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1Visual Sciences, Research School of Biological Sciences, Australian National University, Canberra; 2Department of Anatomy and Histology, School of Medical Sciences and Institute for Biomedical Research and 3Visual Perception Unit, School of Psychology, University of Sydney, Sydney, New South Wales, Australia
Submitted 18 August 2005; accepted in final form 25 September 2005
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ABSTRACT |
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INTRODUCTION |
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; Hammett et al. 1994; Snowden and Hammett 1996). Electrophysiological studies investigating contrast adaptation in the primary visual cortex of cats and primates have shown that a sigmoidal contrast response function is generated when a neuron's spike rate is plotted as a function of stimulus contrast. Prolonged exposure to optimally oriented, high-contrast gratings leads to reductions in responsiveness to subsequently presented test stimuli, which produce rightward and/or downward shifts of a neuron's contrast response function (Bonds 1991; Carandini and Ferster 1997; Ohzawa et al. 1982, 1985; Sclar et al. 1989, 1990). Downward shifts are referred to as response gain control and rightward shifts as contrast gain control (for review see Ibbotson 2005).
The questions we address in this paper are: can nonoptimally oriented stimuli generate strong contrast adaptation and, if so, how common are such neurons in cat cortical areas V1 (area 17) and V2 (area 18; Payne and Peters 2002)? Furthermore, are these neurons found in selected regions of the cortical orientation maps? The question of adaptation to nonoptimal orientations is not new to the literature but a comprehensive study of response properties across all cortical layers based on a large cell sample has not been published. Moreover, this phenomenon has been investigated only in V1. Neurons in both V1 and V2 were included in this study to highlight any differences between these two cortical areas because they receive direct geniculate inputs originating mainly from X- and Y-cells, respectively (Burke et al. 1992; Dreher et al. 1992).
Below is a brief survey of available data on adaptation to nonoptimal grating orientations. Psychophysical studies have shown that contrast adaptation can be induced by gratings orthogonal to the test grating (Ross and Speed 1996; Snowden and Hammett 1992). In recordings from cat area V1, Vautin and Berkley (1977) showed that contrast adaptation was generated by gratings oriented orthogonally to the neuron's preferred orientation in two of 11 tested cells. Ohzawa et al. (1985) comment in their paper on contrast adaptation in cat V1 that they "have preliminary evidence that many cells may be adapted by presentation of stimuli of nonoptimal orientation." However, these data have not been made available in published form. In an abstract, Allison and Martin (1997) state that of 23 cat V1 neurons, the contrast response functions of only two cells were depressed after adaptation to orthogonal motion with a cross-oriented grating. Carandini et al. (1998) failed to show adaptation-related changes in spiking activity of V1 neurons after adaptation to orthogonal gratings in eight cells recorded intracellularly in cats and eight cells recorded extracellularly in monkeys. Some evidence of subthreshold activity changes was apparent from the cat data because the mean membrane potentials of the cells were slightly depolarized after adaptation to orthogonal gratings. The largest study of adaptation to orthogonal gratings was conducted by Sengpiel and Bonhoeffer (2002), who recorded from 80 neurons in layers 2 and 3 of cat V1. Although this paper is the most comprehensive study to date, 58 of the cells were recorded while the cats were anesthetized with high halothane concentrations, which they suggest obscured adaptation to orthogonal gratings. From the remaining 22 cells, 12 showed adaptation to orthogonal gratings, suggesting for the first time that cross-orientation adaptation may be a common phenomenon.
A range of studies have looked at changes in orientation tuning, rather than contrast response functions, after adaptation at various nonoptimal orientations in area V1 in both cat and monkey (e.g., cat: Dragoi et al. 2000; monkey: Dragoi et al. 2002; Kohn and Movshon 2004; Müller et al. 1999). These studies provide additional evidence that adaptation to nonoptimal orientations can alter subsequent responses to optimally oriented gratings and alter the preferred orientation of cortical cells. In cats the postadaptation orientation tuning functions were least affected by adaptation to orthogonally oriented gratings and most to adaptation at orientations that differed by 0–60° from the preferred (Dragoi et al. 2000).
Here we record from 119 neurons to optimally oriented gratings moving in both preferred and antipreferred directions and to moving gratings oriented orthogonally. Recordings were made from all six layers of cat cortical areas V1 and V2, with an emphasis on cells in layer 4 (36% supragranular layers, 41% layer 4, and 23% infragranular layers). We show that around 20% of cortical neurons exhibit contrast gain control only to optimally oriented gratings (orientation-selective adaptation), whereas 20% show similar amounts of contrast adaptation to all adapting directions (nonoriented adaptation). The majority of cells show intermediate levels of orientation-related adaptation (46%) and some do not adapt to any orientation (14%). This is the first study to correlate contrast adaptation to nonoptimally oriented gratings with the orientation, direction, and temporal frequency tuning properties of individual neurons. The results are discussed in the framework of intracortical circuits within the orientation maps of V1 and V2. Correlations between the rate of change in orientation tuning with electrode depth and the orientation tuning of contrast adaptation suggest that nonoriented adaptation may occur more often in pinwheel centers.
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METHODS |
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Experimental procedures complied with the guidelines of the Australian National Health and Medical Research Council and were approved by the Animal Experimentation Ethics Committee of the Australian National University. Data were collected from six adult cats of either sex weighing between 2.8 and 4.2 kg. Animals were initially anesthetized with ketamine HCl (20 mg/kg, administered intramuscularly; Ilium, Smithfield, NSW, Australia) to allow the trachea and right cephalic vein to be cannulated. The head was held in a stereotaxic frame using ear bars, a mouth bar, and head bolt attached to the skull at the midline 3 cm anterior to interaural zero. A craniotomy was performed 0–8 mm posterior and 2–8 mm lateral to interaural zero to allow access to areas V1 and V2 with vertical electrode penetrations (using 1–1.5% halothane anesthesia). Neuromuscular blockade was then induced with an intravenous injection of 50 mg of gallamine triethiodide (Flaxedil; Sigma, St. Louis, MO) in 2 ml of Hartmann's solution and maintained with continuous intravenous infusion of Flaxedil at a rate of 10 mg · kg–1 · h–1 in a 1:1:2 mixture of Hartmann's solution, 5% glucose, and 8% amino acid solution.
After initiation of paralysis, animals were ventilated with a pulmonary pump, and anesthesia was maintained with a mixture of gaseous halothane and a 2:1 ratio of N2O and O2. Expired CO2 was monitored continuously and maintained at 3.5–4% by adjusting the breath rate or stroke volume of the pulmonary pump. The electrocardiogram (ECG) and electroencephalogram (EEG) were monitored continuously, and body temperature was maintained at 38°C with an electric heating blanket. Halothane was administered at 0.5% when recording from neurons and at 1–1.5% whenever surgical intervention was required (i.e., craniotomy, new electrode tracks, or injections). Increases in ECG or changes in the EEG suggesting a reduction in anesthesia were met with increases in the percentage of inhaled halothane. Such events occurred very rarely. Intramuscular injections of Clavulox (1 ml; Pfizer, West Ryde, NSW, Australia), dexamethasone sodium phosphate (1 ml; Ilium), and atropine (0.05 mg/kg; Apex Laboratories, Somersby NSW, Australia) were administered daily.
The corneas were protected with zero-power rigid gas-permeable contact lenses. Pupils were dilated and accommodation was paralyzed with 1% atropine sulfate eye drops (Sigma). The nictitating membranes were retracted with 0.01% phenylephrine HCl eye drops (Sanofi-Synthelabo, New York, NY). Corrective lenses and a streak retinoscope were used to focus the stimulus on the retina at a distance of 57 cm in front of the animal. Artificial pupils (3 mm diameter) were placed in front of the eyes to reduce spherical aberrations. The locations of the optic disc and area centralis of each eye were plotted twice daily by reverse ophthalmoscopy.
Extracellular recordings were made with lacquer-coated tungsten microelectrodes (FHC, Bowdoinham, ME) that were driven by a piezoelectric drive (Burleigh inchworm and 6000 controller, Burleigh Instruments, Rochester, NY). Extracellular signals from individual units were isolated, amplified, and filtered, then acquired with a CED1401 interface and Spike2 software (Cambridge Electronic Designs, Cambridge, UK) sampled at 40 kHz. A Schmitt trigger was also used to trigger TTL pulses, which were passed to Spike2 to allow on-line data analysis.
Stimuli and initial analysis
Locations of the dominant eye and receptive field (RF) of each neuron were initially determined using a hand-driven light bar. The nondominant eye was covered; quantitative testing was performed on the dominant eye with visual stimuli produced by a VSG Series 2/5 stimulus generator (Cambridge Research Systems, Cambridge, UK) and presented on a calibrated monitor (Eizo T662-T, 100-Hz refresh, 1,024 x 768 pixels) at a viewing distance of 57 cm. The location and size of the classical receptive field, as well as the preferred orientation/direction, spatial frequency (SF), and temporal frequency (TF), were determined by calculating on-line tuning functions for each stimulus parameter. Receptive field size was determined in two ways: 1) a circular patch of moving grating centered on the middle of the RF was expanded in size to find which diameter produced the optimal response; and 2) an annulus of moving grating was centered on the RF and the diameter of the empty center was decreased until spiking responses could just be elicited, thus indicating the edge of the excitatory RF. The size tuning function generated by the first measure indicated the diameter at which the response saturated, whereas the size tuning function generated by the second method indicated the outer boundary of the excitatory RF. Both measures usually agreed but, when they differed, the result from method 2 was used to set the stimulus size.
The stimulus for testing contrast adaptation was a sine-wave grating of optimal SF and TF presented in a circular aperture the size of the classical receptive field. The aperture had a diameter of 2–10° and was surrounded by a gray of mean luminance (Lum; 50 cd/m2). Sine-wave contrast is defined as
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Adapted contrast response functions were collected in blocks, with the adaptor grating moving either in the neuron's preferred, antipreferred (preferred +180°), or orthogonal (preferred +90°) direction. Adaptation blocks consisted of 60 s of the adaptor followed by 0.5-s tests (aforementioned contrasts for 10 repetitions) interleaved with 4-s adaptation top-ups. Firing rates used to construct contrast response functions were calculated by taking the mean firing rate across repeats from 0.1 to 0.5 s after the test stimuli appeared. A full data set consisted of four contrast response functions: the nonadapted curve plus the three adaptation blocks. Often when a full data set was collected, we would collect another full data set using a different adapting contrast. Typically, the adaptors had a contrast of 0.32 and the contrast that elicited approximately 50% maximum firing during the nonadapted control, which was estimated from a contrast response function produced on-line. Adapting contrasts of 0.16 and 1 were also frequently tested. Nonadapted contrast response functions were also repeatedly collected and compared to ensure the stability of extracellular isolations. Whenever the controls differed significantly between collecting full data sets, the data were excluded from further analysis. To test the generality of the adaptation effects seen in response to orthogonal and antipreferred gratings, 19 neurons were also tested with adaptors oriented at +45 and +135 ° to the preferred direction in addition to the usual preferred, antipreferred, and orthogonal adaptors.
Quantitative analysis
SIGMOIDAL FITS.
In agreement with previous studies, the contrast response functions collected in the present study were suitably described by a sigmoid function (Fig. 1 A; Sclar et al. 1989). Sigmoid curves were fitted using the equation
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DIRECTION INDEX.
Direction selectivity was measured quantitatively using the direction index (DI)
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SIMPLE AND COMPLEX CELLS.
Neurons were classified on-line as simple or complex based on qualitative tests performed with the light bar (Hubel and Wiesel 1962) and off-line by the relative modulation produced by a sine-wave grating. That is, we assigned simple and complex neurons using the ratio between modulated responses at the stimulus temporal frequency (F1) and mean response (F0) of the spiking responses to drifting sinusoidal gratings (Ibbotson et al. 2005; Skottun et al. 1991). Simple cells had F1/F0 ratios >1, and complex cells had F1/F0 ratios <1. F1/F0 ratios were calculated from the SF and TF tuning tests, which presented stimuli for 3 s. Simple and complex cells were classified using the optimum SF/TF (Skottun et al. 1991) and, because no cell had an optimum TF <1 Hz (Fig. 4C), F1 values were calculated from at least three cycles.
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; Sclar et al. 1989). For our simple cells, the shapes but not the amplitudes of the contrast response functions were very similar using both the F1 and F0 amplitudes. However, the shifts in both c50 and Rmax were greater when using the F0 measure, showing that in our experiments it was a more sensitive gauge of contrast adaptation. These observations agree with recent intracellular recording studies in cat V1, which showed that the F1 component changes very little with contrast adaptation (Carandini and Ferster 1997; Sanchez-Vives et al. 2000a). Consequently, throughout this paper, contrast response functions for simple cells, as for complex cells, are generated using F0 amplitudes. Histology
At the end of the recording session electrodes were moved back and forth 10 times using the piezoelectric drive (amplitude 50 µm), always stopping at exactly the same depth. This was found to generate a buildup of cell damage produced by the tip of the electrode, which provided a good marker of the track depth without damaging the electrode by passing current through it. Then a different "lesion-electrode" was used to produce electrolytic lesions (4 µA, 5–10 s, electrode positive) at 2, 4, and 6 mm below the brain surface at a location displaced 3 mm medially from the recording site. This procedure generated clear lesions that could be correlated for depth with the recording tracks. By placing lesions at several known depths we were able to correct our track depths for the shrinkage commonly produced by fixatives.
Once depth markers were placed, animals were given a lethal dose of pentobarbitone sodium (120–180 mg depending on animal's weight) and immediately perfused with saline (0.9%) followed by 10% formol saline. Brains were extracted and cryoprotected in sucrose (30% in 0.1 M PB) for 2–10 days. Frozen sections (45 µm thick in the coronal plane) from 0 through 8 mm posterior to interaural zero were collected. Sections were mounted onto gelatin chrome aluminum–coated slides and counterstained with thionin for Nissl substance. The tissue was then examined using light microscopy to confirm the locations of electrode tracks. Cell locations were reconstructed by correlating the electrode depths with the bottom of the track after taking account of brain shrinkage based on lesion markers.
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RESULTS |
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Orientation tuning of contrast adaptation
Figure 1 shows contrast response functions for a representative complex neuron in V2. The polar plot on the left shows the response amplitudes following motion in the indicated directions with no prior adaptation. This cell was optimally responsive to horizontal gratings moving vertically upward. When the same grating was moved downward, strong excitation was also generated but with a significantly lower spike rate (t-test, P < 0.015). This neuron had a directional index of 0.22, so the cell was regarded as orientation selective but not direction selective (see METHODS). From the polar plot it is clear that vertically oriented gratings moving to the left or right did not produce responses that were significantly different from the mean spontaneous rate, which is indicated by the gray disc at the center of the polar plot. The orientation full-width half-maximum (FWHM) bandwidth is indicated by a double-headed arrow (41°).
The five graphs surrounding the polar plot show contrast response functions without prior adaptation (solid symbols) and after adaptation to gratings with contrasts of 0.32 moved in the directions indicated by the large arrows (open symbols). It is clear that for this cell contrast adaptation generated a downward and leftward shift in the contrast response functions. Two parameters were extracted from the fitted curves (solid lines): Rmax, which is the maximum firing rate minus the spontaneous rate; and c50, which is the contrast that generates an elevation in firing rate of half Rmax (see METHODS). For this neuron, adaptation is indicated quantitatively by the increases in c50 and decreases in Rmax. This cell is representative of neurons in which adaptation in all directions leads to strong adaptation regardless of the firing rate generated by the adapting stimulus. We refer to this as nonoriented adaptation.
Figure 2 shows four neurons that are representative of the spectrum of adaptation effects. The nonadapted contrast response function is represented by black dots, and responses after adaptation in the preferred, antipreferred, and orthogonal directions are shown as blue squares, green triangles, and red diamonds, respectively. Error bars represent SE values. The inset of Fig. 2 shows direction tuning polar plots for each neuron.
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For one V1 neuron and 18 V2 neurons we used adaptors oriented at +45 and +135° to the preferred direction in addition to the usual preferred, antipreferred, and orthogonal adaptors (e.g., Fig. 1). For all 19 neurons, the adaptation produced when these two additional stimuli were used was consistent with each cell's directional tuning characteristics and the adaptation produced by preferred, antipreferred, and orthogonal adaptors.
Population data
The results presented so far suggest a spectrum of adaptation effects. At one end of the spectrum are cells that exhibit orientation-specific adaptation (Fig. 2, B and C). At the opposite extreme are cells that show nonoriented adaptation (Figs. 1 and 2A), whereas most cells fall between these extremes (Fig. 2D). In addition to the cells that are part of the above spectrum, 17/119 neurons did not adapt to any stimulus orientation (nonadapting cells).
All population analysis used values of c50 shift and Rmax shift to quantify changes in each neuron's contrast response functions (see METHODS). Both of these measures can have values between –1 and 1. For c50 shift, 0 indicates no adaptation, whereas positive and negative values indicate rightward or leftward shifts of the contrast response functions, respectively. For Rmax shift, 0 indicates no adaptation, whereas positive and negative values indicate increases or decreases of the maximum firing rate, respectively. Figure 3 shows histograms in which the total number of full data sets (226) collected at a range of adapting contrasts from 119 units is plotted against c50 shift (left column) and Rmax shift (right column), after adaptation to optimally oriented gratings moved in the preferred (top row) and antipreferred directions (middle row), and to orthogonally oriented gratings moving at 90° to the preferred motion axis (bottom row). The means of the distributions of c50 shift and Rmax shift are noted in each panel (Fig. 3). It is evident that preferred direction motion produces a large rightward shift in c50 and a downward shift in Rmax for most tests (Fig. 3, A and B). Furthermore, c50 shift and Rmax shift show similar distributions after antipreferred direction motion (Fig. 3, C and D).
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It is clear from inspection of the contrast response functions in Figs. 1 and 2 that it is the shift produced by orthogonal adaptation that distinguishes the different adaptation types. Therefore for the rest of this paper we use c50 shift and Rmax shift from orthogonal adaptation to show the spectrum of orientation-related adaptation. Although no cells in our sample responded significantly to orthogonally oriented gratings, there was a clear spectrum of adaptation produced by this stimulus (Fig. 3E). For purely descriptive purposes we have set thresholds at values of c50 shift of 0.1 and 0.5 (see dashed vertical lines in Fig. 3E). Neurons with values of c50 shift <0.1 (n = 23) demonstrate orientation-selective adaptation, cells with values >0.5 (n = 24) demonstrate nonoriented adaptation, and cells between these values have intermediate adaptation properties (n = 55). The 17 nonadapting neurons were excluded from the categories outlined above. Therefore from all 119 cells, 20% exhibit orientation-selective adaptation, 20% nonoriented adaptation, 46% intermediate adaptation, and 14% are nonadapting. For cells showing orientation-selective adaptation, 15 units were located in V1 and eight units were located in V2. For cells showing nonoriented adaptation, 14 units were located in V1 and 10 units were located in V2. For cells showing intermediate adaptation, 24 were located in V1 and 31 located in V2. Seven nonadapting cells were recorded in V1 and 10 were recorded in V2.
Relationship between adaptation and orientation selectivity
Figure 4 shows the relationship between each cell's orientation tuning and the amount of contrast adaptation induced by orthogonal gratings. The top histogram in Fig. 4A shows the distribution of orientation bandwidths (FWHM amplitude: see Fig. 1, polar plot). All of the cells have orientation bandwidths that fall between values of 23 and 148°, with a mean of 57°. Values of c50 shift and Rmax shift from the orthogonal adaptation condition are plotted below the histogram as functions of the orientation bandwidths for all cells. Note that in most cells adaptation was studied with several adapting contrasts, so there are 226 points per scatterplot from 119 cells. Despite the relatively narrow orientation tuning bandwidths of most cells, bandwidth is poorly correlated with orthogonal c50 shift (R2 = 0.02) and orthogonal Rmax shift (R2 = 0.006). Therefore it is not possible based solely on the orientation tuning of a given cell to predict its orientation-related adaptation properties. It is also evident, as shown in Fig. 3E, that a small group of cells consistently show leftward shifts in c50 after orthogonal adaptation (negative values of c50 shift, e.g., below the dotted line in the middle panel of Fig. 4A). This orthogonal facilitation may indicate that cross-orientation adaptation reduces the influence of inhibitory inputs in a small proportion of cells. For simplicity, these cells are included in the orientation-selective category.
Relationship with direction selectivity
We define a cell as direction selective if its direction index (DI) is >0.66 (see METHODS). The top panel in Fig. 4B shows the distribution of directional indices across the entire cell population. Of our cell population 105 are not direction selective and 14 are direction selective. Together they show a wide range of orientation-related adaptation effects. The mean values of c50 shift for nondirectional and directional cells are 0.15 (±0.26) and 0.38 (±0.32), respectively. The mean values of Rmax shift for nondirectional and directional cells are –0.08 (±0.20) and –0.08 (±0.17), respectively. The values for nondirectional and directional cells are significantly different for c50 shift (t-test, P < 0.03) but not for Rmax shift (t-test, P > 0.98). The findings indicate that on average highly directional cells produce larger rightward shifts in c50 after adaptation to orthogonally oriented gratings than cells with directional indexes <0.66. The nonsignificant difference based on Rmax shift reflects a general finding that the maximum firing capacity of the cells is depressed by a similar amount regardless of the adaptation conditions (see Higher contrasts produce more adaptation, below, and Fig. 6).
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Relationship with temporal frequency
Figure 4C, top shows the distribution of optimum temporal frequencies for the 119 cells in this study. Note that the optimum TF was taken to be the value in our test grid that produced the highest spike rate rather than the peak of a fitted TF tuning curve, so the data points in Fig. 3C are aligned with specific TFs. The mean optimum TF for V1 cells was lower (3.6 ± 2.0 Hz) than that for V2 cells (6 ± 3.2 Hz). The values of c50 shift and Rmax shift for the orthogonal adaptation condition are plotted against each cell's optimum TF in the middle and bottom panels, respectively. It is clear from these plots that the amount of adaptation to orthogonally oriented gratings was not related to the TF of the stimulus. The small number of cells that had very negative values of c50 shift after orthogonal adaptation tended to have lower preferred TFs (>6 Hz). The finding that TF does not affect orthogonal adaptation agrees with the finding that adaptation effects were similar in V1 and V2 based on histological cell location (see Factors not linked to orientation-related adaptation).
Orientation-tuning of adaptation does not change with adapting contrast
For 83% of neurons, several full adaptation protocols were repeated with more than one adapting contrast. For neurons that were tested with more than one adapting contrast, 85% showed the same type of orientation-related adaptation across all adapting contrasts. As an example, Fig. 5 shows a neuron that was tested with three adapting contrasts (0.32, 0.66, and 1; same cell as Fig. 1). Despite the large differences in the contrast of the adapting stimuli, the contrast response functions arising from various adapting directions maintain their position relative to each other. In those units that had different properties at different adapting contrasts, there was a tendency to shift from orientation-selective adaptation to nonoriented adaptation as contrast increased.
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Our data indicate that Rmax shows no systematic changes as contrast increases, regardless of the adapting orientation. That is, across the population all contrasts cause similar reductions in Rmax. However, rightward shifts in c50 become larger as the adapting contrast increases, supporting previous observations that contrast gain control is a fundamental property of cortical adaptation (e.g., Ohzawa et al. 1985). Although this was shown previously, the data presented here constitute the first time that contrast gain–related changes have been shown for adaptation to both preferred and orthogonal orientations. To show the population data in a meaningful way, we have normalized the stimulus contrast into a value that indicates relative adapting strength (As). This measure takes account of the different values of c50 in the unadapted condition between cells
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Figure 6, A–C shows As plotted against Rmax shift for the three adapting directions. Although Rmax values generally decreased after adaptation, the amplitude of the decrease was not related to the adapting contrast. The low R2 values for fitted linear regression lines for the relationship between As and Rmax shift are shown on the plots. Note that a common adapting contrast was the estimate of the neuron's nonadapted c50 calculated on-line. As such, one might expect a large proportion of dots on the scatterplots to align at an As of zero. However, because the on-line estimation of c50 was only approximate, these As values scatter around zero. Figure 6, D–F shows As plotted against c50 shift for the three adapting directions. There was a strong relationship between adapting contrast and c50 shift for all the adapting directions of motion. Relatively high contrasts (positive As values) produced increases in c50, whereas relatively low contrasts (negative As values) produced lower c50 shift values. The relationship between adapting contrast and c50 shift was linear, as revealed by good fits to linear regression lines (Fig. 6, D–F; R2 values, inset). In summary, adaptation usually reduces maximum firing rates regardless of contrast, but changes in c50 are related linearly to adapting contrast for all adapting orientations/directions.
Correlation of orientation-related adaptation and orientation columns
We made a total of 10 vertical electrode penetrations in six cats. All tracks were made adjacent to the marginal sulcus (MS) with three tracks penetrating only area V1 and seven rostral tracks penetrating first area V1 then V2 (for an example of the latter track positions and orientations see Fig. 1 in Hubel and Wiesel 1965). The V1/V2 border was noted during recording when a marked shift in receptive field position occurred and was confirmed histologically using the increased thickness of layer 3 as a measure (Payne 1990). We recorded from 43 units located in the supragranular layers (1–3), 49 units in layer 4, and 27 units located in the infragranular layers (5–6).
The trajectory of our electrode penetrations was such that each track passed through a number of orientation columns (Bonhoeffer and Grinvald 1991; Hubel and Wiesel 1974). The average distance between recorded units was 176 µm (SD 236). Along tracks made parallel to the cortical surface, cells of the same adaptation type (e.g., orientation-selective adaptation vs. nonoriented adaptation) tended to cluster together. Of the units recorded in this study, 54/119 (45%) were grouped in clusters of two or more cells that were classified as the same contrast adaptation type (based on the divisions shown in Fig. 3E). There was no statistical difference in the distance between units for those cells grouped in clusters and cells that were not surrounded by similarly classified cells (P > 0.85, Student's t-test). Orientation pinwheels are spaced approximately every 1 mm on the cortical surface (Bonhoeffer and Grinvald 1991; Das and Gilbert 1999; Maldonado et al. 1997). Given the average distance between recorded units relative to the distance between pinwheel centers, the observed proportion of clustering is consistent with the scale of the columnar orientation maps of the primary visual cortex, suggesting that this organization may relate to the adaptation effects described herein.
To quantify a relationship between orientation-related adaptation types and the orientation columns of the cortex we performed the following calculations. We identified cells as belonging to one of four adaptation categories: 1) orientation-selective adaptation; 2) intermediate; 3) nonoriented adaptation; and 4) nonadapting (as shown in Fig. 3E). We then selected cells that were isolated consecutively (no more than 400 µm apart) that were in the same adaptation category. The average distance between cells in this subset of 46 neurons was 131 µm (SD 111). The maximum interunit distance of 400 µm was chosen because it is approximately half the diameter of an orientation hypercolumn. For all these regions of the tracks we calculated the mean difference in orientation tuning between consecutive recordings from the same adaptation type. We also calculated the mean difference in depth between those recordings. A ratio of those two values was then calculated for the different cell categories. We did not isolate any nonadapting cells in consecutive recordings, so they are not discussed further. We were able to identify 11, 21, and 14 units in the orientation-selective adaptation, intermediate, and nonoriented adaptation categories, respectively. Figure 7 plots the mean orientation/depth ratios from those cells.
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Factors not linked to orientation-related adaptation
ABSOLUTE SPIKING RATE. The response of an unadapted neuron to unity contrast was used to approximate the maximum firing rate that could be elicited using our stimuli. The average maximum firing rate for all neurons that showed clear adaptation to preferred orientations was 88 (SD 66) spikes/s. Of the 17/119 neurons that did not adapt, the mean firing rate at maximum contrast was 52 (SD 38) spikes/s. The difference in firing rate between adapting and nonadapting neurons was significant (P < 0.0004, Student's t-test). Therefore neurons not showing contrast adaptation to our protocol have significantly lower maximum firing rates than those of neurons that do adapt. However, between the cell types that do adapt, there was no significant difference in maximum firing.
Given the relationship between firing rate and stimulus orientation/direction (insets: Figs. 1, 2, and 5), we needed to determine whether the spiking rate in the adapted state correlated with the amplitude of adaptation. For each cell, the mean spiking rate during the top-up adaptation periods (normalized to the maximum rate across directions) was used as a measure of spiking rate in the adapted state. For each measure of firing rate produced by a particular adapting stimulus, Rmax shift and c50 shift were used to determine the amount of adaptation. For each cell we compared all combinations of contrast and direction. There was a poor correlation between firing rate in the adapted state and the amount of adaptation. For Rmax shift the relationship between adapted spiking rate and adaptation had a slope <0.001 with an R2 <0.001, whereas for c50 shift the slope was 0.21, with an R2 = 0.001. Thus the firing rate of a neuron when it is in an adapted state does not predict the amplitude of adaptation, regardless of adapting orientation.
OTHER CELL PROPERTIES. We correlated the values of c50 shift derived from the orthogonal adaptation protocols with three further cell attributes: 1) simple versus complex cells; 2) cells located in V1 versus V2 (see Population data, above); and 3) the locations within specific cortical layers (see Correlation of orientation-related adaptation and orientation columns, above). We do not present these correlations in any formal way because there were no significant differences in the relative proportions of any of the orientation-related adaptation properties when comparing these factors.
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DISCUSSION |
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Several authors have shown that orthogonally oriented gratings can generate contrast adaptation, although the reported cell populations are small (e.g., Allison and Martin 1997; Ohzawa et al. 1985; Sengpiel and Bonhoeffer 2002; Vautin and Berkley 1977). Herein we present data from 119 neurons and conclusively show that a relatively large proportion of neurons in V1 and V2 show adaptation to orthogonal stimuli. Moreover, we show that a spectrum of orientation-related adaptation effects exists.
Influence of anesthesia
Sengpiel and Bonhoeffer (2002) recorded from 80 cells in layers 2 and 3 of cat V1. They found very few cells that adapted to orthogonal gratings when using 0.7% inhaled halothane as the anesthetic. However, in one cell where the percentage of halothane was lowered to 0.4%, and in cells recorded using 1.1% isoflurane, more orthogonal adaptation was observed. The amount of orthogonal adaptation was likely related to the dosage of anesthesia rather than the type of anesthetic. In fact, Villeneuve and Casanova (2003) suggest that isoflurane is inferior to halothane for single-cell recordings because it has stronger depressive effects at equipotent concentrations. Our usage of 0.5% halothane probably generated similar effects to the 0.4% of Sengpiel and Bonhoeffer (2002) because both of these relatively low concentrations revealed the adaptation.
Measures of adaptation
Contrast gain control refers to lateral shifts of the contrast response function that center the c50 on prevailing contrasts, whereas response gain control refers to a vertical compression of the contrast response function that causes a general decrease in firing (for review see Ibbotson 2005). Our data support and extend the well-known observation that contrast gain control is the prevalent effect of contrast adaptation in the striate cortex (Bonds 1991; Carandini and Ferster 1997; Ohzawa et al. 1982, 1985; Sclar et al. 1989). Although many of our neurons showed at least some decrease in maximum firing rates to all adapting orientations (reduced response gain), this decrease was similar for most contrasts. Conversely, shifts in c50 to all adapting orientations were linearly related to the adapting contrast. Whereas single-unit data usually show some evidence of covariance between contrast gain and response gain, the population data reveal that only changes in c50 depend on the relative strength of the adapting stimulus. Therefore from population coding and behavioral perspectives contrast gain should be the dominant adaptive effect. Where our data extend previous knowledge is in showing that contrast gain control is also the dominant adaptive effect for adaptation to optimally oriented gratings moving in the antipreferred direction and to orthogonally oriented gratings, thus supporting mechanisms in which a unified gain control mechanism works across the cortical surface in areas V1 and V2 (Bonds 1991).
Sengpiel and Bonhoeffer (2002) recorded from 22 cells in layers 2 and 3 of cat V1 under isoflurane anesthesia. From this population, they identified 12 (55%) that adapted to orthogonal gratings. This is similar to the proportion of cells in our population (66%) that showed at least some adaptation to orthogonal orientations (nonoriented and intermediate groups combined). Despite the similarities in these percentages, their test procedure and analysis methods create some problems in interpreting their data. First they collect responses at only three test contrasts for each adapting contrast (e.g., adapt at 0.07 and test at 0.035, 0.07, and 0.14). Second, the highest adapting contrast for which a three-point contrast response function was collected was 0.28 and the highest contrast presented was 0.56. Therefore their data spanned only half the available range of contrasts and tended to capture only the rising section of neurons' contrast response functions. Third, they assess adaptation with a statistical method that fits their data to two models: nonadapting and contrast gain control. If their adaptation index (AI) is >1 it favors contrast gain control and if it is <1 it favors nonadaptation. Even for optimally oriented gratings, the majority of their AI values are <1, revealing very few neurons that showed clear contrast gain control.
A limitation of their model-driven analysis method is that it does not take account of changes in the slope and maximum amplitude of the contrast response functions caused by changes in response gain (e.g., their Fig. 2B). As discussed above, we found that response and contrast gain covaried in virtually all cells. Therefore in an adapting cell where response and contrast gain occur together, neither of the models in the analysis reported by Sengpiel and Bonhoeffer (2002) would be entirely appropriate. If cells had been tested with a wider range of contrasts it is possible that the influence of response gain control would have been revealed. We are confident that our methods of data collection (including anesthetic state) and analysis provide sensitive measures of adaptation that take account of both response and contrast gain control.
Generating different types of orientation-related adaptation
Bonds (1991) noted that network-driven contrast adaptation seems advantageous over the independent adaptation of single neurons because the visual system must function as a whole, and uniform gain control across the network would preserve the neuronal activity gradient representing the visual scene independent of adaptation state. Recent intracellular studies have shown that contrast adaptation produces hyperpolarization of the membrane potential, which may partially arise from intrinsic membrane mechanisms (Carandini and Ferster 1997; Sanchez-Vives et al. 2000a,b). However, Sanchez-Vives et al. (2000a) caution that intrinsic membrane mechanisms must work together with synaptic mechanisms to produce contrast adaptation because sinusoidal current injection produced less hyperpolarization than visual stimulation and adaptation can be produced without action potentials (this was also observed in the present study).
A likely site for the synaptic mechanisms involved in contrast adaptation is in local intracortical networks, where a range of synaptic interactions have been reported (e.g., Monier et al. 2003). Single-unit and optical imaging studies have shown that orientation selectivity is organized in a columnar fashion across primary visual cortex with iso-orientation regions that converge at pinwheel centers (Bonhoeffer and Grinvald 1991; Hubel and Wiesel 1974; Maldonado et al. 1997). Furthermore, individual cortical neurons receive inputs from local networks encompassing 500–800 µm of the surrounding cortex independent of orientation preference (Das and Gilbert 1999). Therefore the orientation information carried by a cell's local network would depend on its location within the orientation map. Neurons located in pinwheel centers would receive inputs with a broad range of orientation preferences, whereas neurons located in iso-orientation regions would receive inputs with similar orientation preferences (Das and Gilbert 1999; Dragoi et al. 2001; Monier et al. 2003).
The continuum of adaptation effects seen in the present study is reminiscent of the continuum of effects seen in the adaptation-dependent orientation plasticity of area V1. Dragoi et al. (2001) showed that neurons located in iso-orientation regions, with neighbors that prefer similar orientations, showed minimal changes in orientation tuning after adaptation to nonoptimal orientations. Neurons located at pinwheel centers, with neighbors that prefer a broad range of orientations, showed a large degree of orientation plasticity. The local network inputs that influence orientation plasticity may be linked to contrast adaptation, as studied here.
Sengpiel and Bonhoeffer (2002) proposed that if a neuron was located in a pinwheel center and received adaptive inputs from neighboring cells with various orientation preferences, nonoriented adaptation might be expected. Conversely, if a neuron was located in an iso-orientation region it might be expected to exhibit orientation-selective contrast adaptation. However, when recording selectively from pinwheel centers and iso-orientation domains by guiding their electrodes using optical imaging of the cortical surface, they found no significant differences based on their AI measure (see Measures of adaptation, above).
In our study there appears to be a correlation between the rate of change of orientation and the type of adaptation. In areas with the greatest rate of change in orientation we more frequently isolated cells with nonoriented adaptation properties. Conversely, in areas with little orientation change we identified cells with orientation-selective adaptation. This finding fits with the theory proposed by Sengpiel and Bonhoeffer, but not with their data. When the effect of anesthesia is taken into account, their reliable data on correlating orientation maps to adaptation are based on 22 cells and our data are based on 46 cells. Because both studies rely on a small data set and show conflicting findings, future studies using appropriate anesthesia, intrinsic optical imaging techniques, and analysis using full contrast response functions will constitute the only method for clarifying the issue.
An alternative theory to explain nonoriented adaptation is that such cells inherit their adaptive properties from precortical neurons with nonoriented center-surround receptive fields [retina: Baccus and Meister 2002; Chander and Chichilnisky 2001; dorsal lateral geniculate nucleus (LGNd): Solomon et al. 2004]. Although many studies in cat LGNd did not uncover contrast adaptation (Bonds 1991; Movshon and Lennie 1979; Ohzawa et al., 1985), the high-contrast adaptation protocol used by Sanchez-Vives et al. (2000a) did reveal modest LGNd contrast adaptation (also see Shou et al. 1996). However, it is unlikely that this modest adaptation could fully account for the large shifts in contrast response functions observed in the present study, and adaptation in the LGNd cannot explain the orientation-selective adaptation seen in many cells. We therefore consider a cortical network model of adaptation properties to be the most likely mechanism leading to the spectrum of orientation-related adaptation properties that we have observed.
Orientation-related contrast adaptation across brain areas
Although both areas V1 and V2 constitute the primary visual cortex in the cat, their direct input from the LGNd is quite different (for review see Dreher 1986; Dreher et al. 1980; Payne and Peters 2002). Area V1 receives its principal dorsal thalamic input from X-type cells in the LGNd, whereas area V2 receives its LGNd input mainly from Y-type cells and has no X-type input (for reviews see Burke et al. 1992; Dreher et al. 1980, 1992; Stone and Dreher 1973). Despite receiving different input from the LGNd, cells in areas V1/V2 show a similar spectrum of orientation-related adaptation effects. These results correlate well with the finding that nearly half of X-type and Y-type LGNd neurons exhibit some degree of contrast adaptation (Shou et al. 1996). Furthermore, the strong interconnectivity between these two areas could also account for their similar adaptation properties (Freund et al. 1985a,b; Hollander and Vanegas 1977; Humphrey et al. 1985a,b; Price 1985; Price et al. 1994).
Contrast adaptation to orthogonal gratings and cross-orientation suppression
Neurons in V1 tend to produce temporal frequency tuning functions that peak at lower values (<4 Hz) than cells in V2 (2–8 Hz) (Movshon et al. 1977). These findings are similar to ours, which found mean optimum TF tuning of 3.6 and 6.0 Hz for V1 and V2, respectively. Allison et al. (2001) found that cross-orientation suppression of V1 cells was strongest when the mask grating had a TF of about twice that of the neuron's preferred TF. They suggest that cross-orientation suppression in V1 might arise from oriented V2 inputs. In the current study, we showed that the magnitude of contrast adaptation produced by orthogonal gratings did not depend on the adapting grating's TF and was similar for neurons in V1 and V2. This result suggests that cross-orientation suppression and contrast adaptation are produced by different mechanisms, with the possible exception of the small number of neurons that showed orthogonal facilitation after contrast adaptation. Monier et al. (2003) reported that the diversity in the way inhibitory and excitatory inputs are combined in V1 could result from nonhomogeneous lateral intracortical connections. The majority of evidence suggests that contrast adaptation mainly affects excitatory inputs (e.g., Carandini and Ferster 1997). We found that orthogonal facilitation, which may result from adaptation of cross-orientation inhibition, was extremely rare. This result agrees with the finding that neither 
-aminobutyric acid nor bicuculline administration affects contrast adaptation in V1 (DeBruyn and Bonds 1986; McLean and Palmer 1996; Vidyasagar 1990).
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: M. R. Ibbotson, Visual Sciences, Research School of Biological Sciences, Australian National University, Canberra, ACT, Australia 2601 (E-mail: Michael.Ibbotson{at}anu.edu.au)
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