findings, the present results were computed on 101neurons and they show a slightly lower mean optimal spatialfrequency (0.08 c./degree compared to 0.17 c./degree), asmaller number of low-pass units (19% compared to 31%),and a relatively narrower mean bandwidth (1.8 octaves com-

pared to 2.2 octaves). Although it was not explicitly stated, thelowest spatial frequency tested by Zumbroïch and Blakemore(1987) appears to be 0.03 c./degree (derived from the datashown in Figs. 2–4, and 10). The use of a lower spatial fre-quency in the present study could account for the discrepan-cies just noted, since some cells classified as low-pass in theirstudy would likely have been classified as band-pass had theybeen tested with our protocol. In addition, their small samplesize could also account for these differences.

Both reports lead to the conclusion that PLLS cells respondto lower spatial frequencies than cells in most visual areas inthe cat. Based solely on the mean optimal spatial frequency,PLLS area could be considered, along with area 21b (Tardifet al., 2000), as a very low spatial frequency analyzer. Incomparison, extrastriate areas 18, 19, 21a, and PMLS (Movshonet al., 1978; Tardif et al., 1996; Tardif et al., 1997; Khayat et al.,2000) could be considered mid-range spatial frequencyanalyzers, and area 17, of course, being the high spatialfrequency analyzer (Ikeda andWright, 1975; Tardif et al., 2000).Furthermore, based on themean spatial bandwidth, area PLLSis more similar to areas 19, 21b, and PMLS, because they havebroader spatial bandwidths, than areas 17, 18, and 21a (Tardifet al., 2000). Zumbroïch and Blakemore (1987) found asignificant correlation between bandwidth and optimal spa-tial frequency in area PMLS. The present study did not findsuch a relationship in the PLLS area. This suggests that area 17is more strongly related to area PMLS than to area PLLS. At theneuroanatomical level, there is indeed evidence for strongerconnections between areas 17 and PMLS then between areas17 and PLLS (Scannell et al., 1995, 1999).

The significant correlations between azimuthal eccentric-ity and spatial bandwidth and between azimuthal eccentricityand optimal spatial frequency in PLLS cells have importantimplications for the role of this area in perception. Cells withRFs near the area centralis respond optimally to higher spatialfrequencies than cells with RFs further from this region. Thisis consistent with the hypothesis that the PLLS area is in-volved, at least to some degree, in the processing of optic flowfields (Li et al., 2000).

3.3. Responses to illusory edges defined by motion

Each of the single cells recorded in area PMLS responded toedges defined by motion. Most of them were also sensitive to

Fig. 8 – (A) Spatial frequency tuning function established forthe dominant eye of an end-stopped complex cell. Thestimulus was a high-contrast (50%) sinusoidal gratingdrifting in the optimal direction. Each point corresponds tothe mean firing rate of the cell at a given spatial frequency.(B) Peristimulus time histograms representing the same cell'sresponses to kinematograms as a function of pixel size andbar width. (C) These responses are represented in a threedimensional plot to better depict the relationship betweenresponse rate, pixel size and bar width. Raw data of theresponse changes for pixel size and width are normalized tothe maximum response. This cell vigorously responds tokinematograms but shows no coherent relationship betweenresponse rate and pixel size, regardless of bar width. PSTHduration: 2 s.

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specific properties of the stimuli, such as pixel size and dis-tance between the edges (bar width). Almost every cell res-ponded more vigorously to kinematograms made of largerpixels. This facilitating effect of increasing the size of theelements was also found in area 19 of the cat (Khayat et al.,2000), and in the striate cortex of the New World monkey(Bourne et al., 2002). Similarly, enlarging the size of textureelements making up a whole-field pattern increased cellularresponse in areas 17 (Casanova et al., 1995) and PMLS (Merabetet al., 2000) of the cat. Because pixels consist of squares withcontours that are well defined and orthogonal to the directionof motion, it could be argued that orientation cues were alsopresent. Thus, the processing of the edge defined by motionwith these coarser texture elements could solicit orientation-sensitive mechanisms in addition to motion-sensitive ones.However, this is unlikely for a number of reasons: first, be-cause the texture elements are quite small with respect to RFsize; second, because short oriented bars which do not extendto the suppressive regions of the RF even for area 17 do notshow strong orientation selectivity (Henry et al., 1974; Chenet al., 2005); and finally, because these orientation cues wereequally present in the static background and in the motion-defined bars.

A large proportion of cells (74.1%) also modified theirresponse rates as a function of the distance between theleading and trailing edges. This suggests that bars defined bymotion can be integrated to form surfaces in much the sameway as “real” bars. In addition, some cells (8.9%) responded in atransientmanner to the leading and to the trailing edges of thebar, particularlywhen the distance between the two edgeswaslarge. In fact, the greater proportion of cells that respondedoptimally to larger distances between the edges could be ac-counted for by the summed responses evoked by these edges.Similar edge-detector profiles were also found in area 19 of thecat (Khayat et al., 2000). It is likely that the processing of edgesdefined by motion in area PLLS is largely dependent on thesubstantial Y-input to that area (Scannell et al., 1995, 1999).

4. Experimental procedures.

Twelve cats of either sex that weighted from 2 to 4 kgwere usedin this study. All were in good health and there was noindicationofmalformationor pathology. Surgical interventions,manipulations, husbandry and all experimental protocols werecarried out within the guidelines proposed by the CanadianCouncil of Animal Care and by the National Institutes of Health(USA). The experimental protocol was approved by the AnimalCare Committee of the Université de Montréal and the animalscame from a supplier approved by the University.

4.1. Anaesthesia and surgery

The methods used for animal preparation, anaesthesia,surgery, optical preparation, and recording are described indetail in previous papers from the laboratory (Guillemot et al.,1993; Bergeron et al., 1998; Bacon et al., 2000; Khayat et al., 2000;Mimeault et al., 2002) and will only be briefly described herein.The day before the experimentation the cats received anintramuscular injection of dexamethasone (10 mg/kg; Veto-

quinol Canada Inc., Joliette, Canada) to reduce inflammationduring surgery. On the day of recording, the cat was premedi-cated with a subcutaneous injection of acepromazine maleate(Atravet, 1.0mg/kg) and an intramuscular injection of atropine(Atro-sol, 0.2mg/kg; OrmondVeterinary Supply Ltd, Lancaster,Canada) to limit bronchial secretion. The anaesthesiawas theninduced by facemask inhalation of 5% isoflurane mixed withnitrous oxide and oxygen (N2O:O2, 70:30). The animal was thenintubated with an endotracheal tube connected to a respira-tory pump (Harvard, model 665). Respiratory rate and strokevolume were adjusted to maintain end tidal CO2 at a constantlevel (∼4.0%). During all surgical procedures, the animals werekept deeply anaesthetized by maintaining isoflurane levelsbetween 1.0% and 2.0%. The animalswere placed in amodifiedstereotaxic apparatus (Kopf Instruments, Tujunga, CA, USA) toavoid pressure on the eyes and to free the visual field of anyobstruction. A small trepanation (20mm2) was performed overthe PLLS area between Horsley–Clarke coordinates A6-P4 andL10-20 (Palmer et al., 1978). The dura mater was folded backand an electrode was inserted latero-medially (penetrationangle: 30°) in the central representation of the visual field ofthis area (Palmer et al., 1978). In order to reduce brain pulsationandprotect the cortex fromdehydration, thebrainwas coveredwith agar solution (4.0% agar in physiological saline). All pres-sure points and incision sites were routinely infiltrated with alocal anaesthetic (Xylocaine 2.0%; Astra Pharma Inc., Missis-sauga, Canada).

At the end of the surgery, the isoflurane level was pro-gressively reduced (0.5% per 15 min) and throughout therecording session, the animals were maintained under an-aesthesia (N2O:O2, 70:30; isoflurane, 0.5% of gaseous mixture).The absence of reflexes and a stable heart rate ensured thatthe level of anesthesia was sufficient. From that point on, theanimals were paralyzed. The neuromuscular blockade of theextra ocular muscles was maintained by the continuous in-fusion, through a saphenous vein cannula, of a gallaminetriethiodide (Flaxedil: 12.5 mg/kg/h; Rhône-Poulenc, Montréal,Canada) and D-tubocurarine (Tubarine: 1.3 mg/kg/h; Sigma, StLouis, MO, USA) mixture dissolved in a solution of lactatedRinger with dextrose (5.0%).

During the recording session, the stability of the heart ratewas constantly verified to ensure that the level of anaesthesiawas sufficient. The electroencephalogram, which was verifiedregularly, showed slow-wave activity throughout the record-ing session. During neuromuscular blockade and throughoutthe recording session the animals were artificially ventilated.Body temperature was kept constant (38 °C) with the help of aheated water pad.

4.2. Optical preparation and recording

The nictitating membranes were retracted by topical applica-tion of phenylephrine hydrochloride (Neo-synephrine, 0.1%;Winthrop Laboratories, Aurora, Canada). Pupils were routinelydilated by topical application (eye drops) of atropine (Atro-sol,0.2 mg/kg; Ormond Veterinary Supply Ltd, Lancaster, Canada).To prevent eye dehydration and to improve image resolution,neutral contact lenses with a 3-mm artificial pupil wereplaced on each eye. Appropriate dioptric lenses were placedin front of the eyes of the animals, as determined by the direct

Fig. 9 – (A–D) Schematized representations of typicalkinematograms used in the experiment. The diameter ofthe actual stimuli subtended 60 degrees of visual angle; blackand white textured elements (ratio 1:1) subtended 0.06, 0.12,0.25, 0.5, or 1 degrees of visual angle. Shaded areas have beensuperposed onto the figure to delimit the pixels forming themoving bars, and therefore the edges defined by motion.In the actual display, bar and background were identical inevery way so that static bars could not have been perceived.Bars always drifted at the optimal speed in the cells'preferred direction. The bars in (A, B) represent the smallestbar width and the bars in (C, D) represent the largest barwidth (actual bar widths: 1, 2, 4, and 8 degrees). For thetesting of complex cells, the bar covered the entire length ofthe background (B and D). For the testing of end-stoppedsimple and end-stopped complex cells, bar length wasadjusted so as to obtain maximal responses (A and C).

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ophthalmoscopic examination. To determine the relativeposition of the areae centrales, retinal landmarks (optic discsand blood vessels) were projected on a tangent screen located57 cm from the nodal point of each eye (Fernald and Chase,1971). The area centralis of each eye was considered to belocated 16° medially and 7.5° below the iso-elevation line ofthe center of each optic disc (Bishop et al., 1962). The opticalquality of the eyes was checked routinely before and aftereach quantitative protocol.

Recording was performed with tungsten microelectrodesthat had an impedance range of 1–3 MΩ measured at 1000 Hz.The neural spikes were conventionally amplified, displayedon an oscilloscope, isolated with the help of a time–amplitudediscriminator, and then transferred to an audio monitor and aPC computer.

4.3. Histology

Electrolytic lesions were made at each recording track. At theend of the experiment, the cat was deeply anaesthetized with5% isoflurane and perfused through the heart with isotonicsaline followed by formalin (10%). The brain was removed,placed in formalin, and prepared for histology. The blocks oftissue that contained the electrode tracks were sectioned co-ronally (40 μm) using a freezing microtome and then stainedwith cresyl violet. According to the maps of Palmer et al.(1978), all electrode penetrations were within the PLLS area.

4.4. Stimuli

The stimuli were generated with a G3 Macintosh computerusing VPixx software (VPixx Technologies Inc., Longueuil,Canada). They were back-projected by means of a LCD pro-jector (Mitsubishi LVP-X100A; refresh rate was 75 Hz), on atranslucent screen located 57 cm in front of the animal's eyes.The mean luminance of the stimulation field was 19.9 cd/m2

and the resolution of the image was 11.9 pixels/degree. Foreach cell, the stimulation field (length and width) covered thewhole RF andwasprecisely positioned at the center of theRF ofthe dominant eye. The optimal stimulus parameters (directionand velocity) were determined using sinusoidal gratings.The positions, the limits, and the spatial organization of eachRF were carefully investigated. To test for end-stopping, thesize of the stimulus was varied systematically. The principalinclusion criteria for end-stopping was that the cell preferredan oriented stimulus of a specific length, whereby extendingthe stimulus out of the boundaries of the RF caused aclear decrease (more than 80% as evaluated by ear) in responserate.

For each cell, the spatial frequency tuning function wasassessed for the dominant eye using drifting sinusoidalgratings (0.01–1.28 c./degree). The different points on the spa-tial frequency tuning functionwere separated by about half anoctave. Each spatial frequency was presented at least ten timesin a pseudo-random fashion. The cell's firing rate was recordedusing optimal parameters for direction and temporal frequency(2, 4, or 6Hz), asdeterminedbyear. In order toobtain thehighestresponse rate, special care was taken to adjust the size (widthand length) of the gratings to the size of the dominant eye's RF.Contrast was kept constant at 50% and was defined using the

Michelson (1927) formula: Contrast=(max. luminance−min.luminance/max. luminance+min. luminance)×100.

Each trial began with a gradual increase in the contrast ofthe grating (from 0% to 50%) over a period of 100 ms. The cell'sresponse was not recorded during this period in order to pre-vent a transient response from the cell. Subsequently, gratingsdrifted for 1 s and an inter-stimulus interval of 10–15 s wasintroduced between trials to minimize adaptation effects.Between each trial, the screen returned to mean luminance(contrast: 0). Discharge rate was computed for 1 s prior to eachgrating presentation, when the screen was still at meanluminance. During the presentation of the drifting grating, aperistimulus time histogram was constructed for a period of1 s. A Fourier analysiswas conducted to assess themodulationof the response at the first harmonic of the drifting grating.The criteria of Skottun et al. (1991) were used to classify thereceptive field organization. Briefly, cells that showed amodulated response at the first harmonic for spatial frequen-cies higher than their optimal spatial frequency were classi-fied as simple or end-stopped simple. Cells that showed an

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unmodulated response and an overall increase in theirresponse component (mean firing rate) at the first harmonicwere classified as complex or end-stopped complex.

Each data point on the tuning functions of the complex andend-stopped complex cells correspond to the mean firing rateof the cell during stimulus presentationminus themean firingrate during the blank presentation. For simple and end-stopped simple cells, the data points on the tuning functionswere plotted according to the fundamental Fourier responsecomponent from which the response during the blank pre-sentation was subtracted. The tuning functions of all cellswere fitted with either a Gaussian or a sixth-order polynomialfunction (Table Curve 2D, Systat Software Inc., Richmond, CA,USA). The optimal curve fit chosen had a strong correlation(r≥0.9) with the data points and was used to evaluate spatialfrequency and bandwidth. The spatial bandwidth of thetuning function was defined as the full width of the curve athalf the amplitude for the optimal spatial frequency.

Based on their spatial frequency tuning functions, cells wereclassified as either band-pass or low-pass. Band-pass cells res-ponded most strongly or optimally to a given spatial frequencyand presented a drastic attenuation in their response rates athigher and lower spatial frequencies. In contrast, low-pass cellsshowed attenuation in their response rates at high spatialfrequencies, but not at low spatial frequencies. For each cell, thereceptive field of the non-dominant eye was also tested at theoptimal spatial frequency to evaluate ocular dominance. Anocular dominance index ranging from 1 (contralaterally driven)to 7 (ipsilaterally driven) was used. In order to classify the bino-cular cells among the five intermediate categories, an oculardominance index was calculated for every cell using thefollowing formula (ipsilateral/ (ipsilateral+contralateral))×100,in which ipsilateral is the response rate of the cell to the mo-nocular stimulationof the ipsilateral eyeand contralateral is theresponse rate of the cell to the monocular stimulation of thecontralateral eye. Theocular dominance index thereby obtainedallowed for classification using the following criteria: class2: 1.0–20.0%, class 3: 21.0–40.0%, class 4: 41.0–60.0%, class 5: 61.0–80.0%, class 6: 81.0–99.0%.

Edgesdefined bymotionwere elicited bymoving an orientedtextured form (bar) against an identically oriented but statictextured background (see Fig. 9). The texture of the orientedform and of the background consisted of randomly positionedlight and dark squares (1:1 ratio of dark and light elements). Thesizes of the textured elements were set at: 0.06×0.06 degrees,0.12×0.12 degrees, 0.25×0.25 degrees, 0.5×0.5 degrees, or1×1 degree. For simplicity, these patterns are referred to in thetext as 0.06 degrees, 0.12 degrees, 0.25 degrees, 0.5 degrees, and1 degree, respectively. Both the bar and background elementshad the samemean luminance (19.9 cd/m2) and contrast (90%).Because the texture elementsmaking up the oriented form andthebackgroundhad the sameorientationanddensity, the edgesof the oriented textured form could only be defined by motionand were otherwise camouflaged. Thus, the edges of the barcould only be perceived when the pixel polarities were cohe-rentlymoving in thesamedirection.Thehighest texturedensitypatternwas similar to the oneusedbyCasanova et al. (1995) andrevealed, through the 2D Fourier power spectrum analyses, thatall spatial frequenciesandall orientationshad thesameamountof power.

In this experiment, only the dominant receptive fields weretested. Schematized representations of the kinematogramsused in the experiment are shown in Fig. 9. For all cells, thebackgroundsubtended60degrees indiameterand fourdifferenttextured form (bar) widths were used (1, 2, 4, and 8 degrees: seeFigs. 9A and B for small bar widths and Figs. 9C and D for largebar widths). For simple and complex receptive fields, the lengthof the bar covered thewhole field (60 degrees; see Figs. 9B andD)but for end-stopped receptive fields special care was taken toadjust the length of the bar in order to obtain the highestresponse rate (see Figs. 9A and C). The bar drifting speed (15–30 degrees/s) at the cell's optimal directionwas selected in orderto elicit thehighest response rate. Eachof the20conditions (fourbar widths×five pixel sizes) was tested 10 times in a pseudo-random fashion. An inter-stimulus delay of 10–15 s was used toavoid habituation. For each condition, a peristimulus timehistogramhaving500 binsandabinwidthof 4–6mswasderivedfrom the cellular responses.

Acknowledgments

This study was supported by grants from the Natural Sciencesand Engineering Research Council of Canada (NSERC) awardedto J.-P. Guillemot and F. Lepore and by a CanadaResearchChairawarded to F. Lepore.

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