| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
1Departments of Neuroscience and 2Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; and 3Neuroscience Program and 4Departments of Ophthalmology and Visual Sciences and 5Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, Michigan
Submitted 11 October 2006; accepted in final form 22 April 2007
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
20–30% and, in OFF cells, a tonic 1-mV hyperpolarization. In voltage clamp, the grating increased an inhibitory conductance in all cells and simultaneously decreased an excitatory conductance in OFF cells. To determine whether center response inhibition was presynaptic or postsynaptic (shunting), we measured center response gain under voltage-clamp and current-clamp conditions. Under both conditions, the peripheral grating reduced center response gain similarly. This result suggests that reduced gain in the ganglion cell subthreshold center response reflects inhibition of presynaptic bipolar terminals. Thus amacrine cells suppressed ganglion cell center response gain primarily by inhibiting bipolar cell glutamate release. |
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
; Kuffler 1953). For an ON-center cell, the effective stimulus is a brightening over the center, whereas for an OFF-center cell, the effective stimulus is a dimming over the center. Under cone-driven conditions, the receptive field center primarily corresponds to a feed-forward pathway from cones to bipolar cells to the ganglion cell (Fig. 1) (Masland 2001; Sterling and Demb 2004; Wässle 2004). The ganglion cell center shows an approximately Gaussian spatial profile, which arises from the dome-like distribution of bipolar synapses onto the ganglion cell dendritic tree (Kier et al. 1995).
|
) (Fig. 1). In the classical description, the surround represents a broad region extending across and beyond the center (Enroth-Cugell and Robson 1966; Rodieck 1965). This classical surround exhibits poor spatial resolution and therefore senses low spatial frequencies, including broad-field stimuli and low spatial-frequency gratings. In addition to the classical surround, the ganglion cell center can be suppressed by high spatial frequency contrast patterns in the peripheral receptive field. This peripheral suppression can be distinguished from the classical surround based on spatial resolution. For example, the spiking response to a central spot can be suppressed by peripheral contrast, such as high spatial frequency drifting gratings (1 cycle/° or 4–5 cycles/mm on the retina), that would not stimulate the classical surround (Caldwell and Daw 1978a,b; Enroth-Cugell and Jakiela 1980; Lankheet et al. 1992; Shapley and Victor 1979; Solomon et al. 2006). Peripheral suppression is not equally strong in all cell types but is particularly prominent in the brisk-transient cell types, which include the parasol/magnocellular pathway cells in primates and 
/Y-type cells in other mammals (Caldwell and Daw 1978a,b; Enroth-Cugell and Jakiela 1980; Shapley and Victor 1979; Solomon et al. 2006).
Surround mechanisms are created at both synaptic layers in the retina (Fig. 1). At the outer plexiform layer, horizontal cells inhibit cones and bipolar cells (Fig. 1) (Duebel et al. 2006; Lankheet et al. 1992; Mangel 1991; McMahon et al. 2004). Horizontal cells are electrically coupled to one another in a syncytium (Baldridge et al. 1998). Thus the horizontal cell network has requisite properties to convey the classical surround mechanism, sensitive to low spatial frequency stimuli. At the inner plexiform layer, amacrine cells inhibit bipolar cell synaptic terminals and ganglion cell dendrites (Fig. 1). Amacrine cells are driven by bipolar cells, which themselves have relatively narrow receptive fields that would yield sensitivity to high spatial frequencies (Dacey et al. 2000). Furthermore, a nonlinearity at the bipolar cell synapse creates a subunit structure in postsynaptic cells and extends sensitivity to high spatial frequencies (Demb et al. 2001a; Hochstein and Shapley 1976). Thus an amacrine cell mechanism for surround inhibition would be sensitive to both low and high spatial frequencies (Cook and McReynolds 1998; Cook et al. 1998; Demb et al. 1999; Flores-Herr et al. 2001; Taylor 1999).
Here, we asked how amacrine cell inhibition acts to suppress the center response of a ganglion cell. To this end, we presented high spatial frequencies in the ganglion cell's receptive field periphery to selectively stimulate amacrine pathways. Direct measurements showed that such frequencies only minimally stimulate horizontal cells. Therefore the suppressive effects measured in the ganglion cell must be driven by amacrine circuitry. An amacrine cell's inhibitory signal can reach the ganglion cell either directly, through a synapse on the dendrite, or indirectly, through a synapse onto a presynaptic bipolar terminal (Cook and McReynolds 1998; Cook et al. 1998; Flores-Herr et al. 2001; Shields and Lukasiewicz 2003; Taylor 1999). Here, we tested the relative contributions of these two synaptic sites and determined their individual roles in the suppression of the excitatory center response of the ganglion cell.
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
For each experimental session, a retinal whole mount (flattened eye cup) was prepared from a Hartley guinea pig (200–800 g). Procedures were in accordance with University of Pennsylvania, University of Michigan, and National Institutes of Health guidelines. In some cases, an animal was anesthetized with ketamine (100 mg/kg), xylazine (20 mg/kg), and pentobarbital (150 mg/kg), and both eyes were removed, after which the animal was killed by anesthetic overdose. In other cases, an animal was anesthetized with ketamine (40 mg/kg) and xylazine (4 mg/kg) and decapitated, and both eyes were removed. Each eye was hemisected, and the vitreous was peeled off in one piece. Five slits were cut, so that the eye cup would lay flat. The flattened eye cup, which included the neural retina attached to the pigment epithelium, choroid, and sclera, was mounted on a piece of circular filter paper and mounted flat in a chamber on a microscope stage. The filter paper was held in place by a teflon ring that fit tightly in the chamber walls. The tissue was superfused (4–6 ml/min) with oxygenated (95% O2-5% CO2) Ames medium (Sigma, St. Louis, MO) at 32–36°C (Demb et al. 1999). In some experiments, glucose was added to the medium (0.8–3.6 g/l); this increased osmolarity by 2–6% and did not have noticeable effects on the recordings.
Electrophysiology
For intracellular recordings of ganglion cells, Acridine orange (0.001%; Molecular Probes, Eugene, OR) was added to the superfusate, allowing ganglion cell somas to be identified by fluorescence during brief exposure to UV light. A soma in the visual streak was penetrated with a glass electrode (tip resistance, 80–200 M
) filled with 1% pyranine (Molecular Probes) in 2 M potassium acetate. Voltage was recorded with an intracellular amplifier (NeuroData, IR-283, NeuroData Instruments, Delaware Water Gap, PA) and digitized at 5 kHz using AxoScope software (Axon Instruments, Foster City, CA). For horizontal cells, recordings were obtained with glass electrodes (impedance, 80–200 M) back-filled with Alexa 488 or 568 and neurobiotin (5%) in 1.5 M potassium acetate. Membrane potential was amplified (NeuroData, IR-283), sampled at 2 kHz, and digitized by a computer for on-line analysis and storage (Apple Macintosh; 10-bit AD board; custom built software). Resting potential was recorded after cell penetration and continuously monitored during the recording. After the recording, horizontal cells were injected with the dye and fixed for confocal microscopy.
For extracellular (loose-patch) and whole cell recordings from ganglion cells, we identified large somas visualized with a cooled CCD camera (Retiga 1300C, QCapture software, Qimaging, Burnaby, British Columbia, Canada) (Hu et al. 2000). Positive pressure was applied through a patch electrode (3–6 M) and filled with Ames medium, and this electrode was used to "burrow" through the inner limiting membrane and clean the area surrounding the targeted cell (Roska and Werblin 2001). The same electrode was used to form a loose seal onto the targeted cell (30–100 M) and record spikes under voltage clamp (Vhold = 0 mV). This electrode was withdrawn, and a second electrode (3–6 M) filled with intracellular solution was used to obtain a >1-G seal. The patch was ruptured, and recordings were made in the whole cell configuration. Intracellular solution for current-clamp recordings contained (in mM) 140 K methylsulfate, 8 NaCl, 10 HEPES, 0.1 EGTA, 2 ATP-Mg, and 0.3 GTP-Na2, adjusted to pH 7.3. For voltage-clamp recordings, QX-314-Br was added (5 mM) to block sodium channels and improve space clamp, and NaCl was reduced to 3 mM. The calculated reversal for inhibitory responses (EGABA/glycine) would be approximately –73 mV for the first solution. For the second solution, containing Br–, EGABA/glycine should be more depolarized given that GABA/glycine channels are more permeable to Br– than Cl– by 20% (Bormann et al. 1987; Robertson 1989). In calculations below, we assumed that with Br– in the pipette, EGABA/glycine was approximately –68 mV. Junction potential (–9 mV) was corrected in all cases.
For voltage-clamp recordings, the holding potential was compensated for the voltage drop across the electrode tip, based on the following equation: Vh_corr = Vh –[Ileak Rs (1 –Rs_correct)], where Vh is the apparent holding potential before the stimulus (in mV), Ileak is the leak current (in nA), Rs is the series resistance (typically 12–30 M), and Rs_correct is the series resistance compensation (typically between 0.25 and 0.50). Holding potentials were typically restricted to a range negative to –30 mV; positive to –30, the cells showed a large outward current, which resembled a delayed-rectifier potassium current. Signals were recorded with a MultiClamp 700A amplifier and digitized at 10 kHz using pClamp 9.0 software (Axon Instruments).
Programs were written in Matlab (Mathworks, Natick, MA) to analyze responses (downsampled to 1 kHz) separately in the spike rate and subthreshold membrane potential, as described previously (Demb et al. 2001a; Zaghloul et al. 2003, 2005). To exclude the spikes from the voltage response, we down-sampled the raw data (recorded at 5 or 10 kHz) by taking the median value of every 5 or 10 sample points; this had the effect of removing spikes but preserving the subthreshold waveform. In some cells, we instead used a linear interpolation method, where we clipped out each spike with a line from 1 ms before to 1–2 ms after each spike (Zaghloul et al. 2003).
Data are reported as mean ± SE. Statistical significance was assessed using a one-tailed t-test.
Horizontal cell morphology
After recording horizontal cells, the retina was fixed in 4% paraformaldehyde for 20 min. Cells were visualized either based on the Alexa dye staining or by reacting for Neurobiotin by the following procedure: incubate with blocking buffer (10% normal goat serum, 5% Triton-X in 5% sodium phosphate buffer, 1 h); react with streptavidin-fluorescein (8 mg/ml) or streptavidin-Cy5 (40 mg/ml) for 3 h; wash with 5% sodium phosphate buffer (3 x 10 min); mount on a slide using Vectashield (Vector Laboratories, Burlingame, CA). Cells were imaged using a confocal microscope (Leica, Nussloch, Germany; x40 objective; NA, 1.25).
Visual stimulus
The stimulus was displayed on a miniature monochrome computer monitor (Lucivid MR1-103, Microbrightfield, Colchester, VT) projected through a microscope port and through a x2.5 or x4 objective focused on the photoreceptors. The monitor had a vertical refresh of 60 Hz and a spatial resolution of 640 x 480. The stimulus was confined to the central 480 x 480 pixels, which, when projected onto the retina, subtended 3 x 3 or 3.7 x 3.7 mm. The mean luminance was 103-104 isomerizations per middle-wavelength sensitive cone or rod per second, which is within the mesopic rang (Yin et al. 2006); in two cases, a lower mean luminance was used (102 isomerizations per cone or rod per second). The relationship between gun voltage and monitor intensity was linearized in software with a lookup table. Stimuli were programmed in Matlab as described previously (Brainard 1997; Demb et al. 1999; Pelli 1997).
All ganglion cell experiments used a dynamic modulation of a low-contrast spot (diameter, 0.5 mm) centered on the cell body and therefore approximately centered on both the dendritic tree and the 0.5- to 0.7-mm-diam receptive field center (Demb et al. 2001a,b; Dhingra et al. 2003). The spot will necessarily stimulate both the classical center and the surround, because these overlap in space (Enroth-Cugell and Robson 1966). However, the purpose of the spot was to stimulate the bipolar cells that synapse onto the ganglion cell dendritic tree, and for this purpose, the spot, even if centering was off by 0.1 mm, would be adequate to stimulate most of these bipolar cells.
The spot intensity was updated at 60 Hz, with values drawn randomly from a Gaussian distribution. This stimulus approximates "white noise" (Marmarelis and Marmarelis 1978; Sakai and Naka 1987; Fig. 2). The stimulus distribution is presented in contrast units, where the intensity has been normalized by subtracting the mean luminance and dividing by the mean luminance. Thus the stimulus had a mean of 0 and a range of –1 to +1. Stimulus contrast is defined by the Gaussian SD, which was always 0.10. The stimulus lasted 240 s and included twelve 20-s periods: 10 s of the spot (spot alone) alternating with 10 s of the spot plus a drifting grating (spot + grating). The grating was presented in the periphery, excluded from a 1-mm patch centered on the cell body. The grating had a square-wave profile with a spatial frequency of 4.3 or 5.0 cycles/mm (bar width = 116 or 100 µm), a temporal frequency of 2 Hz, and a contrast of 1.0. The analysis was performed on data collected during the last 8 s of each 10-s half-period. Thus for each contrast, there was 8 x 12 = 96 s of data. This relatively short stimulus increased the probability of highly stable intracellular recordings and provided data with sufficient signal-to-noise for the analysis. Because of the alternating contrast half-periods, any instabilities during the recording should be distributed equally between the two half-periods. Horizontal cell recordings used drifting sine-wave gratings (2 Hz, 0.7 contrast, 0.05–6 cycles/mm) presented over a field of 2.4 x 3.2 mm.
|
We analyzed both subthreshold and spiking responses using a linear-nonlinear (LN) cascade analysis (Carandini et al. 2005; Chichilnisky 2001). In this analysis, a linear filter represents the impulse response function of a cell, or the theoretical response to a brief light flash (Fig. 2). This filter, plotted in reverse, represents the cells weighting function. The linear prediction of the response (i.e., the linear model) is calculated at a given point in time by multiplying the stimulus by the weighting function and summing the result (i.e., convolution; Carandini et al. 2005). To compute the L filter, f(t), we cross-correlated the stimulus and the response (Chichilnisky 2001; Lee and Schetzen 1965; Sakai et al. 1995; Wiener 1958). To generate the L model of response (rL), we convolved (*) the stimulus [s(t)] and the L filter
 |
 |
; Chichilnisky 2001; Kim and Rieke 2001; Sakai et al. 1995; Victor 1987; Zaghloul et al. 2003).
To generate the N function, we plotted the L model versus the actual response (analyzed after down-sampling to 1 kHz) and binned the data (1000 samples/bin). These binned points represent the average response output (in spikes/s, mV, or pA) at each level of the L model value (in arbitrary input units). For the simultaneous fitting procedure described below, we required a descriptive function fitted through the points of the N function. A Gaussian cumulative distribution function (cdf) provides a good fit to the spike N function (Chander and Chichilnisky 2001; Zaghloul et al. 2003, 2005)
 |
), the response gain (
), and the spike threshold (
) (Chander and Chichilnisky 2001; Chichilnisky 2001). The fit was performed using standard routines in Matlab that minimize the mean-squared error (MSE) between the data and the fitted line (Fig. 2). For the membrane N points, we fit the data with the function
 |
allows for a vertical offset (because the membrane N function goes negative). As we found previously, the Gaussian cdf provided a good fit for the membrane potential N function for ON cells but not for OFF cells (Zaghloul et al. 2003). Thus for OFF cells, we fit the positive and negative sides of the membrane N function separately (Zaghloul et al. 2003, 2005). Analysis: simultaneous fit of spike responses to the spot alone and spot + grating conditions
There is a free scale factor in the LN model: the y-axis of the filter and the x-axis of the nonlinear function can be scaled by the same amount without changing the output of the model (Chander and Chichilnisky 2001; Kim and Rieke 2001; Zaghloul et al. 2005). Therefore to compare between the two conditions, we initially multiplied each filter by a factor, so that the variance of the two linear models was equal (Baccus and Meister 2002). We scaled the nonlinearities by the same factors (i.e., a stretch along the x-axis) so that each LN model output remained unchanged. After normalizing in this way, the two filters showed only slight differences in kinetics, but there was a large difference between the N functions: the peripheral grating typically reduced the spike rate, which resulted in an apparent rightward shift of the N function.
We considered three models to explain the effect of the peripheral grating on the N stage of the model. First, we considered a gain change model in which the two curves were fit with the same Gaussian cdf, except for a scaling along the x-axis. Specifically, both curves were fit with the same and parameters, but unique parameters (i.e., total of four parameters). This model was used previously to account for the reduced gain evoked by an increase in the contrast of the spot itself (Beaudoin et al. 2007; Chander and Chichilnisky 2001; Kim and Rieke 2001; Zaghloul et al. 2005). However, based on a preliminary analysis of OFF cells, it was apparent that this model failed in a systematic way: an underestimation of the small amplitude responses to the spot alone and an overestimation of the small amplitude responses in the presence of the grating. Therefore we considered a threshold change model in which the two curves were fit with the same Gaussian cdf except for a horizontal shift along the x-axis. Specifically, both curves were fit with the same and parameters, but unique parameters (i.e., total of four parameters). Finally, we considered a combined model that allowed for both a gain change and a threshold change. Specifically, both curves were fit with the same parameters but unique and parameters (i.e., total of 5 parameters).
The gain change and threshold change models have the same form and equal number of parameters so we could directly compare the MSE of these models to determine which provided a better fit to the data. The combined model has an additional parameter and so we expect it to yield a lower MSE. However, the combined model allowed us to test whether the gain change or threshold change dominated in explaining the difference between the two contrast curves when both gain and threshold were allowed to vary.
Analysis: simultaneous fit of subthreshold responses to the spot alone and spot + grating conditions
For membrane potential or current responses, we fit both low and high contrast N functions with the Gaussian cdf plus offset. The low and high contrast functions were fit with the same and parameters but unique and parameters. Thus the fitted N functions were similar to those for the spike N functions, except that membrane potential N functions were allowed to have unique offsets (i.e., unique y-intercepts because of the unique ). The unique offsets were necessary, because in some cases, the y-intercept of the membrane N function differed between contrasts by several millivolts or tens of picoamperes (Baccus and Meister 2002). Furthermore, the gain change between conditions (change in ) was independent of the change in the y-intercept. For ON cells, we performed the simultaneous fit with the cdf to the full nonlinear function, whereas for OFF cells, we fit just the excitatory (depolarizing or inward current) side of the nonlinear function (Zaghloul et al. 2003, 2005). For the subthreshold response, the above gain change model showed good fits, without systematic errors of any sort, and so we did not consider alternative models as we did for the spike response.
Evaluation of the LN model
The LN model is useful to the extent that it provides an accurate representation of a cell's response. We evaluated model accuracy by building the model based on data collected with one stimulus and testing the model's predictive power (explained variance: r2) on the averaged response to a second stimulus. The model-building stimulus was a modulated spot, as described above: twelve 20-s periods (10 s of spot alone and 10 s of spot + grating). The model-testing stimulus was a 2-s segment of spot modulation, repeated 10 times in a 20-s period (10 s of spot alone and 10 s of spot + grating). The model-testing period (MT) was interspersed six times within the model building periods (MB): 3 MT; 2 MB; 3 MT; 2 MB; 3 MT; 2 MB; and 3 MT. In all cases, we only analyzed the last 8 s of data from each 10-s half-period. Thus the LN model was built from 96 s of data for both spot alone and spot + grating conditions. The response to the 2-s model-testing segment was averaged over 24 repeats for both spot alone and spot + grating conditions. We tested how well the LN model predicted the averaged response to the 2-s model-testing segment (Fig. 2). The model was assessed in this way for 14 cells (11 OFF cells and 3 ON cells) and for both spiking and subthreshold measurements. For spiking responses, we evaluated the model in cases where the firing rate was at least 1 spike/s for both conditions. We evaluated model fits for the theshold change model, which generally yielded better fits than the gain change model.
For spiking responses, the r2 between the LN model and the data were 0.80 ± 0.03 for the spot alone condition and 0.65 ± 0.03 for the spot + grating condition (n = 13). For membrane potential responses, the r2 between the LN model and the data were 0.94 ± 0.01 for the spot alone condition and 0.90 ± 0.01 for the spot + grating condition (n = 11). Thus the LN model generally gave a better fit for the spot alone condition than for the spot + grating condition, although this difference was most marked for spiking responses; membrane potential responses were very well captured by the LN model in either condition. The relatively low r2 value for spiking response to the spot + grating condition was likely caused by the lower spike rates in this condition.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
We targeted Y-type () ganglion cells, because these cells show significant peripheral suppression in vivo and because we could identify them routinely by their large cell bodies in the visual streak (Caldwell and Daw 1978b; Demb et al. 1999, 2001a; Enroth-Cugell and Jakiela 1980; Peichl et al. 1987). We presented a series of stimuli to confirm that the cell had the characteristic properties: a "brisk-transient" center response to a spot (0.5 mm diam), an antagonistic surround response to an annulus (inner diameter, 0.74; outer diameter, 2.0 mm), and a nonlinear (frequency-doubled) response to a contrast-reversing grating (spatial frequency = 4.3 or 5.0 cycles/mm) (Cleland and Levick 1974; Demb et al. 2001a,b; Enroth-Cugell and Robson 1966; Hochstein and Shapley 1976). In whole cell recordings, cells had an input resistance of 32 ± 3 (SE) M (n = 17), similar to our previous measurements and similar to Y-types cells in the cat retina (Beaudoin et al. 2007; Cohen 2001; Manookin and Demb 2006; O'Brien et al. 2002; Zaghloul et al. 2003).
Here we report on 65 ganglion cells (48 OFF-center and 17 ON-center). As in previous studies, we had a bias toward successful recordings from OFF-center cells (Zaghloul et al. 2003, 2005), which outnumber their ON-center counterparts by about twofold (B. Borghuis, unpublished observations). We recorded extracellularly from 36 cells (26 OFF-center and 10 ON-center), which, when viewing a gray screen at mean luminance, fired spontaneously at 10 ± 3 spikes/s (range = 0–57). We recorded intracellularly from 32 cells (25 OFF-center and 7 ON-center), which fired spontaneously at 5 ± 2 spikes/s (range = 0–49) and rested at –64.1 ± 0.9 mV. Ten cells were recorded with sharp intracellular electrodes, and 22 were recorded with whole cell patch electrodes. We made additional whole cell recordings with QX-314 (5 mM) added to the pipette solution to block spiking (n = 13 cells). Some cells were recorded by both extracellular and intracellular methods (n = 23 cells). Our main criteria for stable intracellular recordings were a low resting potential and a well-modulated light response to the flickering spot stimulus described below.
Spatial tuning of horizontal cells in the guinea pig retina
We recorded horizontal cells to assess their spatial sensitivity in the guinea pig retina. Here we report on cells that were successfully filled with a fluorescent dye to identify the type (A-type or B-type; Peichl and Gonzalez-Soriano 1993). At mean luminance, horizontal cells rested at –47 ± 5 mV (n = 30). Responses were recorded to sine-wave gratings at a range of spatial frequencies; the response was quantified as the amplitude of the best fitting sine-wave at the 2-Hz drift frequency (F1 amplitude). Maximal response amplitudes were 3.8 ± 0.5 (A-type; n = 24) and 3.2 ± 1.1 mV (B-type; n = 6). Both types of horizontal cell showed low-pass spatial sensitivity with half-maximal sensitivities near 0.4–0.5 cycles/mm (Fig. 3D). At 5 cycles/mm, the response declined to 4 (HA cells, n = 24) or 7% (HB cells, n = 6) of the peak response (Fig. 3D). In the following experiments, we used spatial frequencies of 4.3 or 5.0 cycles/mm. At these frequencies, the bar width (100 µm) should match the putative width of a bipolar cell receptive field, which drive amacrine cells but is too fine to strongly stimulate horizontal cells (Fig. 3) (Dacey et al. 2000; Demb 1999, 2001a,b; Lankheet 1992; Passaglia et al. 2001; Werblin 1972).
|
We first quantified the effect of the peripheral grating on the ganglion cell receptive field center as measured in the spike response. To stimulate the cell, a 0.5-mm-diam spot flickered over the center; on each frame of the monitor (16.7 ms), an intensity value was drawn randomly from a Gaussian distribution (see METHODS and Fig. 4A). This stimulus approximates white noise, with approximately equal stimulus energy over the range of temporal frequencies to which the cell is most sensitive (Zaghloul et al. 2005). In alternate 10-s half-cycles, the spot was presented either alone or in the presence of a grating in the peripheral receptive field (Fig. 4A). During the 10-s presentation, the grating drifted at 2 Hz and thus maintained a constant contrast signal at each point in the periphery (Enroth-Cugell and Jakiela 1980).
|
Peripheral contrast causes the linear filter to become more biphasic in OFF-center cells
To further quantify the impact of the peripheral grating on the center response, we analyzed the spike train with an LN analysis (see METHODS and Fig. 2). The goal of the LN analysis was to separate changes in the temporal sensitivity of the cell from changes in contrast sensitivity (Carandini et al. 2005; Chichilnisky 2001). The linear filter indicates the temporal sensitivity of the cell. The nonlinear function shows the relationship between the filtered contrast (i.e., the linear model of the response) and the output of the cell; the slope of this function indicates the contrast sensitivity (see METHODS). For the spike response, we restricted our analysis to those cells that fired at a rate of at least 1 spike/s in both conditions and cells that showed a significant reduction in firing rate during the spot + grating condition (n = 40 OFF cells and 5 ON cells).
For OFF cells, the grating caused the linear filter to become more biphasic (Fig. 5C). To quantify the change in the shape of the filter, we compared the amplitude of the first phase of the response (i.e., positive response for ON cells; negative response for OFF cells), with the second phase of the response (i.e., negative response for ON cells; positive response for OFF cells). We calculated a biphasic index, which was the second phase amplitude (s2 or sg2 for the spot alone or spot + grating) divided by the first phase amplitude (s1 or sg1). If there were no second phase of the response, the index would be zero, whereas if the second phase amplitude equaled the first phase amplitude, the index would be –1. Most cells, with or without the peripheral grating present, had an index between –0.2 and –1.0 (Fig. 5C).
|
Peripheral suppression in OFF cells is best explained by an increased spike threshold
We next compared the effect of the grating on the spiking nonlinear function. We considered two models, with an equal number of parameters (4), to describe the suppression of spiking caused by the grating. In the first model, the grating causes a gain change. The reduced gain corresponds to a reduced slope of the nonlinear function. We also considered a second model, in which the grating causes an increased threshold for spiking (threshold change model). This increased threshold corresponds to a rightward shift (on a linear axis) in the nonlinear function in the spot + grating condition (see METHODS). For an OFF cell, the threshold change model provided a more satisfactory fit (Fig. 5A). We also considered a combined model, with an additional parameter (5), in which the two nonlinear functions differed by both their gain (slope) and their threshold (horizontal position along the x-axis).
To quantify the difference between the gain change model and the threshold change model, we fit both models for each cell and compared the difference in MSE between each model and the data. For OFF cells (n = 40 cells), the threshold change model yielded lower MSE (7.2 ± 0.8 spikes/s) compared with the gain change model (11.1 ± 1.4 spikes/s); the MSE was relatively lower for the threshold change model by 27 ± 4% (P < 0.001). The combined model fit yielded a lower MSE (6.8 ± 0.7 spikes/s) compared with the models above, as expected based on the additional parameter added in the fit. However, the MSE for the combined model was only slightly lower than that for the threshold change model above. Furthermore, in the combined model fit, the relative gain in the presence of the grating was 0.98 ± 0.02 (i.e., reduction of 2%), and thus a gain change did not play a major role in describing the suppressed spiking. In the combined model, the threshold change (i.e., rightward shift) was 23.8 ± 1.4 arbitrary (linear model) units, similar to that found using the threshold change model (23.3 ± 1.5 units). Thus for OFF cells, the effect of the grating could be described concisely as an increased spike threshold.
For ON cells (n = 5), the MSE was not significantly lower for the threshold change model (31 ± 8 spikes/s) compared with the gain change model (57 ± 27 spikes/s); the threshold change model was lower by 19 ± 14% (not significant). For the combined model (MSE = 28 ± 7 spikes/s), the peripheral grating caused both a significant gain reduction (0.85 ± 0.03; i.e., reduction of 15%) and an increased threshold (rightward shift, 19 ± 6 units). Thus for this subset of ON cells (which showed significantly reduced firing during the grating), the effect of the grating could be described as a combination of an increased spike threshold and a reduced gain.
In the preceding experiments, the grating was always presented at full contrast (1.0). In six OFF cells, we further tested the effect of the grating at a lower contrast (0.25). Data were analyzed using the threshold change model. For these cells, the low contrast grating caused a rightward shift of the nonlinear function of 8 ± 2 linear model units at low contrast and 20 ± 3 units at high contrast. Thus there was a significant effect of the grating at low contrast with a significantly greater effect at high contrast (difference of 12 ± 2 units; P < 0.01). Therefore at the lower contrast level, the effect of the grating was not saturated.
Peripheral contrast causes tonic membrane hyperpolarization in OFF cells
To understand the mechanism underlying the above effects on spiking, we analyzed the effect of the peripheral grating on the subthreshold membrane potential. In OFF cells, the grating evoked a tonic membrane hyperpolarization (Fig. 6). The hyperpolarization was largest at the onset of the peripheral stimulus and slowly declined over the 10-s half-cycle. During the period of analysis (2–10 s after grating onset or offset), the grating hyperpolarized the membrane potential from –64.6 ± 0.9 to –65.7 ± 0.8 mV, a difference of 1.2 ± 0.2 mV (n = 25 cells; P < 0.001). At the offset of the peripheral stimulus, the membrane initially depolarized strongly, and this depolarization declined during the half-cycle (Fig. 6C).
|
Peripheral contrast reduces the response gain of subthreshold responses
In addition to tonic hyperpolarization of the membrane potential, the peripheral grating suppressed the amplitude of response fluctuations to the central spot (Fig. 6A). To quantify the nature of this suppression, we performed an LN analysis of the subthreshold response, similar to the analysis described above for the spike rate. Two LN models were fit that allowed, between conditions, a tonic membrane polarization (difference in the y-intercept of the nonlinearity) plus a gain change (difference in the slope of the nonlinearity; see METHODS). The example OFF cell shows the typical effect: the grating caused both hyperpolarization and a reduced gain (Fig. 7A; see also Fig. 2). The accompanying spike response is shown with the threshold change model fitted to the data (Fig. 7A; see also Fig. 2). For ON cells, there were two patterns. In the first pattern, the grating caused a hyperpolarization and reduced gain, similar to the OFF cell (Fig. 7B). In the second pattern, the grating caused a depolarization and reduced gain (Fig. 7C). The accompanying spike responses for the two ON cells are shown with the combined model fitted to the data (Fig. 7, B and C). For both patterns, the grating reduced the slope of the spike nonlinearity, but the rightward shift of the spike nonlinearity was only present in the case where the grating evoked hyperpolarization (Fig. 7B).
|
32%). For ON cells, the gain was reduced to 0.78 ± 0.06 (P < 0.001; i.e., reduction of 22%). The change in the y-intercept is a measure of membrane polarization, independent of the nonlinearity. The y-intercept showed a hyperpolarization for OFF cells (–1.0 ± 0.2 mV; P < 0.001), similar to the direct analysis of membrane potential above, but no significant change for ON cells (+0.3 ± 0.4 mV). Thus the reduced gain in the membrane potential response, observed in all cells, was not always accompanied by membrane hyperpolarization. The apparent independence of these two inhibitory effects suggests distinct underlying cellular mechanisms. To further analyze the relationship between the suppressive effects on spiking and subthreshold responses, we measured three correlations. This analysis was performed on OFF cells, where the effect of the grating on spiking could be described concisely as an increased threshold; the analysis was further restricted to those OFF cells that fired at a rate of at least 1 spike/s for both conditions (n = 21 cells). There was not a significant correlation between the reduced gain in the subthreshold response and the hyperpolarization (i.e., the change in the y-intercept of the nonlinear function; Fig. 8A). Thus these two effects on the subthreshold response could arise from different cellular mechanisms; we further support this conclusion with analyses below. The rightward shift of the spike response was correlated with both the hyperpolarization (r = –0.57; P < 0.05) and the reduced gain in the subthreshold response (r = –0.79; P < 0.01; Fig. 8, B and C). Thus the suppression of the spiking response was related to both suppressive effects expressed in the subthreshold potential.
|
We tested whether the hyperpolarization in OFF cells, caused by the peripheral grating, could be explained by a direct inhibitory synapse onto the ganglion cell dendrite. To test this, we made intracellular recordings with QX-314 in the pipette to block spiking and improve the space clamp. We measured the response to the peripheral grating in voltage clamp at several holding potentials. Every cell showed an increased conductance (i.e., positive slope on the I-V plot) during both the transient and sustained periods of the grating (Fig. 9). For OFF cells (n = 6), the conductance increased by 8 ± 2 nS for the transient response and 1.3 ± 0.4 nS for the sustained response. The transient response reversed at –94 ± 4 mV, and the sustained response (2–10 s after grating onset) reversed at –97 ± 18 mV.
|
gGABA/glycine) and decreased an excitatory conductance (gcation), which would move the reversal potential for the summed conductance (gtotal) negative to EGABA/glycine. To determine the relative contributions of the two underlying conductances [where the conductances represent changes () from resting conductances], we used the following formula
 |
gcation/gGABA/glycine), EGABA/glycine = –68 mV, Ecation = 0 mV, and Etotal was the measured reversal potential for the leak-subtracted response to the grating. The above equation follows from Ohm's law, given that, during the sustained period of the grating, ication = –iGABA/glycine. From this equation, we could divide the total conductance into the two underlying conductances (after establishing their relative contributions). This procedure is depicted graphically in Fig. 9C. For OFF cells (n = 6), the grating evoked an inhibitory conductance of 1.7 ± 0.4 nS (P < 0.05; 2-tailed t-test) in parallel with a decreased excitatory conductance of 0.43 ± 0.14 nS (P < 0.05; Fig. 9D). For ON cells (n = 4), the conductance increased by 5 ± 3 nS for the transient response and 2.2 ± 0.6 nS for the sustained response. The transient response reversed at –29 ± 1 mV, and the sustained response reversed at –55 ± 6 mV. For the sustained response, the grating evoked an inhibitory conductance of 1.8 ± 0.5 nS (P < 0.05) in parallel with an increased excitatory conductance of 0.41 ± 0.22 nS (not significant; Fig. 9D). Therefore in all cells, the grating evoked an increased inhibitory conductance, consistent with a direct inhibitory synapse on the ganglion cell dendrite. This inhibitory conductance was accompanied either by a decreased excitatory conductance (OFF cells) or a trend toward an increased excitatory conductance (ON cells) that depended on cell type.
Evidence that peripheral contrast reduces the gain of subthreshold ganglion cell center responses by inhibiting presynaptic bipolar cells
We next tested whether the reduced gain of the subthreshold response could be explained by postsynaptic shunting inhibition or rather by presynaptic inhibition of bipolar terminals. To test this, we made intracellular recordings with QX-314 in the pipette, to block spiking and improve space clamp, and compared recordings of membrane current (Im; with the holding potential, Vhold, near the resting potential) to recordings of membrane voltage (Vm). Consider first the voltage-clamp condition, where conductances in parallel would add. We considered the total membrane conductance as a sum of three conductances: synaptic conductance driven by the central spot (gcenter), synaptic conductance driven by the peripheral grating (ggrating), and a leak term (gleak), each with an associated reversal potential
 |
We used the LN model above to compare the effects of the grating on Im and Vm (Fig. 10). Across 13 cells (n = 9 OFF cells and 4 ON cells), the reduction in gain was the same in both conditions: the grating reduced the center gain to 0.73 ± 0.04 (i.e., 27% reduction) for Im and to 0.72 ± 0.04 (i.e., 28% reduction) for Vm (Fig. 10). The effect was similar for OFF cells (Vm, 0.73 ± 0.03; Im 0.74 ± 0.03) and ON cells (Vm, 0.70 ± 0.10; Im 0.69 ± 0.10). These gain reductions in Vm are consistent with those recorded with standard pipette solution above (22–32%). The reduced gain was accompanied in OFF cells by a slight hyperpolarization as reflected by a change in the y-intercept of the nonlinearity of –0.6 ± 0.5 mV (n = 9). This hyperpolarization was somewhat small and inconsistent across cells, compared with recordings above, perhaps caused by the more depolarized value for EGABA/glycine (i.e., with Br– in the pipette solution) and the accompanying decreased driving force on inhibition (see METHODS). Under voltage clamp, OFF cells showed a small outward current (16 ± 13 pA). For ON cells, there was little change in the y-intercept under either condition (current clamp: –0.2 ± 0.7 mV; voltage clamp: 7 ± 28 pA). In general, our main conclusion from the comparison of current-clamp and voltage-clamp recordings is based on the similar reduction in gain across the two conditions. The reduced gain of the ganglion cell center Vm response must be primarily caused by amacrine cell inhibition of presynaptic bipolar cell terminals.
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTS |
|---|
