An evaluation of the two-dimensional Gabor filter model of simple receptive fields
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Behavioral/Systems/CognitiveHighly Selective Receptive Fields in Mouse Visual Cortex Cristopher M.Niell and Michael P.StrykerW.M.Keck Foundation Center for Integrative Neuroscience,Department of Physiology,University of California,San Francisco,San Francisco,California 94143-0444Genetic methods available in mice are likely to be powerful tools in dissecting cortical circuits.However,the visual cortex,in which sensory coding has been most thoroughly studied in other species,has essentially been neglected in mice perhaps because of their poor spatial acuity and the lack of columnar organization such as orientation maps.We have now applied quantitative methods to characterize visual receptive fields in mouse primary visual cortex V1by making extracellular recordings with silicon electrode arrays in anesthetized mice.We used current source density analysis to determine laminar location and spike waveforms to discriminate putative excitatory and inhibitory units.We find that,although the spatial scale of mouse receptive fields is up to one or two orders of magnitude larger,neurons show selectivity for stimulus parameters such as orientation and spatial frequency that is near to that found in other species.Further-more,typical response properties such as linear versus nonlinear spatial summation(i.e.,simple and complex cells)and contrast-invariant tuning are also present in mouse V1and correlate with laminar position and cell type.Interestingly,we find that putative inhibitory neurons generally have less selective,and nonlinear,responses.This quantitative description of receptive field properties should facilitate the use of mouse visual cortex as a system to address longstanding questions of visual neuroscience and cortical processing.Key words:visual cortex;receptive field;mouse;orientation;spatial frequency;contrast-invariant tuningIntroductionOver the past nearly half century since visual responses were first described in the mammalian visual cortex(Hubel and Wiesel, 1962),there has been intensive research into the neural circuit and developmental mechanisms that give rise to selective recep-tive field(RF)properties.However,although the description of visual encoding has become increasingly more quantitative (Ringach,2004;Carandini et al.,2005),as has the anatomy of neuronal subtypes(Gilbert,1983;Douglas and Martin,2004),it has been difficult to link these functional and anatomical findings into a local circuit model of cortical processing.Even more ques-tions remain about how such a circuit might be assembled during development,despite advances in understanding the molecular mechanisms involved(Waites et al.,2005;Rash and Grove,2006; Polleux et al.,2007)and the consequences of altered sensory input(Hensch,2005;Hofer et al.,2006).The recent proliferation of genetic technology in mice may provide tools to answer many of these questions(Callaway, 2005).Targeted gene disruption and transgene expression can result in much more specific manipulations than have been pos-sible via pharmacology or sensory alteration and can allow per-turbation of cellular signaling(Huang et al.,1999;Karpova et al., 2005),synaptic plasticity(Zeng et al.,2001;Sawtell et al.,2003), and firing patterns(Tan et al.,2006;Zhang et al.,2007a),even at the single-cell level(Brecht et al.,2004).Furthermore,fluorescent protein labeling provides precise anatomical techniques to yield information about cell type(Feng et al.,2000;Tamamaki et al., 2003)or even synaptic connectivity(Wickersham et al.,2007)in the intact brain,bridging the gap between circuit structure and function.Indeed,studies have already begun to take advantage of genetic methods to study visual system development(Fagiolini et al.,2003;Cang et al.,2005),plasticity(Fagiolini et al.,2004;Syken et al.,2006),and function(Sohya et al.,2007).Studies of cortical visual processing have typically used carni-vores or primates,which are considered to have a more refined visual system,including a much larger cortical region for visual processing,higher acuity,extensive visual behaviors,and orien-tation,ocular dominance,and spatial frequency columns(Issa et al.,2000;Ohki and Reid,2007;Van Hooser,2007).Understand-ing visual processing in such a simple system as the mouse cortex, which lacks both fine-scale spatial acuity and maps such as ori-entation columns,should provide insight into the minimal mechanisms necessary for receptive field development and func-tion.However,although recent studies in rat and squirrel have described receptive field properties in the absence of orientation maps(Fagiolini et al.,1994;Girman et al.,1999;Heimel et al., 2005;Ohki et al.,2005),much less is known about visual encod-ing and processing in mice.Several early electrophysiology stud-ies(Dra¨ger,1975;Mangini and Pearlman,1980;Metin et al., 1988)(for review,see Hubener,2003)indicated that,although mouse cortical neurons can be classified into categories similar to those described in other species,their overall level of stimulusReceived Feb.11,2008;revised April23,2008;accepted June12,2008.This work was supported by National Institutes of Health Grant EY02874(M.P.S.)and a Helen Hay Whitney Foundationfellowship(C.M.N.).WethankDrs.JonathanHorton,SteveLisberger,andLindaWilbrechtandmembers of the Stryker laboratory for comments on this manuscript and helpful discussions.We also thank Drs.Michael LewickiandEizaburoDoiforprovidingroutinestofitGaborfunctionsandDr.DarioRingachfordataonGaborfitsand circular variance from macaque.Correspondence should be addressed to Michael P.Stryker,Department of Physiology,513Parnassus Avenue, RoomHSE-802,UniversityofCalifornia,SanFrancisco,SanFrancisco,CA94143-0444.E-mail:stryker@. DOI:10.1523/JNEUROSCI.0623-08.2008Copyright©2008Society for Neuroscience0270-6474/08/287520-17$15.00/07520•The Journal of Neuroscience,July23,2008•28(30):7520–7536selectivity may be significantly less than that of other species. However,these studies are difficult to evaluate because they were performed before many of the recent quantitative techniques for receptive field characterization were developed.A recent study, using two-photon imaging of calcium signals,measured differ-ences in orientation selectivity in inhibitory and excitatory neu-rons of mouse primary visual cortex V1(Sohya et al.,2007)but did not perform a thorough characterization of other visual re-sponses and selectivity and was restricted to imaging the superfi-cial layers.We therefore undertook a quantitative survey of receptive field properties in V1of the anesthetized mouse.In addition to determining the types of stimuli and range of stimulus parame-ters that are appropriate for probing vision in mice,we sought to confirm that the basic properties of visual processing that have been studied in other species are present in mice.These include orientation and spatial frequency tuning,the presence of both simple and complex response types,and higher-order properties such as contrast-invariant tuning.Furthermore,to relate these functional properties to computation in the cortical circuit,we analyzed responses by their laminar location within cortex and by putative identity as inhibitory versus excitatory neurons based on spike waveform.Materials and MethodsIn vivo physiology.Recordings were made from adult C57BL/6mice,2–6 months of age.The animals were maintained in the animal facility at University of California,San Francisco(UCSF)and used in accordance with protocols approved by the UCSF Institutional Animal Care and Use Committee.For surgery,mice were sedated with an intraperitoneal in-jection of chlorprothixene(5mg/kg)and then anesthetized with ure-thane(0.5–1.0g/kg,i.p.,at10%w/v in saline).Administration of chlor-prothixene several minutes before urethane greatly reduced the dosage of urethane necessary to induce surgical anesthesia.Additionally,atropine (0.3mg/kg)and dexamethasone(2mg/kg)were administered subcuta-neously to reduce secretions and edema,respectively.The animal was maintained at37.5°C by a feedback-controlled heating pad.A tracheot-omy was performed,and a small glass capillary tube was inserted to maintain a free airway.After retracting the scalp,we performed a small craniotomy,ϳ1mm in diameter,and nicked a slit in the dura to allow insertion of the multisite electrode.The exposed cortical surface was covered with2.5%agarose in extracellular saline(in m M:125NaCl,5 KCl,10glucose,10HEPES,and2CaCl2,pH7.4)to prevent drying and provide mechanical support.The electrode was lowered into the brain through the agarose to an appropriate depth and was allowed to settle for 30–45min before the beginning of recording.The electrode was placed without regard for the presence of visually responsive units,and all units stably isolated over the recording period were included.For superficial recordings,the electrode was often inserted at an angle,to increase the distance between the insertion and recording sites.The eyes were covered with ophthalmic lubricant ointment until recording,at which time the eyes were rinsed with saline and a thin layer of silicone oil(30,000cen-tistokes)was applied to prevent drying while allowing clear optical trans-mission.We did not induce cycloplegia,and the resting pupil diameter wasϳ1mm.We recorded only at sites with receptive fields located at least20°lateral to the visual meridian,to avoid confounding effects at-tributable to the binocular zone of vision.At the end of recording,the animal was killed by overdose of barbitu-rates.For histology,the animal was intracardially perfused with4%para-formaldehyde in PBS,and the brain was sectioned coronally at100m with a vibratome(Lancer).Sections were incubated for several hours in 0.1g/ml4Ј,6-diamidino-2-phenylindole(DAPI;Sigma-Aldrich)to stain nuclei and imaged on an epifluorescence microscope. Recordings were made with silicon microprobes from NeuroNexus Technologies.Two configurations were used:a linear probe with16sites spaced at50m intervals(model a1x16-3mm50-177),which could be used to span across multiple layers of cortex;and a tetrode configuration, with four tetrode clusters,each consisting of four sites separated by25m on a side(model a2x2-tet-3mm-150-121),which was used primarilyto provide better isolation of units in layers2/3and4.Approximately two-thirds of all units were recorded with the tetrode configuration.The shanks of the probes were15m thick and3mm long,with a maximum width at the top of the shank of94m(tetrode)or123m(linear.)For experiments followed by histology to reconstruct penetrations,the elec-trode was coated with a small amount of the lipophilic vital dye DiI (Invitrogen).Signals were acquired using a System3workstation (Tucker-Davis Technologies)and analyzed with custom software in Matlab(MathWorks).For local field potential(LFP)recording,the ex-tracellular signal was filtered from1to300Hz and sampled at1.5kHz. Current source density(CSD)was computed from the average LFP by taking the discrete second derivative across the electrode sites,at two-site spacing to reduce noise,and interpolated to produce a smooth CSD map. For single-unit recording,the extracellular signal was filtered from0.7 to7kHz and sampled at25kHz.Spiking events were detected on-line by voltage threshold crossing,and a1ms waveform sample was acquired around the time of threshold crossing.To improve isolation of single units,recordings from groups of four neighboring sites were linked,so that a waveform was acquired on all four sites in response to a threshold crossing on any of the four.This procedure was used for both the tetrode and linear configuration electrodes;in the latter case,sites1–4,5–8, 9–12,and13–16(in order along the shank)were grouped together.In both cases,this“virtual tetrode”acquisition had two primary benefits: improved discriminability when a waveform appeared on more than one site,and common-mode noise rejection of signals shared on all four sites. Whereas the larger amplitude spikes of layer5and layer6units were sometimes recorded simultaneously on adjacent sites at the50m spac-ing of the linear electrode,layer2/3neurons often appeared predomi-nantly on one site,even at the25m spacing of the tetrode configura-tion.In both cases,many units had signals on nondominant sites that were below the voltage trigger threshold;however,the simultaneous ac-quisition allowed this low-amplitude information to be integrated to improve discriminability.The individual waveform samples were aligned by their most negative time point.To identify single units,the spike waveforms from the four sites together were parameterized by6–10independent components us-ing the FastICA package for Matlab(http://www.cis.hut.fi/projects/ica/ fastica/)and clustered by a mixture-of-Gaussians model using Klusta-Kwik(Harris et al.,2000).Using independent components,rather than principal components,allowed us to take advantage of common-mode noise rejection from waveforms simultaneously acquired across multiple sites in the virtual tetrode configuration.Whereas independent compo-nents analysis(ICA)has been used previously to filter the continuous data across channels(Snellings et al.,2006),we instead performed ICA on the individual waveforms,to integrate waveform parameterization and noise rejection.Quality of separation was determined based on the Mahalanobis distance and L-ratio(Schmitzer-Torbert et al.,2005)and the presence of a clear refractory period.Units were then classified as narrow or broad spiking based on prop-erties of their average waveforms,at the electrode site with largest ampli-tude.Three parameters were used for discrimination(Fig.1F,G):the height of the positive peak relative to the initial negative trough,the time from the minimum of the initial trough to maximum of the following peak,and the slope of the waveform0.5ms after the initial trough.This third measure provided a proxy for the total duration of the slower positive peak,because our waveform sampling was not of sufficient du-ration to measure the entire return to baseline.Two linearly separable clusters were found,corresponding to narrow-spiking(putative inhibi-tory)and broad-spiking(putative excitatory)neurons.These clusters were separated identically by both k-means and linkage clustering.Unit classification was stable and did not change when only two of the three measurements were used.The width of the initial trough,as used previ-ously(Bruno and Simons,2002),did not give good separation,which may be attributable to filtering by the electrodes or acquisition system because this is the shortest timescale in the waveform,or to its sensitivity to the alignment of individual waveforms in computing the averageNiell and Stryker•Receptive Fields in Mouse Visual Cortex J.Neurosci.,July23,2008•28(30):7520–7536•7521waveform.Because of the small numbers of narrow-spiking units in each layer,in cases in which no significant difference was seen across the individual layers,we pool together data for narrow-spiking units from all layers into one category for presentation.Visual stimuli.A challenge involved in our approach of unbiased recording from a number of neurons simultaneously is that stimuli can-not be tailored to individual neurons.For ex-ample,it is much faster to measure spatial fre-quency tuning of a single unit by finding the optimal orientation and then varying the spatial frequency only at this orientation than by pre-senting all orientations and all spatial frequen-cies,as in this study.This also limited our ability to vary multiple parameters in a single stimulus set:for example,to measure contrast-invariant tuning,we chose one spatial frequency and then varied orientation and contrast,because the curse of dimensionality makes it impractical to sample finely across all three parameters.Thus,the response was limited to those neurons tuned to the spatial frequency we chose.This will become even more of a limitation in new techniques,such as two-photon calcium imag-ing,which can sample large populations but have poorer temporal resolution.Stimuli simi-lar to the contrast-modulated noise movies de-scribed below,which can provide rapid mea-sures of responsiveness across a widely tuned population,may help to address these challenges.Stimuli were generated in Matlab using the Psychophysics Toolbox extensions (Brainard,1997;Pelli,1997)and displayed with gamma correction on a monitor (Nanao Flexscan,30ϫ40cm,60Hz refresh rate,32cd/m 2mean lumi-nance)placed 25cm from the mouse,subtend-ing ϳ60ϫ75°of visual space.Episodic stimuliwere repeated five to seven times,with stimulus conditions randomly interleaved,and a gray blank condition (mean luminance)was in-cluded in all stimulus sets to estimate the spon-taneous firing rate.Episodic stimuli included drifting sinusoidal gratings [1.5s duration,temporal frequency of 2Hz,12directions,spatial frequency of 0.01,0.02,0.04,0.08,0.16,0.32,and 0cycles/°(cpd),i.e.,full-field flicker],full-length drifting bars (width of 5°,velocity of30°/s,16directions),drifting short bars (4ϫ8°,30°/s,four directions),contrast-reversing (counter phase)sinusoidal gratings (0.04cpd,sinu-soidally reversing at temporal frequencies 1,2,4,and 8Hz,eight orien-tations,2s duration),and contrast-reversing square checkerboard (0.04cpd,square-wave reversing at 0.5Hz).Episodic stimuli were shown at 100%contrast with the background at mean luminance,except in a subset of experiments on contrast-invariant tuning,in which drifting sinusoidal gratings were presented as described above but at fixed spatial frequency of 0.04cpd and contrast 6.25,12.5,25,50,and 100%.Gaussian noise movies were created by first generating a random spa-tiotemporal frequency spectrum in the Fourier domain with defined spectral characteristics.To drive as many simultaneously recorded units as possible,we used a spatial frequency spectrum that dropped off as A (f )ϳ1/(f ϩf c ),with f c ϭ0.05cpd,and a sharp cutoff at 0.12cpd,to approximately match the stimulus energy to the distribution of spatialfrequency preferences.The temporal frequency spectrum was flat with a sharp low-pass cutoff at 4Hz.This three-dimensional (x ,y ,t )spec-trum was then inverted to generate a spatiotemporal movie.This stimu-lus is related to the subspace reverse correlation method (Ringach et al.,1997),in that both explicitly restrict the region of frequency space that is sampled.To provide contrast modulation,this movie was multiplied by a sinusoidally varying contrast.Movies were generated at 60ϫ60pixels and then smoothly interpolated to 480ϫ480pixels by the video card to appear at 60ϫ60°on the monitor and played at 30frames per second.Each movie was 5min long and was repeated two to three times,for 10–15min total presentation.A brief clip of the contrast-modulate movie is available as supplemental data (available at as supplemental material).Data analysis.The average spontaneous rate for each unit was calcu-lated by averaging the rate over all blank condition presentations.For drifting gratings,responses at each orientation and spatial frequency were calculated by averaging the spike rate during the 1.5s presentation and subtracting the spontaneous rate.The preferred orientation was de-termined by averaging the response across all spatial frequencies and calculating half the complex phase of the valueS ϭ¥F ͑͒e 2i ¥F ͑͒minarlocationandspikewaveformclassification.A ,Schematicoflinearmultisiteprobe.B ,AverageLFPresponses for 16sites through the depth of cortex.Arrows show consecutive peaks of the high-frequency oscillation.C ,CSD analysis of traces in B demonstrates segregation of responses by layer.Blue represents current sinks,and red represents current sources.Because the CSD is based on a second derivative at two-site spacing,the CSD cannot be computed for the top two and bottom two sites,soit spans 550m rather than 750m.Units for CSD are normalized from Ϫ1to 1.D ,Average spike waveforms for all units analyzed,alignedtominimumandnormalizedbytroughdepth,demonstratingnarrow-spiking(blue;n ϭ45)andbroad-spiking (green;n ϭ186)units.E ,Average of all waveforms for narrow-spiking and broad-spiking units.F ,G ,Scatter plot of spike waveform parameters for all units.7522•J.Neurosci.,July 23,2008•28(30):7520–7536Niell and Stryker •Receptive Fields in Mouse Visual CortexThe orientation tuning curve was constructed for the spatial frequency that gave peak response at this orientation.Given this fixed preferred orientationpref,the tuning curve was fitted as the sum of two Gaussianscentered onpref andprefϩ,of different amplitudes A1and A2but equal width,with a constant baseline B.From this fit,we calculated twometrics:an orientation selectivity index(OSI)representing the ratio of the tuned versus untuned component of the response,and the width of the tuned component.OSI was calculated as the depth of modulation from the preferred orientation to its orthogonal orientationorthoϭpref ϩ/2,as(R prefϪR ortho)/(R prefϩR ortho).Tuning width was the half-width at half-maximum of the fit above the baseline,R ortho.In addition, direction selectivity was calculated from the fitted function as(R prefϪR opposite)/(R prefϩR opposite).We used these measures of selectivity,rather than the circular vari-ance,because the circular variance,which is a single global measure of the tuning curve,combines aspects of both depth of modulation and tuning width into one value and thus does not give as intuitive a description of the tuning curve(but see Ringach et al.,2002for a thorough exposition). Furthermore,because it has not yet gained common usage,there is less previous data for comparison.However,measuring orientation selectiv-ity as(1Ϫcircular variance),i.e.,the absolute value of S as defined above, gave similar results(supplemental Fig.S2,available at as supplemental material)and should be useful in the future,particularly in cases in which a single metric for orientation selectivity is desirable. The spatial frequency tuning curve was determined from drifting grat-ings from the response at orientationpref described above and was fit to a difference of two Gaussians(Hawken and Parker,1987).Bandwidth was calculated from the fit as the ratio of the spatial frequencies that gave half-maximal response.Units were classified as having low-pass spatial frequency tuning if the response to0cpd,the full-field flicker,was at least 50%of the peak response.Linearity of response was calculated from drifting gratings,at the ori-entation and spatial frequency that gave peak response.First,we binned the1.5s presentation into a spike histogram at100ms intervals and subtracted the spontaneous rate.We then applied the discrete Fourier transform and computed F1/F0,the ratio of the first harmonic(response at the drift frequency)to the0th harmonic(mean response). Responses to drifting full-field bars were analyzed by computing peri-stimulus time histograms with bin size100ms.The spontaneous rate was subtracted,and the peak firing rate for each orientation was used to generate an orientation tuning curve,which was fit to the sum of two Gaussians and analyzed as described above.Receptive field size was calculated from4ϫ8°light bars.Bars were swept across the visual field at eight locations along the axis perpendic-ular to the direction of motion,e.g.,for horizontally moving bars,each presentation swept a bar across at a different vertical position.The re-sponses from the eight sweeps were binned at100ms and used to con-struct firing rate as a function of bar position.This was fitted with a two-dimensional Gaussian,with independent widthsx andy,and RF radius was calculated by averaging the half-width at half-maximum of the two axes of the Gaussian fit(equivalent to the semi-major and semi-minor axes of the ellipse generated by the half-maximum contour).This process was repeated for four different directions and averaged across all conditions that gave a sufficient response.The bar length,8°,was chosen to elicit strong responses from as many units as possible.However,it puts a lower limit on our measurement of RF size,ϳ4°,so we may have overestimated the size of the smallest receptive fields.Temporal frequency was calculated from the response to contrast-reversing sinusoidal gratings at a fixed spatial frequency of0.04cpd.The average firing rate over each2s presentation was calculated,and the spontaneous rate was subtracted.For the orientation that gave the largest total response,a temporal frequency tuning curve was fit using a differ-ence of two Gaussians.To analyze the response to contrast-modulated white noise movies,we binned the number of spikes in response to each frame of the movie.A responsiveness metric was calculated by taking the discrete Fourier trans-form at the modulation frequency,normalized by the average firing rate. Additionally,the spike-triggered average(STA)of contrast-modulated movie responses was computed by the mean of the frames preceding each spike.Because we used a1/f power spectrum for the stimulus set,the raw STA is broadened by the correlations in the stimulus set.However,be-cause the stimulus is Gaussian and therefore only contains second-order correlations,we were able to correct the STA exactly by normalizing its Fourier transform by the power spectrum of the stimulus set(Theunissen et al.,2001;Sharpee et al.,2004).In general,the strongest STAs preceded spikes by a lag of two movie frames(66ms).Preferred orientation and spatial frequency were calculated by finding the peak of the spatial Fou-rier transform of the STA at66ms lag.STA receptive fields were also fit to Gabor functions(a sinusoid with a Gaussian envelope),described by F͑xЈ,yЈ͒ϭA expͩϪxЈ22xϪyЈ22yͪcos͑2fxЈϩ͒,where xЈand yЈare rotated,and translated coordinates are defined by xЈϭcos(xϪx c)ϩsin(yϪy c)and yЈϭϪcos(xϪx c)ϩcos(yϪy c). Fits were performed in Matlab,based on routines provided by Michael Lewicki and colleagues.Contrast–response curves were generated from the response to drift-ing gratings with spontaneous rate subtracted,for the preferred orienta-tion determined as above.Because stimuli were presented at a preselected spatial frequency(0.04cpd)rather than the preferred spatial frequency of each unit,the peak firing rate is not indicative of the maximal respon-siveness of the unit,and the curves were thus normalized to maximum response of1.Normalized curves were fit to the Naka–Rushton equation (Naka and Rushton,1966):R(C)ϭg/(1ϩ(C/C50)b),where C is contrast, g is the gain,C50is the midsaturation contrast,and b is a fitting exponent that describes the shape of the curve.Statistical significance was determined by Mann–Whitney U test,ex-cept when otherwise stated.In the figures,*pϽ0.05,**pϽ0.01,and ***pϽ0.001.For figures representing the median of data,error bars show standard error of the median as calculated by a bootstrap.In other cases,error bars represent SEM.ResultsLaminar and cell-type identityWe recorded235isolated single units from27adult mice.Indi-vidual recording sessions consisted of simultaneous recordings of ϳ4–12isolated single units across the16sites of the multielec-trode array.Of these units,87%(204of235)were responsive to at least one episodic visual stimulus.Nonresponsive units were not included in analysis of tuning properties.We generally per-formed only one electrode insertion per animal to avoid damage to cortex from multiple penetrations and to maintain a stable anesthetic state without needing to redose.To verify the laminar location of each recording site of the multielectrode array,we measured in addition to electrode depth the LFP response to square-wave contrast reversal of a checker-board.As shown in Figure1B,the averaged responses show a large deflection startingϳ40ms after the contrast reversal,which varies in both amplitude and waveform across the recording sites. CSD analysis provides a method to transform the set of LFP recordings into the locations of current sources and sinks,with current sinks generally corresponding to sites of synaptic conduc-tances(Mitzdorf,1985;Swadlow et al.,2002).This transformation revealed a laminar distribution of activation in response to checker-board reversal(Fig.1C),with a current sink beginning in layer4,in which sensory input first arrives,spreading up to layer2/3,and finally a weak sustained sink in layer5.Retrospective histology (supplemental Fig.S1,available at as supple-mental material)confirmed the correspondence between CSD and laminar identity.Furthermore,layer4corresponded to the maximum negativity of initial dip in the LFP(Fig.1B,C)(sup-plemental Fig.S1,available at as supplemen-tal material),which allowed us to deduce the laminar location even for recordings that did not span the entire depth of cortexNiell and Stryker•Receptive Fields in Mouse Visual Cortex J.Neurosci.,July23,2008•28(30):7520–7536•7523。
