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Visual Sciences, Research School of Biological Sciences, Australian National University, Canberra, Australia
Submitted 20 September 2004; accepted in final form 12 February 2005
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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). The wallaby pretectum is a retino-recipient region of which the NOT forms a large component (Ibbotson et al. 2002). The wallaby pretectum also contains other nuclei such as the olivary pretectal nucleus, as is the case in eutherian mammals (e.g., Hutchins and Weber 1985; for review, Ibbotson and Dreher 2005). The wallaby NOT contains direction-selective neurons that are optimally responsive to wide-field visual motion, as naturally generated by self-movement. Stimulation and lesion studies have confirmed that the NOT has an essential role in driving horizontal optokinetic nystagnus (OKN) in eutherian mammals (e.g., Collewijn 1975b; Schiff et al. 1988), while evidence from eye rotation and electrophysiological experiments point toward a similar role in the wallaby (Hoffmann et al. 1995). Recordings have been made from the NOT in marsupials (opossum: Volchan et al. 1989; wallaby: Hoffmann et al. 1995; Ibbotson et al. 1994) and in several eutherian species (e.g., cat: Hoffmann and Schoppmann 1981; ferret: Klauer et al. 1990; monkey: Hoffmann et al. 1988; Ilg and Hoffmann 1996; Mustari and Fuchs 1990; rabbit: Collewijn 1975a). The NOT has a highly conserved physiology despite significant differences in input structure between species.
In marsupials, direct retinal inputs arise almost exclusively from the contralateral eye (opossum: Vargas et al. 1998; wallaby: Ibbotson et al. 2002), and there is little if any input from the visual cortex (Ibbotson et al. 2002; Pereira et al. 2000). In monkeys, retinal input comes from both eyes (Telkes et al. 2000), and there is a very large input from the visual cortex, which is 
10-fold larger than the retinal input (Distler et al. 2002; Ilg and Hoffmann 1993). The visual cortex also provides a major input to the NOT in cats (Schoppmann 1981). This paper shows that contrast adaptation occurs in the wallaby NOT, but the connectivity suggests that the effects most likely arise from retinal circuitry. This is of interest because much speculation has previously suggested that contrast adaptation is a solely cortical phenomenon (Maffei et al. 1973; Movshon and Lennie 1979; Ohzawa et al. 1985). However, recent comparative studies in both primates and salamanders have shown that contrast adaptation may, at least partially, arise in the retina (e.g., Baccus and Meister 2002; Chander and Chichilnisky 2001). Evidence also suggests that adaptive effects can be observed in other subcortical brain structures, such as the macaque dorsal lateral geniculate nucleus (Solomon et al. 2004). This work provides further comparative data suggesting that contrast adaptation is not limited only to the cortex.
Studies have revealed that contrast adaptation may have a beneficial effect on visual processing by shifting the contrast response functions of cells such that they are maximally sensitive to contrast changes close to the prevailing contrast in the environment (e.g., Ohzawa et al. 1985). This mechanism is thought to provide an important function for the visual system because it allows the system to operate optimally in a wide range of visual environments (Ibbotson 2005). It is likely that contrast adaptation at the inputs to motion detectors allows them to function more effectively, and this could explain adaptive effects observed in the highly motion-selective NOT. It has also been shown that adapting to specific temporal frequencies of motion can adjust the tuning properties of cat cortical cells (Saul and Cynader 1989a,b) and some motion-sensitive insect neurons (Maddess and Laughlin 1985). In the insect, this mechanism was argued to have beneficial effects on the coding of image speed (also see, Clifford et al. 1997; for a review, Ibbotson 2005).
This work is the first to study adaptation to both contrast and temporal frequency (TF) in a noncortical visual pathway, namely the retino-pretectal system. Adaptation in this pathway shows the generality of adaptation in different visual brain areas and suggests that adapting to the prevailing visual environment is important for coding motion signals relevant for eye movement control. Evidence from humans has shown that adaptation to moving gratings can influence the magnitude of subsequent horizontal ocular following responses, which are partially driven by cells in the NOT (Ibbotson and Maddess 1994; Maddess and Ibbotson 1992). The wallaby results presented here may therefore have general implications for processing in mammalian visual systems beyond this specific species.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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). Briefly, animals were anesthetized with an intramuscular injection of 30 mg/kg ketamine (Parnell) and 1.5 mg/kg xylazine (Rompun, Bayer). Anesthesia was continued with periodic intravenous injections of thiopentone (Pentothal, Boehringer-Ingelheim) into the tail vein. The wallabies were intubated with a 4-mm endotracheal cannula and placed in a stereotaxic apparatus. Paralysis was initiated by intravenous injection of suxamethonium (50 mg/ml; Scoline, Glaxo), and the endotracheal cannula was connected to a respirator. Stroke volume was adjusted to give 3.7% CO2 in the expired air. After paralysis, animals were ventilated with a mixture of 75% N2O-25% O2 and infused intravenously with 5.6 ml/h of Hartmann’s lactate solution carrying 1 mg/kg/h of pentobarbitone (Boehringer-Ingleheim) and 4.2 mg/kg/h of suxamethonium. Body temperature was maintained at 37°C with an electric blanket. All procedures were approved by the animal ethics committee of the Australian National University and followed the guidelines of the Australian National Health and Medical Research Council.
Recordings were made from single units in the NOT using tungsten-in-glass microelectrodes. Extracellular responses were amplified, passed through a Schmidt trigger, and collected on a computer as spike arrival times. The locations of the recording sites were marked with electrolytic lesions at the bottom of each track using DC current (2 µA for 5 s). Animals were killed with an overdose of pentobarbital sodium and perfused via the heart with 10% formol saline solution and then 4% paraformaldehyde in saline. The locations of the electrolytic lesions and electrode tracks were evident in all animals, confirming that the recording sites were in the NOT (for examples of NOT anatomy in wallabies, see Ibbotson et al. 1994, 2002).
Visual stimuli
The stimuli were achromatic, luminance modulated, drifting sine-wave gratings presented on a display monitor (CCID7551, Barco Industries) and were generated by a computer-controlled video display driver (AT Vista, True Vision). The refresh rate of the monitor was 97.75 Hz, and each frame contained 480 lines (640 pixels/line). The screen luminance was 45 cd/m2. Gratings could be positioned at any orientation and moved back and forth along the spatial frequency vector. The gratings could be moved at TFs between 0.38 and 24.32 Hz. The screen subtended 90 x 67°. All experiments began by identifying the optimal motion direction, approximate center of the receptive field, and the optimum spatial frequency and TFs. Gratings were presented to the contralateral eye in a circular aperture, surrounded by a gray field with mean grating luminance. Stimulus aperture sizes were selected such that moving patterns generated robust responses and clear temporal tuning characteristics without any obvious signs of saturation close to the optimum TF. In most cells, the aperture sizes were large (>30° diam). However, in a small number of cells, it was necessary to use small apertures of only 10° diam to prevent response saturation in the control data, i.e., responses that flattened out for a wide range of TFs or contrasts. The aperture size was the only parameter used to reduce saturation; all other stimulus parameters were optimal for each cell.
Adaptation protocol
Response functions were measured by moving the grating for 1 s in the preferred direction. The test sequence consisted of 1 s of motion (control), a 5-s rest period (blank screen), and an adapting motion for 10 s, followed by a short interval of 100 ms where the grating was stationary. The 1-s test stimulus was then presented, followed by a 20-s rest interval where the image was a gray field of mean luminance. In some cells, the controls were run in separate trials before and after the adaptation protocol to make sure that changes did not occur in the controls as a result of the adaptation protocol. For these trials, 1-s test periods were interspersed between 5-s rest periods. Adapting gratings moved at one of several constant drift rates. The spatial frequency in most experiments was 0.25 cpd, but for some cells sensitive to very low image speeds, it was necessary to use 
1.0 cpd to produce optimal response magnitudes. Spatial frequencies were never changed between adapting and test phases. Contrast response functions were measured using the same procedure except that the grating contrast was varied in the test phase rather than the drift rate of the gratings. All contrasts are expressed as Michelson contrasts (Luminancemax – Lmin/lmax + Lmin), and the tests ranged from 0 to 0.9. The drift rates in the adapting and test phases were the same and were chosen as the optimum based on prior tests. The adapting contrast used in the experiments was usually 0.24, but other values were also used. A least eight trials were run for each adaptation condition. Spontaneous activities were calculated by averaging the firing rates measured in blank-screen periods at intervals during the experiments, always following the 20-s rest periods.
Fitting algorithms
Contrast response functions in both the control and postadaptation conditions were fit with the following sigmoidal function using a least squares fitting algorithm
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, the value of n was forced to assume a single value for the adapted and unadapted conditions, which was optimized jointly for both conditions.
The TF response functions were fit using a least squares fitting algorithm with a skewed Gaussian (Priebe et al. 2003)
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For all fits, the Rmax value was constrained so that it could not fall outside ±15% of the maximum measured response. It was found that this constraint prevented the Rmax value from occurring at contrasts significantly higher than unity or the peak TF (TFopt) value occurring at unrealistically high values. The C50 values never exceeded unity contrast. R2 values were calculated for all fits, with the R2 values ranging from 0.81 to 0.99 (mean R2 = 0.89, SD = 0.10).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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). Preferred direction motion increases the firing rate, whereas anti-preferred motion inhibits the background firing rate. In this paper, only responses to preferred direction motion are considered, but adapting motion directions were varied. Wallaby NOT neurons can be divided into two categories in the unadapted state: fast cells and slow cells (Ibbotson and Price 2001). Fast cells are optimally stimulated at relatively high TFs but low spatial frequencies and slow cells at low TFs and high spatial frequencies. The dividing line between the two populations occurs at a speed of 4°/s (TF/spatial frequency). The adaptation results are shown separately for fast and slow cells, but there were no obvious differences between the adaptive properties of the two cell types. Influence of adaptation on contrast responses
Figure 1 shows peristimulus time histograms (PSTHs) obtained from an NOT neuron in a range of control and adapting conditions. In all plots, the mean spontaneous activity is shown as a straight horizontal line through the graph. Increases in spiking rate are shown as bars above the spontaneous rate, whereas decreases below spontaneous are represented by bars below that value. The left column shows responses to image motion in the cell’s preferred direction at five contrasts with no prior adaptation (motion starts at 1 s and ends at 2 s). The right column shows the responses to the same five contrasts, but the test stimulus is preceded by 10 s of image motion in the preferred direction with a grating of C = 0.24 (adaptation phase). Only the tail end of the adapting phase of the stimulus is shown in Fig. 1. It is evident that in the control condition the response increases with increasing stimulus contrast. This is also the case in the adapted condition, but responses are greatly attenuated for contrasts <0.3.
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Scatter plots present the C50 values (Fig. 3A) and Rmax values (Fig. 3B) before and after adaptation for 26 fast cells (circles) and 16 slow cells (triangles). Each plot shows a diagonal line that represents the equality point where control and postadaptation values are the same. Cells fell along a continuum between those where the shift in C50 was large but the reduction in Rmax was small and those where the reduction in Rmax was large but the shift in C50 was small. All cells showed either a change in C50, Rmax, or both. There were no obvious differences between the fast and slow cell populations. It is important to note that the Rmax value is only the response component and does not include the spontaneous activity (Fig. 3). Most Rmax values were between 20 and 60 spikes/s. Lower values were recorded in some cells where the diameter of the stimulus aperture throughout testing was 10°. Small stimuli were used in these cells because they showed very large increases in response with small increases in stimulus size, such that their responses saturated very easily. Efforts were made to reduce saturation in the controls so that any changes that were observed could be attributed to adaptation rather than to other nonlinearities.
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Influence of adaptation on TF responses
The TF response functions of NOT cells were measured for gratings moving at a range of TFs in the preferred direction using the same adapting and test times as those used in the contrast experiments. Spatial frequencies were always held constant during adapting and test phases. Optimum spatial frequencies and TFs were determined in initial tests, and the adapting drift rate was usually chosen to be at or below the optimum TF (for exceptions, see Fig. 8).
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To quantify these changes for the cell population, the postadaptation values for Rmax, TFopt, and TF50 are plotted as functions of the respective control values in the form of scatter plots (Figs. 6 and 7). As with the contrast data, the value of Rmax is the response component only and does not include the spontaneous activity of the cells. The values for fast and slow cells are shown in Figs. 6 and 7, respectively. For both cell types, values of Rmax ranged from 15 to 50 spikes/s (Figs. 6, A and D, and 7, A and D). Values of Rmax are the same or lower in the postadaptation condition (i.e., above the line in Figs. 6A and 7A). Values of TFopt were either unchanged or showed rightward shifts to higher TFs (i.e., below the line in Figs. 6B and 7B). Changes in TF50 showed quite large variations across the cell population (Figs. 6C and 7C). Many cells showed very little change in TF50, whereas others showed quite large rightward shifts along the TF axis. There were no obvious differences between fast and slow cells.
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All data up to this point have focused on adaptation at TFs at or below the peak TF tuning (e.g., Fig. 5, B and C, vertical dashed lines). More thorough examinations were conducted on 12 neurons in which the influence of motion adaptation at each cell’s peak TF and at one-half and twice the peak value were measured (e.g., 1.6, 3.2, and 6.4 Hz). To present this data succinctly, ratios were calculated as follows: Rmax(after adaptation)/Rmax(before adaptation) and TF50(before adaptation)/TF50(after adaptation). These ratios were selected because in both cases no adaptation gives values of unity, whereas normal adaptation (i.e., Rmax decreased, TF50 increased) will give values below unity. Values above unity will show a reversal of the usual adaptation effects (i.e., Rmax increased, TF50 decreased). The Rmax and TF50 ratios for the 12 cells are shown in Fig. 8. The greatest change in Rmax always occurred after adaptation at the peak TF (Fig. 8A). Similarly, the largest rightward shift in TF50 always occurred for adaptation at the peak TF (Fig. 8B). There was no evidence that adaptation at TFs above the optimum TF caused a leftward shift in the tuning functions. In other words, adaptation did not cause repulsion along the TF axis.
Comparing contrast and TF results
Figure 9A shows a scatter plot of C50 before and after adaptation for all the fast and slow cells combined. These data are presented alongside a scatter plot showing the TF50 before and after motion adaptation in the same cells (Fig. 9B). In both plots, the open symbols represent cells that showed a significant change in C50 (t-test, P < 0.01). The comparison reveals that cells that show rightward shifts in their contrast response functions also tend to show rightward shifts in their TF response functions, suggesting a link between the two effects.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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This paper shows for the first time in any species that directional neurons of the pretectal NOT demonstrate contrast adaptation effects similar to those observed in the primary visual cortex. Adaptation to motion in the preferred direction causes some cells to show a reduction in firing rate to all tested contrasts, suggesting a fatigue-like effect. However, many cells show a clear rightward shift in the tuning function along the contrast axis. The rightward shift occurs because responses to low contrasts are attenuated, whereas those to high contrasts remain largely unaffected. This effect is commonly discussed as a type of contrast gain control (e.g., Ohzawa et al. 1982, 1985). In this view, adaptation adjusts the sensitivity of cells to the range of contrasts that they have experienced in the recent past (for review, Ibbotson 2005). Consequently, the dynamic range of the cells is optimized to detect changes in contrast close to the prevailing level, rather than being spread thinly across a wide range of contrasts. Such a mechanism might improve the performance of the visual system by providing robust input signals that are matched to the prevailing contrasts in the visual environment.
The contrast adaptation observed in the NOT is likely to occur in the motion detector input circuitry presynaptic to the recorded cells (Ibbotson and Clifford 2001a,b). This suggestion arises from two observations. First, Ibbotson et al. (1998) showed that a test stimulus must be presented in the same region of an NOT cell’s receptive field as the prior adaptation for adaptive effects to be observed. Such a finding argues against the effect being related to changes in the membrane potential of the recorded cell itself, because, if it were, adaptation in any region of the receptive field should influence the responses of all other regions of the receptive field (Kohn and Movshon 2003). Second, neurons in the wallaby NOT can be divided into two categories based on their spatiotemporal tuning properties (Ibbotson and Price 2001; Ibbotson et al. 1994). Slow cells prefer relatively low TFs but high spatial frequencies, whereas fast cells prefer high TFs and low spatial frequencies. The spontaneous activities of these neurons are altered after a period of motion in very characteristic ways. After preferred direction motion, the spontaneous activities of fast cells are transiently inhibited, whereas the spontaneous rate is transiently elevated after anti-preferred motion: opposite-sign after-effects (Price and Ibbotson 2002). Some slow cells can show small opposite-sign after-effects, but most have the reverse pattern. That is, after preferred direction motion, the spontaneous rate is transiently elevated and vice versa: same-sign after-effects (Price and Ibbotson 2002). Recordings were obtained from 16 slow cells and 26 fast cells in this study. Cells that showed changes in their contrast and TF tuning functions after adaptation were found among both cell types, suggesting that the effects are not restricted only to cells where adaptation generates a hyperpolarization of the membrane potential (i.e., fast cells). Contrast adaptation has been shown to be at least partially generated by a hyperpolarization of the recorded cell’s membrane potential in the cat primary visual cortex (Carandini et al. 1998). Slow cells in wallaby NOT show adaptation despite motion stimulation, causing same-sign after-effects, which implies an afterdepolarization of the membrane potential. These observations support the notion that the adaptation occurs before the NOT.
Where could the adaptation occur? The NOT in the marsupial wallaby receives a large direct input from the contralateral eye and a very small input from the ipsilateral eye (Ibbotson et al. 2002). Areas 17 and 18 of the visual cortex provide no obvious input to the NOT (Ibbotson et al. 2002), unlike the case in the eutherian cat (Schoppmann 1981) and monkey (Distler et al. 2002; Ilg and Hoffmann 1993). Unpublished observations on a small number of cells (n = 3) examined contrast adaptation in the wallaby cortex as part of studies of other cortical properties (Ibbotson and Mark 2003). In these three cortical cells, clear contrast adaptation was observed, which resembled the rightward shifts in contrast tuning seen in the NOT (Fig. 2, A and B). While the sample size is not sufficient to make major comment, the fact that NOT and cortical neurons show contrast adaptation, but the two areas do not appear to be connected (Ibbotson et al. 2002), suggests that contrast adaptation may be generated independently in two brain regions in the same species or that both derive the effect from the retina.
It was generally accepted that contrast adaptation is a cortical phenomenon (Maffei et al. 1973; Movshon and Lennie 1979; Ohzawa et al. 1985). However, recent experiments have shown that contrast adaptation does influence the responses of cell’s in salamander and monkey retinas (Baccus and Meister 2002; Chander and Chichilnisky 2001) and the magnocellular layers of the lateral geniculate nucleus in macaques (Solomon et al. 2004). Increases in stimulus contrast progressively and reversibly attenuate light responses in both salamander and monkey retinal ganglion cells, suggesting that a portion of the contrast gain alterations observed in the cortex arise from retinal mechanisms. It is therefore quite feasible that wallaby NOT shows contrast adaptation as a result of mechanisms in the retina. This information would then feed into the NOT and perhaps be further processed such that the effect becomes direction-selective (Ibbotson et al. 1998). We do not know if the retinal inputs to wallaby NOT are direction-selective, but this is the case in rabbits (Oyster et al. 1972) and cats (Hoffmann and Stone 1985).
Motion-specific adaptation
Many NOT cells show changes not only to their contrast tuning but also to their TF tuning following motion adaptation. The physiological results confirm a close link in the retino-pretectal pathway between contrast and motion adaptation. Psychophysical studies on humans, which presumably reflect processing in the retino-geniculate-cortical pathway, have shown that speed and contrast are not independently coded (e.g., Muller and Greenlee 1998). For example, adaptation to moving patterns decreases perceived contrast (Blakemore et al. 1973; Hammett et al. 1994), whereas stimulus contrast influences perceived speed over a wide range of contrasts (Stone and Thompson 1992; Thompson 1982; Thompson et al. 1996). It might therefore be the case that the adaptation mechanisms generating changes to contrast coding also influence speed coding and that these mechanisms are common to both the retino-pretectal and retino-geniculate-cortical pathways.
The change in the TF response functions of NOT cells following motion adaptation could represent a form of TF-related gain control. Certainly the fact that the maximum firing rate at optimum TFs changes very little but the optimum TF and TF50 shift to the right in many cells following adaptation suggests that these cells are not being fatigued by adaptation. The rightward shift in TF tuning functions tends to release the cells from saturation at values at or below the unadapted optimum TF and in so doing increases the sensitivity of the cells to changes in TF close to the adapting value. This combined with a very small change in Rmax in those cells that show rightward shifts in TF50 is suggestive of an active speed-related gain control mechanism. Saul and Cynader (1989a,b) examined the influence of motion adaptation on responses of neurons in area 17 of the cat primary visual cortex. They recorded responses to motion before and after adaptation to moving gratings. They found that, in the spatial domain, adapting at a given spatial frequency resulted in a broad reduction in responsiveness at spatial frequencies above and below the adapting frequency, often with a differential loss of sensitivity at low spatial frequencies (Saul and Cynader 1989a).
In the temporal domain, adaptation generally shifted the preferred TFs of the cat cells. The most common result was that adaptation at a particular TF led to the maximal response attenuation at that same frequency, thus altering the shape of the overall tuning function (Saul and Cynader 1989b). The changes in TF tuning in cat area 17 were direction dependent: after preferred direction adaptation, response attenuation was greater at frequencies equal to or above the adapting frequency; after anti-preferred adaptation, attenuation was greatest at frequencies equal to or below the adapting level. These effects have similarities to the wallaby data but also differences. In the cat, adaptation was evident after anti-preferred motion, whereas there was little evidence of adaptive effects in the wallaby cells. The difference may be species-related or could reveal differences between processing in different visual pathways. Area 17 is not specialized only for motion-processing, whereas the NOT in both species is a motion-specific region (Hoffmann and Schoppmann 1981; Ibbotson et al. 1994). Perhaps different adaptation mechanisms have developed to allow optimum processing for the selective roles of these brain regions.
Functions
Wallabies are known to be highly visual animals and to be day-and-night active (for discussion, see Ibbotson and Mark 2003), so it would be expected that their visual systems need to operate over wide ranges of contrast and luminance. A major function of the NOT is to detect horizontal retinal slip, which would usually be caused by the head rotating about its vertical axis, and to send this information to the motor centers in the brain stem (Collewijn 1975a,b; Hoffmann 1989; Yakushin et al. 2000). These directional signals can be combined with vestibular signals to drive appropriate image-stabilizing ocular following during head and body rotation. Ocular following responses of this type have been shown to occur in wallabies during wide-field visual stimulation with a panoramic stimulus (Hoffmann et al. 1995). Maddess and Ibbotson (1992) showed, in humans, that prior exposure to moving patterns while the eyes fixated a stationary target attenuated the speed of the early components of subsequent ocular following (also see Ibbotson and Maddess 1994). These results suggest at first glance that motion adaptation has a negative effect on the control system that drives ocular following. However, further experiments suggested that, under certain conditions where sudden speed changes were imposed on the adaptive stimulus, ocular following could be enhanced by motion adaptation (Maddess and Ibbotson 1992). No behavioral evidence is available from the wallaby to show the effects of motion adaptation on subsequent ocular following responses. However, the present neural data suggest that horizontal ocular following would be influenced in the wallaby following prolonged exposure to moving patterns.
Ibbotson et al. (1998) suggested that visual motion adaptation might have a beneficial effect in the eye movement control system because the temporal resolution of neurons was improved following motion adaptation, i.e., the sensitivity to speed changes was increased (also see Clifford et al. 1997; Ibbotson and Mark 1996). This conclusion is supported by these data, which reveal that some cells are released from saturation at TFs close to the adapting TF, without loss of spiking capacity at the optimum TF. Other cells show a distinct shift in their optimum TF, again with little reduction in Rmax. These effects could be said to resemble a form of speed-related gain control that is similar to contrast gain control. Therefore cells are better able to resolve changes in image speed by using their entire spiking capacity to code a more restricted range of speeds (Maddess and Laughlin 1985; Maddess et al. 1991).
Experiments on the visual system of a fly have shown effects that resemble speed-related gain control. Maddess and Laughlin (1985) presented moving patterns to a uniquely identifiable direction-selective neuron in the fly optic lobe (H1). They found that the speed response function in the unadapted state increased almost linearly when response was plotted as a function of log-speed (response range: 100–300 spikes/s for speeds of 3–80°/s). When the speed response function was measured following adaptation at a speed of 58°/s, the function shifted rightward such that a linear relationship was obtained on the semilogarithmic plot (response range, 80–220 spikes/s for speeds of 20–100°/s). The results show that the cell generated a smaller Rmax value (300–220 spikes/s) and shifted its speed sensitivity rightward to match the prevailing adaptation speed in a manner similar to some cells in wallaby NOT.
Mechanisms
The observation that cells showing contrast gain control also show lateral shifts in their TF tuning suggest a potential link between the effects. It is therefore reasonable to suggest that alterations in the TF tuning are simply related to the alterations in contrast sensitivity. The differential attenuation to low contrast stimuli following adaptation may be the result of an increase in the threshold required to initiate spikes (Carandini and Ferster 1997). In this scheme, for unadapted conditions, high contrasts produce such strong stimulation that the spiking output saturates, but low contrasts produce graded responses that are closely correlated with the intracellular activity (Carandini et al. 1998). Following adaptation, a membrane hyperpolarization increases the threshold required to initiate spikes. Spiking responses to high contrasts appear largely unchanged because the cells are still close to saturation, if not still fully saturated. However, intracellular activity generated by low contrast stimuli is no longer sufficient to generate spikes, leading to a rightward shift of the contrast response function, as observed from the spiking output. The same change in spiking threshold would presumably influence responses to other stimulus parameters. Therefore low TFs, which normally produce low spiking rates, might also be expected to show attenuated spiking activity, whereas responses to optimum TFs would be unaffected.
One possible mechanism by which an NOT cell’s membrane potential could be adjusted during adaptation is if local interneurons, which exist as part of an adapting neural network (e.g., Carandini and Ferster 1997), provide an inhibitory input. However, evidence from rats, cats, and monkeys shows that GABAergic inputs to the large direction-selective neurons in the NOT are rare or absent (Horn and Hoffmann 1987). This suggests that it is unlikely that local interneurons have a role in altering membrane thresholds during adaptation. However, in marsupial opossums, there is evidence of inhibitory interactions between the NOTs in each hemisphere, suggesting that GABA may be present in that marsupial species (Pereira et al. 1995). Even so, connections between the nuclei are unlikely to be a source of adaptive processes and are thought to relate primarily to the provision of ipsilateral input to each NOT (Ibbotson et al. 2002).
While a simple explanation for adaptation involving membrane hyperpolarization is tempting, it cannot easily explain the rightward shifts in TFopt, which were observed in some NOT cells. Clifford and Langley (1996) suggested that adaptation of the temporal delay filters, which are an inherent component of motion detectors, might explain some of the motion-related adaptive effects observed in insect nervous systems (Maddess and Laughlin 1985). Similar claims were later made for neurons in mammalian motion detectors (Clifford et al. 1997; for a review, Clifford and Ibbotson 2003). However, adaptive temporal filters should lead to very large rightward shifts along the TF axis, whereas these data show only modest rightward shifts. The theory suggests that halving the delay filter time constant should double TFopt (Clifford et al. 1997). Given that motion detection requires multiple processing steps, it is likely that adaptation might influence several stages in the motion processing pathway. It is therefore quite possible that modest adaptation occurs at the temporal delay filter stage, thus shifting TFopt to slightly higher values. This specifically motion-related adaptation would presumably combine with contrast-related effects occurring at earlier stages in the NOT input circuitry.
Summary
This paper has shown that neurons in the pretectal NOT of the marsupial wallaby adapt after exposure to moving gratings. The adaptation in many cells attenuates future responses to low contrasts while having less effect on the maximum response at high contrasts. These results suggest a form of contrast gain control, as observed previously in cortical neurons in cat and monkey. Cells also show changes to their TF tuning functions after motion adaptation, the maximum adaptation occurring when cells are stimulated at their peak unadapted TF. All adaptive effects were direction-selective, the preferred direction of motion producing the strongest effects. Because the primary visual cortex of the wallaby has few, if any, direct connections with the NOT, it is likely that the adaptation observed here occurs independently of the cortex or that adaptation in the NOT and cortex arises in the retina.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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FOOTNOTES |
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Address reprint requests and other correspondence to: M. R. Ibbotson (E-mail: Ibbotson{at}rsbs.anu.edu.au)
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REFERENCES |
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