The response of a neuron in striate cortex to an optimally oriented stimulus is suppressed by a superimposed orthogonal stimulus. The neural mechanism underlying this cross-orientation suppression (COS) may arise from intracortical or subcortical processes or from both. Recent studies of the temporal frequency and adaptation properties of COS suggest that depression at thalamo-cortical synapses may be the principal mechanism. To examine the possible role of synaptic depression in relation to COS, we measured the recovery time course of COS. We find it too rapid to be explained by synaptic depression. We also studied potential subcortical processes by measuring single cell contrast response functions for a population of LGN neurons. In general, contrast saturation is a consistent property of LGN neurons. Combined with rectifying nonlinearities in the LGN and spike threshold nonlinearities in visual cortex, contrast saturation in the LGN can account for most of the COS that is observed in the visual cortex.
The response of a neuron in striate cortex to an optimally oriented test grating is attenuated by an overlapping orthogonal mask grating (Morrone et al. 1982, 1987). The mask does not have to be perpendicular as it suppresses response to the test grating over a wide range of orientations (Bonds 1989; DeAngelis et al. 1992; Morrone et al. 1982). This cross-orientation suppression (COS) has been studied extensively, and it has generally been thought to be caused by intracortical inhibition (Blakemore and Tobin 1972; Bonds 1989; Carandini and Heeger 1994; Carandini et al. 1997; DeAngelis et al. 1992; Heeger 1992; Morrone et al. 1982, 1987; Sengpiel et al. 1998). The mechanism of COS has functional implications because it may play a role in important properties of visual cortical neurons, such as the refinement of orientation selectivity (Carandini and Ringach 1997; Chapman and Stryker 1992; Lauritzen et al. 2001; Somers et al. 1995; Vidyasagar et al. 1996), and spatial frequency selectivity (Bauman and Bonds 1991), and as a component of contrast normalization (Carandini and Heeger 1994; Heeger 1992).
Because COS can be induced by a wide range of mask orientations and spatial frequencies, and cells in the LGN are also broadly tuned for these parameters, it is plausible that the mechanism for COS originates in the LGN (Bonds 1989; DeAngelis et al. 1992; Walker et al. 1998). However, this explanation is not consistent with the experimentally shown lack of COS in LGN cells (Bauman and Bonds 1991) and the fact that geniculate afferents to the visual cortex seem to be exclusively excitatory (Creutzfeldt and Ito 1968; Garey and Powell 1971; Tanaka 1985). Based on these arguments, several studies have concluded that the source of COS is intracortical inhibition from a pool of cortical neurons exhibiting a wide range of orientation and spatial frequency preferences (Bauman and Bonds 1991; Bonds 1989; DeAngelis et al. 1992; Heeger 1992; Morrone et al. 1982; Sengpiel et al. 1998; Walker et al. 1998).
In recent work, synaptic depression at thalamo-cortical synapses has been proposed as the mechanism underlying COS in the visual cortex (Carandini et al. 2002; Freeman et al. 2002; Mrsic-Flogel and Hubener 2002). Synaptic depression is a form of short-term synaptic plasticity in which the efficacy of a given synapse is temporarily reduced according to its recent activity level (Abbott and Nelson 2000; Chance et al. 1998). However, there are temporal aspects of synaptic depression in relation to COS that require examination. In the study reported here, we studied the recovery time course of COS and find it inconsistent with typical temporal properties of synaptic depression. This makes it unlikely that synaptic depression is a major mechanism underlying COS in the visual cortex.
As an alternative to the synaptic depression hypothesis, we propose a feedforward mechanism for COS, in which suppression originates in the LGN through a rectifying nonlinearity and contrast saturation, and that COS is strengthened in the visual cortex by a spike threshold nonlinearity. Our measurements of contrast saturation in LGN are consistent with this notion. After the conclusion of our experimental work, two relevant publications have appeared. In one, intracellular techniques were used to study COS (Priebe and Ferster 2006). The main finding was that cross-oriented stimuli suppressed both synaptic inhibition and synaptic excitation. A feedforward model provided a prediction of COS. In the second study, COS was found to be rapid. From other temporal measurements, COS was suggested to be caused by feedforward signals or rapid intracortical neuronal paths (Smith et al. 2006).
All procedures complied with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Extracellular recordings are made with epoxy coated tungsten microelectrodes (A-M Systems) in the striate cortex of anesthetized and paralyzed mature cats (5–8 mo, 2.5–3.5 kg). Anesthesia is induced with thiopental sodium intravenously and maintained at an appropriate rate determined individually for each cat. A tracheal cannula is positioned and the animal is artificially ventilated (25% O2-75% N2O) at a rate adjusted to maintain expired CO2 at 4–5%. Temperature is maintained at 38°C. After the anesthetic level is stabilized at a constant rate of thiopental sodium, the cat is paralyzed with an intravenous infusion of pancuronium bromide (0.2 mg/kg/h). EEG, ECG, heart rate, temperature, and end-tidal CO2 are monitored during the experiment. A craniotomy is performed over area 17 and LGN. The dura is resected and covered with agar and wax to prevent drying and to reduce pulsation. For area 17 recording, electrode penetrations are made along the medial bank of the postlateral gyrus, 4 mm posterior and 2 mm lateral from the Horsley-Clarke origin (Horsley and Clarke 1908) at an angle of 10° medial and 20° anterior. For LGN recording, vertical electrode penetrations are made 6 mm anterior and 9 mm lateral from H-C zero. Small position adjustments of our electrodes are made to enter the LGN near the representation of the area centralis.
Single units are isolated by the shapes of their spike waveforms. Initial estimates of each neuron's tuning parameters are obtained qualitatively using computer-controlled manipulation of drifting sinusoidal gratings. Quantitative measurements of tuning functions for orientation, spatial frequency, temporal frequency, size, ocular dominance, and stimulus contrast are performed. Response amplitude is taken as the mean firing rate for complex cells or as the mean amplitude of the first harmonic of the response for simple cells and LGN cells. Because contrast gain and dynamic response range are apparently similar for X and Y cells (Hartveit and Heggelund 1992), we did not use this classification system for our LGN cell sample. The stimuli are presented to the dominant eye (responsive eye for LGN cells), whereas the nondominant eye (unresponsive eye for LGN cells) views a blank CRT screen of the same mean luminance as the gratings.
Recovery time course of COS
After the initial characterization of a cell in area 17, we examined the responses to an optimal test stimulus before, during, and after the presentation of an orthogonal mask, as shown in Fig. 1. Test and mask stimuli are sinusoidal drifting gratings at optimal and orthogonal orientations, respectively. The temporal frequencies of the mask stimuli are predetermined through a separate protocol (Allison et al. 2001; Freeman et al. 2002; Li et al. 2005). In brief, the temporal frequency of the test grating is fixed to the optimal for the cell while that of the mask is varied from 0.5 to 25 Hz. The temporal frequency that elicits maximum suppression is used for the mask stimulus in this experiment. For our population of cells, 4–10 Hz produced maximum COS values. To examine the recovery time-course of COS, we measured the response to the test stimulus after presentation of a 0.5-s mask. After the mask is presented, five delay periods are used before the test stimulus (0, 0.5, 1, 2, and 4 s as depicted in Fig. 1C). We also measured responses to test-only (Fig. 1A) and to test plus mask (i.e., plaid) conditions (Fig. 1B). Presentations are randomized with 10-s interstimulus intervals. Blank trials (i.e., no stimulus) are also interleaved with stimulus presentations. The contrast of both the test and mask is 50%. The entire condition set is repeated 10–20 times, and responses are averaged across trials.
We recorded from a total of 160 cells. Of these, 89 were from LGN and 71 were from visual cortex. Of the cortical cells, 25 and 46 were classified as simple and complex types, respectively.
Recovery time-course of COS
To consider synaptic depression as a principal mechanism of COS, we first note a predicted recovery time-course that this process would require. Specifically, responses to the test stimulus after a mask should recover exponentially toward the test-only response condition (Abbott et al. 1997; Chance et al. 1998; Varela et al. 1997). This prediction is quantified as (1) where R represents the response of the neuron to the test stimulus after the mask, a is the mean response to the test-only stimulus, b is the mean response to the plaid stimulus, and T is the time constant for synaptic depression (Abbott et al. 1997; Chance et al. 1998; Varela et al. 1997).
Reported time constants for a fast form of synaptic depression are generally between 200 and 600 ms (Abbott et al. 1997; Chance et al. 1998; Varela et al. 1997). In our analysis, we use these values (i.e., T = 200 and 600 ms) as upper and lower bounds to predict the responses of cortical cells. To make these predictions suitable for our measurements, we averaged R(t) across the first 500 ms of each response.
Figure 2 A shows data from a representative cortical simple cell where the responses are plotted as a function of the delay between mask and test stimuli. The top and bottom horizontal dashed lines represent responses to the test and plaid, respectively. Dotted lines above and below the dashed lines represent ±SE of the mean value. All responses represent the average neural spike rate during the first 500 ms of the test stimulus. This example cell exhibits clear suppression to the plaid stimulus. The mean responses to the test (top dashed line) and plaid (bottom dashed line) are 42.8 and 23.9 spikes/s, respectively, i.e., there is a 44% response reduction when the mask is superimposed on the test stimulus. If the mechanism underlying this suppression was synaptic depression, we would expect the response to the test stimulus to be reduced for some period after removal of the mask because synaptic depression is known to have a prolonged recovery time (Abbott et al. 1997; Chance et al. 1998; Varela et al. 1997, 1999). However, responses to the test grating for all temporal delay conditions (0, 0.5, 1, 2, or 4 s, empty circles) are nearly identical to those of the test-only condition (top dashed line).
This result is consistent across a population of cortical cells (n = 36). The mean response curve for all cells tested is shown in Fig. 2B, in which responses are normalized by the response to the test-only condition. On average, a simultaneously presented mask stimulus suppresses the response to a test stimulus by 42% (mean normalized response = 0.58 ± 0.05 SE). However, the mean normalized response to the test after mask stimulus is 1.05 ± 0.05. This shows that recovery from the suppressive effects of the mask is very rapid. To quantify this effect across our population, we calculated the magnitude of recovery for each response and expressed it as a percentage of the suppression induced by the plaid. Figure 2C is a population histogram of the values we obtained for the 0-s delay condition. A value of 100% represents complete recovery, whereas 0% indicates the response is suppressed to the same degree as that of the plaid. Consistent with the data of Fig. 2B, the responses of most neurons (20 of 36) are essentially unaffected by the preceding mask stimulus (percent recovery ≈ 100%). This applies equally to both simple and complex cells (P = 0.5, t-test). A model of synaptic depression predicts percent recovery values between 60 and 30% (up and down triangles) for assumed time constants of 200 and 600 ms (Abbott et al. 1997; Chance et al. 1998; Varela et al. 1997), respectively. These results suggest that synaptic depression processes are too slow to account for the rapid recovery from a briefly presented mask stimulus.
The data presented in Fig. 2, A–C, depict response magnitudes averaged over a 0.5-s time window. However, this analysis may be insensitive to rapid changes mediated by especially fast forms of short-term synaptic depression. To examine this possibility, we compare poststimulus time histograms (PSTHs) for the following three stimulus conditions: 1) the test-only stimulus (Fig. 2, D and E, empty circles), 2) the test stimulus after the mask (0-s delay condition; Fig. 2, D and E, filled circles), and 3) the plaid stimulus (Fig. 2, D and E, empty squares). Only responses from complex cells are included in this analysis because the onsets of simple cell responses are related to the phase of the drifting grating and are therefore not suitable for resolving fast response onset times. Results for a representative complex cell are given in Fig. 2D, and the population averaged responses are shown in Fig. 2E. Consistent with the above analysis, there is no significant difference between the test-only response time course (empty circles) and the test after mask response time-course (filled circles). This is the case even during the first 100 ms of the response. For comparison, the response to the plaid stimulus is clearly suppressed at these early time-points. If COS in visual cortex were mediated by synaptic depression, we would expect a mask stimulus to induce suppression during the initial part of a subsequent test stimulus. However, there is no evidence of this type of suppression.
Finally, we note that studies of cells in visual cortex in which stimuli have been presented in random sequence show that firing rate increases have relatively long latencies (∼20 ms) compared with decreases (Bair et al. 2002; Smith et al. 2001). This delay has been cited as evidence in support of a model of synaptic depression (Carandini et al. 2002; Freeman et al. 2002). However, 20 ms is substantially shorter than the recovery times for synaptic depression typically reported in the literature (Abbott et al. 1997; Chance et al. 1998; Varela et al. 1997, 1999). Furthermore, the origin of this delay is not clear because a number of different mechanisms could cause a delay between the onset and offset of firing rates (Bair et al. 2002).
Linearity and the LGN
The analysis presented above shows that synaptic depression at thalamo-cortical synapses is unlikely to be the principal mechanism for COS in visual cortex. Other work on the temporal and adaptation properties of COS suggests that the primary mechanism underlying COS operates before the formation of cortical receptive fields (RFs) (Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). Therefore COS in visual cortex is likely to originate from the LGN itself, rather than thalamo-cortical synapses. Previous studies of COS have generally overlooked the response nonlinearity of neurons in the LGN (Bonds 1989; Carandini and Heeger 1994; DeAngelis et al. 1992; Sengpiel et al. 1998; Walker et al. 1998).There are two major nonlinear inputs from LGN cells to cortical simple cells: spike rectification and contrast saturation in the LGN. The former refers to the fact that LGN responses cannot be decreased below zero and the latter obtains because LGN cells do not generally respond proportionally to stimulus contrast. Although contrast saturation in LGN neurons is less pronounced than that in visual cortex (Ohzawa et al. 1985; Sclar et al. 1990), an assumption of linearity is not warranted. To determine the degree of nonlinearity, we measured contrast response functions for a population of cells in the LGN (n = 74) for a wide range of contrast levels (1, 2, 4, 7, 14, 26, 50, and 100%).
Figure 3 A shows an example LGN contrast response function that exhibits a modest degree of saturation (empty circles). The firing rates at 50 and 100% contrast are 86 and 91 spikes/s, respectively. A neuron without contrast saturation would exhibit a substantially larger response at 100% contrast (172 spikes/s) based on its response to the 50% contrast stimulus. To quantify the effects of contrast saturation, we calculated a contrast saturation index (CSI). This index is based on neural responses to two successive and adjacent contrast values that are used to determine contrast response functions. The lower of two successive levels is used to predict responses to the higher contrasts. This prediction, based on the assumption of a linear contrast response function, is compared with the measured response. CSI values of 0 and 0.5 represent, respectively, linear and full contrast saturation. Formally, CSI is defined as (2) where Rmeasured and Rpredicted are, respectively, measured and predicted responses, as described above.
The example cell in Fig. 3A exhibits contrast saturation over a wide range of contrast levels. For contrasts above 7%, CSI increases monotonically with stimulus contrast (filled circles, dashed lines). Different levels of contrast saturation are observed across our population of LGN cells. Figure 3B shows the distribution of CSI values for the 50 and 100% response pairs. In our sample, the mean CSI is 0.33 ± 0.02 (SE, n = 74). This indicates that contrast saturation is a consistent property of LGN cells. To study the contrast saturation properties for different contrast levels, CSIs are calculated for two adjacent contrasts (Fig. 3C). The numbers of cells for 7 and 14, 14 and 26, 26 and 50, and 50 and 100% contrasts are 44, 70, 74, and 74, respectively. In all cases, contrast pairs are used only for responses that are significant (P ≤ 0.05, t-test). Consistent with the example cell for which data are shown in Fig. 3A, the strength of contrast saturation increases approximately monotonically with stimulus contrast.
Predicted suppression through a feedforward model
The experimental data we present here show clearly that COS in visual cortex recovers rapidly after the presentation of a mask stimulus (Fig. 2) and that most neurons in the LGN exhibit some degree of contrast saturation (Fig. 3). The first result suggests that synaptic depression is unlikely to be the principal source of COS in the visual cortex. The second finding indicates that contrast saturation at the LGN level may play an important role in COS. To explore this possibility, we use a physiologically plausible model to propose that COS arises from response nonlinearities of LGN cells and is strengthened in visual cortex by spike threshold nonlinearities.
Our model follows the serial processing notion by which orientation selectivity is derived from excitatory LGN input (Hubel and Wiesel 1962; Reid and Alonso 1995). It follows closely the construction of a previous model used to explore synaptic depression in visual cortex (Carandini et al. 2002). The cortical simple cell RF is determined by thalamocortical excitaiton. on and off cortical RF subregions arise from on-center and off-center LGN cells. The excitation is accompanied by subtractive inhibition of a “push-pull” form. Both excitation and inhibition are included through on-center and off-center neurons. Although intracortical inhibition could be included in this construction (Palmer and Davis 1981; Troyer et al. 1998), our approach is for inhibition to be derived exclusively from LGN. In our model, LGN cells operate linearly but have an added nonlinearity. The nonlinearity is established by assuming a resting spike rate of LGN cells of 10 spikes/s (Carandini et al. 2002) and a contrast saturation component as conveyed in Fig. 3. For each LGN cell in our model, the contrast saturation nonlinearity is estimated by using the mean normalized contrast response function from all LGN cells (n = 74). This contrast response function is well fit (the goodness of fit Rsquare = 0.99) with a hyperbolic ratio function, R = 1.0 × C/(C + C50), where C and C50 represent stimulus contrast and the contrast level that elicts half of maximum response, respectively. The estimated value of C50 from our population of LGN cells is 27.8%. Consistent with the model in the previous study (Carandini et al. 2002), we assume that the maximum modulation response of LGN cells is 100 spikes/s. However, in our model, thalamo-cortical synapses operate in a linear manner, i.e., synaptic depression is not included. We also assume that there is a spiking mechanism for cortical cells and that the firing rate is a rectified version of the membrane potential with a threshold of 5 spikes/s (Anderson et al. 2000; Carandini and Ferster 2000; Carandini et al. 2002).
In our model, depicted in Fig. 4 A, summation of LGN inputs occurs in a “push-pull” manner. In this configuration, excitation from an on-center LGN cell and inhibition from an off-center LGN cell form an on subregion. Likewise, excitation from an off-center and inhibition from an on-center LGN cell form an off subregion (Carandini and Heeger 1994; Carandini et al. 1997; Glezer et al. 1982; Tolhurst and Dean 1990). Although inhibition is thought to operate through cortical inhibitory interneurons (Palmer and Davis 1981; Troyer et al. 1998), for our present purpose, it is assumed to come directly from LGN cells. This assumption is for simplicity. Intracortical inhibitory connections included in other models (Albrecht and Geisler 1991; Ben-Yishai et al. 1995; Carandini and Heeger 1994; Carandini and Ringach 1997; Heeger 1992; McLaughlin et al. 2000, 2003; Shelley and McLaughlin 2002; Shelley et al. 2002; Wielaard et al. 2001) are not considered in this analysis because they do not seem to be relevant to COS (Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). We assume that test and mask stimuli are sinusoidal drifting gratings that are vertical and horizontal, respectively. The contrast, duration, and temporal frequency of both test and mask stimuli are 50%, 2 s, and 4 Hz, respectively. A plaid is formed by the superposition of these gratings. The model simple cell responds to the test stimulus but not the mask. If LGN cells respond linearly to a visual stimulus, a simultaneous presentation of the mask will not affect the synaptic input to the model simple cell. However, with spike rectification and contrast saturation in the LGN, synaptic input to the model simple cell is assumed to be reduced by the mask.
The first stage of our model consists of a standard feedforward process for the generation of simple cell RFs in visual cortex (Fig. 4A). The RF of the simple cell is modeled to receive inputs from 50 LGN cells. LGN cells are spatially distributed 5 × 5 across the simple cell RF, for which the orientation selectivity to vertical orientations arises from the arrangement of excitatory LGN inputs. Summation of LGN inputs occurs in a push-pull manner, i.e., an excitation is supplemented by a subtractive inhibition through cortical inhibitory interneurons. For simplicity, we assume that inhibition is derived directly from the 5 × 5 array of LGN cells that feed the model simple cell. Eight LGN neurons are represented in Fig. 4, A and B. Four of the eight LGN cells provide “push” excitatory input to the cortical cell. The remaining four LGN cells provide “pull” inhibitory input to the cortical cell through inhibitory interneurons. Excitatory (or inhibitory) input consists of two on-center and two off-center LGN cells with on-center cells in the on (or off) and off-center cells in the off (or on) subfield of the cortical RF. Excitation by an on-center cell is balanced by overlapping inhibition from an off-center cell. Figure 4B shows how contrast saturation in the LGN may lead to COS in visual cortex. The model simple cell gives a maximum response (4) to a vertical grating and no response (0) to a horizontal grating. We consider two possible outcomes when both gratings are presented simultaneously in orthogonal orientations. First, if LGN cells respond linearly to stimulus contrast, the response of the cortical cell will be the same as that to a vertical grating alone (i.e., 4). However, if cells in the LGN exhibit contrast saturation as shown in Fig. 3, the response of the cortical cell will be reduced (<4).
As shown above, contrast saturation is present in LGN neurons (Fig. 3). We applied the contrast response nonlinearity (spike rectification and contrast saturation) for each LGN cell in our model to predict the level of COS in visual cortex. Normalized responses of the model cortical cell are shown in Fig. 4C. The two bars labeled test and mask represent, respectively, the normalized responses of the model cortical neuron to test and mask presentations at 50% contrast levels. The bar labeled predicted represents the predicted responses to the plaid based on a population of LGN neurons exhibiting the mean contrast saturation observed in this study (CSI = 0.33). In this case, the predicted response is 0.60, i.e.,the plaid suppresses the response of the cell by 40%. This represents a strength of suppression that is 7% higher than the mean contrast saturation (0.33) we observe in LGN. This difference may be accounted for by a rectifying nonlinearity of LGN cells and a spike output nonlinearity of cortical cells. The fourth bar in Fig. 4C, labeled measured, represents the mean response to the plaid, which is normalized by the response to the test-only condition averaged across the entire 2-s stimulus duration (n = 36, see Fig. 2B). In this case, we observe a response reduction of 49% relative to the test-only condition. This is close to the predicted strength of COS (40%) from the model cortical cell and consistent with our previous report of COS (Walker et al. 1998). Therefore if we incorporate a rectifying nonlinearity for LGN cells and a spike threshold nonlinearity for cortical cells, contrast saturation in the LGN can account for most COS in cortical cells for high contrast conditions.
Because the CSI in LGN increases with stimulus contrast (Fig. 3C), our model predicts that the level of suppression increases with contrast. To test this prediction, we measured COS for 35 cortical cells at different test and mask contrasts. Figure 5 A shows the normalized responses to different combinations of test and mask contrasts. The degree of suppression clearly increases with mask contrast and at very low values (<10%), the level of suppression is minimal. This is qualitatively consistent with our model prediction. To quantify this result, we compare the measured and predicted COS for mask and test stimuli that have equal contrast levels of 12.5, 25, and 50% (Fig. 5B). The gray bars depict the measured levels of suppression, whereas the black and white bars denote the mean CSI of LGN cells and predicted COS values, respectively. Compared with measured COS levels, our model predicts 98.5, 90.3, and 78.9% suppression for 12.5, 25, and 50% contrasts, respectively. The data of Fig. 5 show that contrast saturation at the LGN level plays a reduced role at low contrast levels. For 12.5, 25, and 50% contrast levels, contrast saturation in the LGN can account for 24, 47, and 84%, respectively, of the predicted COS. This result suggests that contrast saturation in the LGN may be the principal source of COS for high contrast levels, but for low contrasts other nonlinear mechanisms are important.
Contrast saturation in the LGN and temporal frequency
COS can be obtained with high temporal frequency mask stimuli that do not typically activate cortical cells (Allison et al. 2001; Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). To explore this in this study and determine if this finding is consistent with our model, we measured contrast response functions at different temporal frequencies for a population of 15 LGN cells. Figure 6 A shows results for an example LGN cell. As the data show, there is contrast saturation for the tests at 7 and 10 Hz. Saturation also occurs at temporal frequencies of 2, 4, 15, and 25 Hz, but this is not obvious in Fig. 6A because we use a standard logarithmic scale for contrast. We use the data at 50 and 100% contrasts to calculate CSI as a function of temporal frequency. This function is shown in Fig. 6B for the example LGN cell. We quantify the tuning properties of this predicted suppression by fitting a Gaussian function to the temporal frequency tuning curve of CSI. We use this curve to calculate peak (TFpeak) and cut-off (TFcut-off) temporal frequencies. TFcut-off is defined as the temporal frequency at which the CSI value is reduced by one half of that at TFpeak. The example cell exhibits a TFpeak of 8.6 Hz and a TFcut-off value of 14.5 Hz. The distributions of TFpeak and TFcut-off values for 15 LGN cells are presented in Fig. 6, C and D. Although these temporal frequency tuning properties are broadly distributed, they are consistent with those of COS in visual cortex (Allison et al. 2001; Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). Considered together, these results are in accord with the hypothesis that contrast saturation in the LGN at high contrast levels may be the principal mechanism mediating COS in visual cortex.
We examined potential mechanisms of COS in the visual cortex. A primary process proposed in previous work is synaptic depression. Recovery from synaptic depression typically requires several hundred milliseconds (Abbott et al. 1997; Chance et al. 1998; Varela et al. 1997, 1999). Our current data show that the response to an optimal test stimulus is unaffected by a briefly presented orthogonal mask as long as the two stimuli do not overlap in time. This result therefore is inconsistent with a model in which synaptic depression is the principal source of COS. We find that contrast saturation in LGN cells, which has not been considered in previous studies (Carandini et al. 2002; Freeman et al. 2002), leads to strong COS in visual cortex for high contrast stimuli. This effect, in combination with the known rectifying nonlinearities in LGN and spike output nonlinearities of cortical neurons (Carandini 2004; Carandini and Ferster 2000; Priebe et al. 2004), can account for observed levels of COS in visual cortex. We studied the saturation mechanism in LGN. However, this effect is likely to have a retinal origin. Retinal contrast gain control mechanisms are well established and include the types of saturation effects we observe in LGN (Shapley and Victor 1978, 1979).
Recent work has shown that the temporal frequency and adaptation properties of cortical cells are not well matched to the characteristics of COS. COS can be induced by mask stimuli with higher temporal frequencies than those that can activate most cortical cells, and it is not affected by prolonged adaptation to mask stimuli (Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). These temporal frequency and adaptation properties are consistent with those of LGN neurons but not cortical cells. Based on these results and the finding that COS is weak in the LGN (Bauman and Bonds 1991; Freeman et al. 2002), synaptic depression at thalamo-cortical synapses has been proposed as the mechanism underlying COS in the visual cortex (Carandini et al. 2002; Freeman et al. 2002; Mrsic-Flogel and Hubener 2002). Synaptic depression is a form of short-term synaptic plasticity in which the efficacy of a given synapse is temporarily reduced according to its recent activity level (Abbott and Nelson 2000; Chance et al. 1998). In vitro studies have revealed at least two separate components of short-term synaptic depression acting over different time scales. A fast form occurs within 5–10 presynaptic action potentials and requires 200–600 ms for recovery, whereas a second slower form requires many action potentials to reach full strength and approximately 10s to fully recover (Chance et al. 1998; Varela et al. 1997, 1999). If synaptic depression underlies COS in the visual cortex, its temporal properties should be evident in the responses of cortical neurons. For example, if the slow form of synaptic depression were mediating COS, we would expect to observe stronger suppression after prolonged adaptation to a mask stimulus. This is because synapses would become depressed during the adaptation period and remain depressed throughout the test period. In contrast, the response without prior adaptation would be high initially and exhibit suppression only at later time-points, after synaptic depression had set in. Previous studies of COS do not support this prediction. COS is just as strong with prior adaptation as it is without (Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). This suggests that the slow form of synaptic depression with its prolonged onset time is not a viable mechanism for explaining COS.
Another prediction of the synaptic depression hypothesis is that test and mask stimuli need not to be presented simultaneously to observe COS. Because both fast and slow forms of synaptic depression exhibit significant recovery time constants, the suppressive effects of a mask stimulus should remain strong for at least a few hundred milliseconds after its removal. In this study, we evaluated this prediction for neurons in the striate cortex. In contrast to the prediction, we found that both test and mask stimuli must be presented simultaneously to observe COS. This result suggests that neither form of synaptic depression is likely to be a dominant mechanism underlying COS in visual cortex.
In previous studies, COS has been attributed to intracortical inhibition (Allison et al. 2001; Bauman and Bonds 1991; Bonds 1989; DeAngelis et al. 1992; Sengpiel et al. 1998). The primary evidence in support of this view is the finding that COS is blocked by application of the GABA agonist bicuculline over a large region of visual cortex (Morrone et al. 1987). However, this view has been questioned recently by data on the temporal frequency and adaptation properties of COS (Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). COS in the visual cortex can be obtained with a mask drifting too fast to elicit responses from most cortical cells (Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). In addition, COS is unaffected by adaptation to a mask grating, even though the responses of most cortical neurons are substantially suppressed by adaptation. These findings suggest that the primary mechanism underlying COS operates before the formation of cortical RFs (Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005).
There are at least three plausible ways by which this could occur. The first possibility is that COS is present in LGN neurons and simply propagates to visual cortex. Although some cells in the LGN exhibit weak COS, it does not seem strong enough to explain the strength of suppression normally observed in visual cortex (Freeman et al. 2002). Furthermore, because LGN neurons are not selective for stimulus orientation, their responses to overlapping orthogonal stimuli are typically greater than those to either stimulus in isolation (Freeman et al. 2002). A second possibility is that COS may be mediated at thalamo-cortical synapses through synaptic depression (Carandini et al. 2002; Freeman et al. 2002). This is somewhat complicated to verify because synaptic depression seems to be stronger in vitro than in vivo (Boudreau and Ferster 2005; Chung et al. 2002; Sanchez-Vives et al. 2000). However, based on the data and analysis reported here, this possibility also seems unlikely. A third possibility is that COS in visual cortex may originate from other subcortical nonlinear properties, such as rectifying nonlinearities and contrast saturation in LGN neurons as we propose here and as considered briefly in a previous study (Freeman et al. 2002).
The feedforward model of COS postulated here is capable of explaining several of the properties of COS reported in previous studies (Bonds 1989; DeAngelis et al. 1992; Freeman et al. 2002; Li et al. 2005; Morrone et al. 1982). First, it accounts for the broad tuning of COS to mask orientation because LGN neurons do not exhibit orientation tuning. Second, it explains the fact that excitatory and suppressive RFs are largely co-localized (DeAngelis et al. 1992). It is clear that contrast saturation occurs only when the mask stimulus is presented simultaneously on an overlapped region of the excitatory test stimulus. Contrast saturation would not occur if the mask and test stimuli were presented to different regions of the RF. Third, it accounts for the immunity of COS to prolonged mask adaptation (Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). LGN cells in the cat exhibit a small adaptation effect compared with cortical cells (Ohzawa et al. 1982, 1985; Sanchez-Vives et al. 2000; Shou et al. 1996). A prolonged mask grating will slightly shift the contrast response function of LGN cells to the right. This will reduce COS in visual cortex with a decrease of contrast saturation in the LGN (see Fig. 4B). On the other hand, the prolonged mask grating can decrease the firing rate of cortical cells (Carandini et al. 1998). These adaptation effects, which may reduce or increase COS, may offset each other. Finally, our model accounts for the temporal frequency properties of COS. It has been shown that COS can be obtained with mask stimuli drifting too rapidly to elicit substantial responses from cortical neurons (Allison et al. 2001; Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). Most LGN neurons exhibit strong contrast saturation to gratings at high temporal frequencies (Fig. 6). This result is in agreement with the temporal frequency properties of COS in visual cortex (Allison et al. 2001; Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005).
Results from this and previous studies of temporal frequency and adaptation properties of LGN and cortical cells point to subcortical mechanisms as the primary basis of COS (Freeman et al. 2002; Li et al. 2005; Sengpiel and Vorobyov 2005). With spike output nonlinearities attributable to cortical cells, these findings suggest that rectifying nonlinearities and contrast saturation in the LGN can account for most COS in visual cortex. Of course, we cannot rule out the possible participation of other mechanisms such as synaptic depression and intracortical inhibition.
A recent paper casts doubt on a purely excitatory feedforward mechanism for COS (Smith et al. 2006). Measurements were made of suppression, recovery, onset, and offset time-courses for cortical neurons. COS and its release occur quickly. However, it is delayed after the offset time response. This suggests a role for intracortical inhibition. However, to show this, inhibitory interneurons must be identified with the same temporal and adaptation properties as those for COS in visual cortex.
Another relevant issue concerns response phase advances with stimulus contrast increases. These have been reported in retina (Shapley and Victor 1978), LGN (Saul and Humphrey 1990), and visual cortex (Dean and Tolhurst 1986). When a mask is simultaneously presented with a test stimulus, the combination produces increased contrast. As a result, we should observe a phase advance of the response to the plaid in our experiments. However, phase advances are not evident in our data (Fig. 2, D and E). Two possible reasons may account for this. First, the resolution (Fig. 2, D and E) is not high enough to show phase advances in the response to the plaid. More spikes would be required from a number of repetitions to obtain higher resolution. Second, although synaptic depression is relevantly weak in vivo (Boudreau and Ferster 2005; Sanchez-Vives et al. 1998), phase delay from synaptic depression at thalamo-cortical synapses may cancel out the phase advance from the increased contrast in the plaid.
Species differences may also be relevant. In the primate, magnocellular but not parvocellular cells are reported to show contrast saturation (Benardete et al. 1992; Shapley et al. 1981). In addition, parvocellular cells seem to constitute the major input to visual cortex (Malpeli et al. 1996). COS has been shown in primate visual cortex (Carandini et al. 1997). Therefore it may be that contrast saturation from LGN cells is less of a factor in COS in the primate visual pathway.
A recently published study, in which intracellular techniques were used, reaches similar conclusions to ours regarding the origins of COS in the visual cortex (Priebe and Ferster 2006). In this study, excitatory and inhibitory conductances are both reduced about equally by the overlapped mask. This suggests that cortico-cortical inhibition is not involved in COS. To explain COS, a toy model is also used in which there is feedforward excitation from thalamic relay cells to cortical simple cells. Within this model, contrast saturation and rectification of LGN cells are used to account for COS in simple cells for low and medium contrast levels (8 and 32%). However, this model produces elevated DC responses to the plaid stimulus (Table 1 of Priebe and Ferster 2006). Most likely, this is caused by the use of a “push” only model. In this study, we used a “push-pull” architecture in which feedforward excitation is balanced with antiphase inhibition separated in spatial phase by 180°. The inhibition is thought to be mediated by cortical interneurons (Carandini et al. 2002; Ferster 1988; Glezer et al. 1982; Palmer and Davis 1981; Tolhurst and Dean 1987; Troyer et al. 1998). Our model incorporates LGN rectification, contrast saturation, and cortical spike threshold in a “push-pull” format where antiphase inhibition balances feedforward excitation. For low contrasts, this arrangement is appropriate. However, it is relatively less effective at high contrasts where other factors including cortico-cortical effects may operate. Overall, however, a “push-pull” approach seems preferable especially because it seems to be required to account for fundamental response characteristics of cortical neurons such as orientation tuning invariance (Sclar and Freeman 1982; Troyer et al. 1998).
In summary, we showed that synaptic depression at thalamo-cortical synapses is unlikely to be the primary mechanism in COS because it requires considerable recovery time. The combination of spike rectification and contrast saturation in the LGN and a spike output nonlinearity of cortical neurons into a standard “push-pull” feed-forward model can explain COS in visual cortex.
This work was supported by National Eye Institute Grants EY-01175 and EY-03716.
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