Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15261
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ABSTRACT |
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Murthy, Aditya and Allen L. Humphrey. Inhibitory contributions to spatiotemporal receptive-field structure and direction selectivity in simple cells of cat area 17. Intracortical inhibition contributes to direction selectivity in primary visual cortex, but how it acts has been unclear. We investigated this problem in simple cells of cat area 17 by taking advantage of the link between spatiotemporal (S-T) receptive-field structure and direction selectivity. Most cells in layer 4 have S-T-oriented receptive fields in which gradients of response timing across the field confer a preferred direction of motion. Linear summation of responses across the receptive field, followed by a static nonlinear amplification, has been shown previously to account for directional tuning in layer 4. We tested the hypotheses that inhibition acts by altering S-T structure or the static nonlinearity or both. Drifting and counterphasing sinewave gratings were used to measure direction selectivity and S-T structure, respectively, in 17 layer 4 simple cells before and during iontophoresis of bicuculline methiodide (BMI), a GABAA antagonist. S-T orientation was quantified from fits to response temporal phase versus stimulus spatial phase data. Bicuculline reduced direction selectivity and S-T orientation in nearly all cells, and reductions in the two measures were well correlated (r = 0.81) and reversible. Using conventional linear predictions based on response phase and amplitude, we found that BMI-induced changes in S-T structure also accounted well for absolute changes in the amplitude and phase of responses to gratings drifting in the preferred and nonpreferred direction. For each cell we also calculated an exponent used to estimate the static nonlinearity. Bicuculline reduced the exponent in most cells, but the changes were not correlated with reductions in direction selectivity. We conclude that GABAA-mediated inhibition influences directional tuning in layer 4 primarily by sculpting S-T receptive-field structure. The source of the inhibition is likely to be other simple cells with certain spatiotemporal relationships to their target. Despite reductions in the two measures, most receptive fields maintained some directional tuning and S-T orientation during BMI. This suggests that their excitatory inputs, arising from the lateral geniculate nucleus and within area 17, are sufficient to create some S-T orientation and that inhibition accentuates it. Finally, BMI also reduced direction selectivity in 8 of 10 simple cells tested in layer 6, but the reductions were not accompanied by systematic changes in S-T structure. This reflects the fact that S-T orientation, as revealed by our first-order measures of the receptive field, is weak there normally. Inhibition likely affects layer 6 cells via more complex, nonlinear interactions.
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INTRODUCTION |
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The analysis of object motion in the visual world
begins in primary visual cortex (area 17) through the action of
direction-selective neurons (Hubel and Wiesel 1962).
These cells respond well to motion in one direction across their
receptive fields and weakly or not at all to motion in the opposite
direction. The mechanisms underlying this selectivity remain
unresolved. However, among simple cells, important insights have been
gained through the study of spatiotemporal (S-T) receptive-field
structure. Many direction-selective simple cells in cat area 17 have
S-T-oriented receptive fields in which response timing changes
gradually across the field (Albrecht and Geisler 1991
;
McLean and Palmer 1989
; Movshon et al.
1978
; Reid et al. 1991
; Saul and Humphrey
1992a
). This organization confers directional tuning: a
stimulus moving in a direction that successively activates
receptive-field positions with progressively shorter delays, or
response phases, elicits a larger net excitatory response than a
stimulus moving in the opposite direction. In contrast, all
nondirection-selective cells lack S-T-oriented receptive fields.
We recently showed (Humphrey and Saul 1998;
Murthy et al. 1998
) that S-T structure is well
correlated with directional tuning in layer 4 of cat area 17. The
degree to which cells are S-T oriented accounts for over half of their
directional tuning on average. We also showed that a linear-nonlinear,
or exponent, model (Albrecht and Geisler 1991
;
Heeger 1993
) accounts well for directional tuning in
most layer 4 cells. The model consists of two stages: a linear process
in which S-T orientation confers a directional bias and a static
nonlinear process that amplifies the bias to accentuate selectivity.
The nonlinearity may be a threshold or, equivalently, an exponential
amplification, either of which accentuates differences in response
amplitude to optimal versus nonoptimal stimuli. The exponent model,
however, does not account for directional tuning in layer 6 because
receptive fields there are weakly S-T oriented and unrealistically
large static nonlinearities are required to account for their tuning
(Murthy et al. 1998
). Dynamic nonlinear interactions
(Emerson and Citron 1992
) likely predominate in layer 6.
Intracortical inhibition is important for direction selectivity, as
evidenced by the fact that blocking GABAA-mediated
inhibition reduces selectivity in most simple cells (Sillito
1984). How inhibition acts is not clear, however. One
hypothesis is that it creates or enhances S-T orientation. If so, then
blocking inhibition should produce a reduction in S-T orientation that
is correlated with a loss of directional tuning. An alternative, though
not mutually exclusive, hypothesis is that inhibition is "flat,"
merely suppressing weak responses (Sato et al. 1995
). It
might act by lowering membrane potentials relative to spike threshold.
This iceberg effect should enhance an initial directional bias but not
affect response timing. In terms of the exponent model, if this was the
primary inhibition, then blocking it should reduce direction
selectivity and change the value of the exponent but not alter S-T orientation.
To evaluate these two hypotheses, S-T structure and direction selectivity were assessed in simple cells by measuring responses to stationary counterphasing and drifting gratings, respectively. Bicuculline methiodide (BMI), a GABAA antagonist, was applied iontophoretically to reduce intracortical inhibition. We observed that BMI reduced direction selectivity in most cells. In layer 4, the effect was paralleled by a well-correlated reduction in S-T orientation. In layer 6, no systematic changes in S-T structure were seen. We also calculated for each layer 4 cell the value of an exponent that represents the static nonlinearity. Application of BMI reduced the exponent in most cells, but the reduction was not correlated with the changes in directional tuning. Thus inhibition affects direction selectivity in layer 4 primarily by enhancing S-T orientation and secondarily by accentuating the static nonlinearity. The measures used here do not allow us to discern how inhibition operates in layer 6.
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METHODS |
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Physiological preparation
Details of surgical preparation are described elsewhere
(Murthy et al. 1998; Saul and Humphrey
1990
). Briefly, adult cats were anesthetized throughout the
experiment using halothane in nitrous oxide and oxygen. A tracheostomy
was performed, paralysis was induced using gallamine triethiodide and
D-tubocurarine chloride, and the animal was ventilated
artificially. Heart rate, mean arterial blood pressure, and the
cortical electroencephalogram were monitored continuously to assess
physiological state. The halothane level was adjusted to maintain the
dominant frequencies of the electroencephalogram <4 Hz during all
stages of the experiment. Lactated Ringer solution was infused
intravenously to maintain hydration. The corneas were covered with
contact lenses fitted with 3-mm artificial pupils.
Recording, visual stimulation, and iontophoresis
Extracellular recordings of single neurons were made using
micropipettes filled with 0.6-2.0 M KCl (~35-20 M). The
recording electrode was glued to a three-barrel micropipette array,
with its tip protruding from the array by ~20 µm (Havey and
Caspari 1980
). The array tip was broken to 5-7 µm, yielding
an inner diameter of ~1.5 µm per barrel. Each barrel contained one
of the following solutions: bicuculline methiodide (BMI; 2.5 mM in 165 mM NaCl, pH = 3); gamma-amino butyric acid (GABA; 0.5 M, pH = 3); or sodium acetate (2.0 M) for current balancing, to which 4%
Pontamine sky blue was added. Drug barrels were subject to constant
retaining currents when not in use, using
18 nA for GABA and
10 to
15 nA for BMI. Currents were controlled using a Neurophore
iontophoresis unit.
Receptive fields initially were mapped manually on a tangent screen.
All subsequent stimuli were presented monocularly at 57 cm on a
Tektronix 608 monitor driven by a Picasso image synthesizer linked to
an LSI-11/73 computer. Mean luminance was 15 cd/m2 and
Rayleigh-Michelson contrast was ~0.4. Simple cells were identified using standard criteria (Hubel and Wiesel 1962;
Skottun et al. 1991
).
Drifting sinewave gratings were used to determine each cell's optimal
stimulus orientation and spatial and temporal frequency (Humphrey and Saul 1998). A set of randomly interleaved
counterphasing and drifting gratings then was presented before
(control) and during iontophoresis of BMI and, when possible, after
recovery from the drug (postcontrol). The counterphasing grating was
presented at eight spatial phases over one-half cycle of the stimulus
spatial frequency. Control responses usually reflected ~5 trials of
each phase of the counterphase stimulus and 12-20 trials of each
direction of the drifting stimulus. Trial duration was 4-6 s. The
number of trials during BMI application was variable; it depended on the latency and strength of the BMI effect but was usually greater than
the number of control trials.
To test the effectiveness of BMI and determine approximate levels of
ejection current, we performed a standard titration procedure (Sillito 1977, 1984
) on most cells. GABA was
iontophoresed at a current that just suppressed the response to a
grating moving in the preferred direction. While continuing to eject
GABA, BMI ejection current was activated and increased until the
response returned to control levels. One such titration is shown in
Fig. 1, which plots a cell's response
amplitude over time. Here, 6 nA of GABA was sufficient to suppress the
control responses. Within 3 min of simultaneously applying BMI at 12 nA, the GABA effects were reversed. Cessation of both drugs led to
recovery of the cell's firing rate to original values within 2 min.
Having determined an effective BMI current by this procedure, we then
used it as a starting value in the visual tests.
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Data analysis
Action potentials were summed into peristimulus time histograms
(PSTHs) to measure the average response per cycle of the periodic stimulus. First harmonic (±SE) amplitude and temporal phase were obtained for each PSTH, with response phase expressed in cycles relative to the stimulus (Saul and Humphrey 1990).
From responses to moving gratings we computed a directional
index (DI) as DI = (PD NPD)/(PD + NPD), where PD and
NPD are the response amplitudes in the preferred and nonpreferred
directions of motion, respectively. Ratios of 0 and 1, respectively,
reflect no and complete direction selectivity. An estimate of the
standard error of DI was obtained from separate DI measurements on
individual sets of trials. Only cells with DIs >0.33 were considered
selective and subsequently tested with BMI.
Responses to counterphasing gratings were used to characterize S-T
receptive-field structure. We relied primarily on a recently developed
method described in detail by Murthy et al. (1998). Because the present results require understanding the method and its
rationale, we summarize it here. In a strictly linear model of
directional tuning, a stationary, counterphasing sinewave grating elicits predictable patterns of response amplitude and phase as a
function of grating position in the simple-cell receptive field. For a
completely direction-selective cell, as the spatial phase of the
grating changes, response phase changes monotonically with a slope of 1.
In contrast, amplitude remains constant and an amplitude modulation
(AM) ratio, defined as [1 (min amp/max amp)], is 0. For a
directionally nonselective cell, response phase is constant (i.e.,
slope = 0) within each half of the grating cycle. However, amplitude varies sinusoidally with spatial phase and the AM ratio is 1. For a cell with intermediate directional tuning, amplitude also
fluctuates sinusoidally and the modulation ratio lies between 0 and 1, and the response phase versus spatial phase data do not follow a
straight line but are fit by an arctangent function (e.g., Fig.
4A). We used this fit to derive a spatiotemporal
index (STI) for each cell that reflects the slope of the function
at the spatial phase generating the maximum response. The STI is a
metric that summarizes the S-T orientation of the receptive field. STI
is 1 and 0, respectively, for receptive fields that are completely S-T
oriented or unoriented. In a strictly linear system, S-T orientation determines directional tuning; thus STI and DI values are equal (see
Murthy et al. 1998
for derivation of the relationship).
In a linear system, S-T orientation and AM to counterphased gratings
are related inversely. Hence either or both measures potentially
predict direction selectivity. However, cells are subject to
nonlinearities that, in the context of direction selectivity, have been
modeled as static nonlinearities (Albrecht and Geisler 1991; Heeger 1993
). They accentuate differences
in amplitude to optimal versus nonoptimal stimuli and hence increase AM
ratios beyond those due to linear summation. Thus conventional linear predictions of direction selectivity that use response amplitude (e.g.,
Reid et al. 1991
) underestimate the linear contribution because of nonlinear amplitude distortion. In contrast, response phase
is not affected by static nonlinearities, so the phase-based measure,
STI, provides a better estimate of the linear contribution. We used the
STI to quantify changes in the temporal organization of the receptive
field induced during blockade of GABAA-mediated inhibition
and to estimate their linear contribution to changes in direction
selectivity. As described in the RESULTS, we also employed
the STI to evaluate the contribution of static nonlinear processes to
directional tuning.
In addition to the STI, we used phase and amplitude measures to make
conventional linear predictions of directional tuning for comparison
with our STI-based measures and to examine relationships between S-T
structure and direction selectivity that require information about
amplitude. We used a superposition method similar to that of
Jagadeesh et al. (1997). It derives from the fact that
the sum of two counterphasing gratings in spatial and temporal
quadrature constitutes a drifting grating. Assuming linearity, the
responses to the counterphasing gratings equal those to the drifting
grating. We identified pairs of gratings in spatial quadrature from the eight spatial phases tested. First harmonic response amplitude and
phase at each spatial phase were expressed as a vector in polar
coordinates. Temporal quadrature was simulated by translating the
response phase of one grating in each pair by a quarter cycle. The
paired responses were summed vectorially to give predicted responses to
a grating drifting in each of two directions. A mean predicted
amplitude and phase1 was calculated from the
four quadrature pairs. Predicted amplitudes to each direction of motion
also were used to derive a predicted DI.
For ease in viewing, the counterphase data from control trials were normalized in three ways. First, response phase was plotted so as to increase with increasing spatial phase, thereby normalizing for preferred direction of motion. Second, the response amplitude and phase functions were shifted equally horizontally so that amplitude peaked near 0.25 and 0.75 cycles. Third, the phase functions were shifted vertically to pass through the origin. The BMI and postcontrol data were shifted similarly to maintain spatial phase correspondence in the different conditions.
Reconstructing recording sites
Electrode penetrations were marked by extracellular deposits of
Pontamine dye. Animals were administered a lethal dose of Nembutal and
perfused with aldehydes. Brain sections were stained for Nissl
substance, electrode tracks were reconstructed (Murthy et al.
1998), and cells' recording locations were assigned using the
laminar criteria of O'Leary (1941
; Humphrey et
al. 1985
).
Statistics
All comparisons of means were made using a paired
t-test (Miller and Freund 1985). Pearson
product-moment (r) or Spearman rank
(rs) correlations were used for other comparisons.
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RESULTS |
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Results are based on 27 simple cells from 17 cats; 17 cells were in layer 4 and 10 were in layer 6. We first describe changes in direction selectivity and S-T receptive-field structure produced by GABAA blockade in three representative cells. We next summarize how the blockade affected the population and show that effects on S-T structure differed between layers 4 and 6. We then show that inhibition contributes to direction selectivity in layer 4 mainly by increasing S-T orientation.
Effect of BMI on direction selectivity and S-T structure in individual cells
Figure 2A shows average responses of a layer 4 cell to one cycle of a sinewave grating drifting at 2 Hz during control, BMI, and postcontrol conditions. During control trials, the cell was highly direction selective (DI = 0.92), discharging vigorously in the preferred direction of motion (Fig. 2A, bottom) and weakly in the nonpreferred direction (top). Within 4 min of iontophoresing BMI, selectivity was abolished (DI = 0). To facilitate comparison, control responses (· · ·) are superimposed on the BMI data. Interestingly, the loss of direction selectivity in this cell reflected both an increase in response amplitude to the nonpreferred direction and a decrease in amplitude to the preferred direction. Additionally, there were shifts in response timing that were most visible in the preferred direction: the response during BMI was delayed by about a quarter cycle relative to the control response. The drug effect was reversible as evident in the postcontrol trials taken within 3 min of terminating BMI. We show later that BMI-induced changes in amplitude and timing to moving gratings reflect changes in the amplitude and timing structure of the receptive field.
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The cell's S-T structure during the three conditions is shown in Fig. 3. The PSTHs illustrate responses to a 2-Hz counterphasing grating presented at different spatial phases in the receptive field. During control trials (Fig. 3A), the receptive field displayed clear S-T orientation, as evidenced by a gradual shift in response timing with increasing spatial phase. To further illustrate this, mean phase values are plotted against spatial phase in Fig. 4A. An arctangent function fit to the response phase data yielded an S-T orientation index (STI) of 0.62.
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Figure 3B illustrates the changes in S-T structure during iontophoresis of BMI. Control responses are shown superimposed on the corresponding BMI profiles. Each pair of PSTHs is normalized to equate maximum firing rates to better illustrate the relative changes in timing induced by the drug. Reduction of inhibition produced clear timing changes at all spatial phases; responses were elicited later than in control trials. These shifts are defined as phase lags. In Fig. 4C the lags are plotted as increases in response phase, and all were statistically significant (P < 0.05).
Figures 3B and 4C also reveal that the
BMI-associated response did not lag the control discharge uniformly
across spatial phase. Phase lags were greatest at zero spatial phase
and progressively less up to ~0.44 cycles. Because of symmetry
(Movshon et al. 1978; Reid et al. 1991
),
the same timings were duplicated in the second half cycle. The changes
resulted in much more uniform timing within each half of the grating
cycle, and the receptive field became essentially S-T unoriented
(STI = 0.16). The loss of S-T orientation would be expected in a
linear model of directional tuning.
In such a model, amplitude profiles vary systematically with direction
selectivity (see METHODS) (Murthy et al. 1998). For a cell
with a DI of 1.0, amplitude should be constant (i.e., unmodulated) as
the position of a counterphased grating changes. As DI decreases, the
degree of modulation should increase. Figure 4D shows that BMI altered the AM ratio, increasing it to 0.90 from a control value of
0.67. Overall, then, the changes in phase and amplitude during
GABAA blockade are consistent with a linear spatiotemporal model of direction selectivity. This was supported by the conventional linear predictions using the superposition method: BMI reduced predicted DI from 0.47 to 0.08.
After cessation of the GABAA antagonist, the control pattern of response timings was reinstated (Figs. 3C and 4E), S-T orientation was again clearly discernable (STI = 0.70), and the AM ratio decreased to 0.75, its approximate control value. The conventional linear prediction of direction selectivity (0.40) also returned to a near-control value.
The aforementioned cell was one of the most striking examples of the effects of reducing inhibition. Another simple cell in layer 4, the direction selectivity of which was reduced but not abolished by BMI, is shown in Fig. 2B. This result was the more typical one. The cell was highly direction selective (DI = 0.93) during control trials. Within 2 min of applying BMI, responses to both directions of motion increased by about the same amount. However, the relative increase in response to the nonpreferred direction was greater, resulting in a 53% reduction in DI. The effect of BMI was reversible, as seen in the postcontrol data. Unlike the previous cell, these changes were not accompanied by any significant shift in response phase to the drifting grating.
Figure 5, B and C, shows that the BMI-induced changes in the cell's direction selectivity reflected changes in S-T orientation: STI decreased from 0.51 to 0.18. Unlike the previous cell, timing changes were associated with phase leads not lags, and they did not occur at all positions. Significant shifts occurred only between spatial phases of 0.25 and 0.38 cycles (and 0.75-0.88 cycles). However, as before, the phase shifts resulted in more uniform timing across the receptive field.
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The decrease in this cell's direction selectivity also was accompanied by increases in response amplitude to the counterphasing grating (Fig. 5D), although the AM ratio changed little, from 0.89 to 0.81. The superposition analysis predicted a reduction in DI from 0.31 to 0.19. After cessation of the GABAA block, timings, S-T orientation and amplitudes returned to approximate control values (not illustrated).
Figure 2C illustrates the effect of reducing inhibition on a simple cell in layer 6. Like the previous example, the response to each direction of motion increased during BMI but the relative change in the nonpreferred direction was greater, reducing DI from 0.97 to 0.65. Strong directional tuning returned in the postcontrol trials. Figure 6 illustrates the cell's S-T structure. Unlike the layer 4 cells, this receptive field lacked prominent S-T orientation during control trials (STI = 0.14; Fig. 6, A and C). BMI induced slight phase leads at some positions but the timing shifts did not significantly change S-T orientation (Fig. 6, B and C). This is not surprising given the initially low S-T orientation. The reduction in DI was accompanied by an increase in the AM ratio, from about 0.67 to 0.81 (Fig. 6D). The superposition analysis predicted a reduction in DI from 0.37 to 0.17. Thus for this and two other layer 6 cells (not illustrated), changes in S-T structure correctly predicted the reduction in direction selectivity. However, unlike layer 4 cells, the changes largely reflected alterations in the AM ratio rather than in S-T orientation. Further, in other layer 6 cells (see following text) the minor changes in S-T structure induced by GABAA blockade predicted an increase in DI but a decrease was seen. Overall, changes in S-T structure accounted poorly for changes in directional tuning in this and other layer 6 cells.
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Population results
EFFECT OF BMI ON DIRECTION SELECTIVITY. Figure 7 plots the DI under control versus BMI conditions for each cell. Most (89%) cells lie significantly below the line of unity slope, indicating that the drug reduced their direction selectivity. However, the strength of the effect varied widely, from slight reductions to complete loss. For 37% of the cells, DI was reduced to <0.33, our criterion for selectivity, but most of these cells maintained a directional bias. Interestingly, two cells reversed their preferred direction: one was patently selective and the other was biased for direction.
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EFFECT OF BMI ON SPATIOTEMPORAL STRUCTURE. Clear differences between layers 4 and 6 were observed in the action of BMI on S-T structure. Figure 8A plots the S-T orientation of each cell during control and BMI conditions. During control trials, layer 4 cells displayed a wide range of STI values, from 0.16 to 0.83 (mean = 0.41 ± 0.04). During blockade of inhibition, STI was significantly reduced in 12 of the 16 cells (mean STI = 0.17 ± 0.03; P < 0.05). Similar to the effect on DI, however, the change in STI varied among cells; a few became completely S-T unoriented but most continued to display an obvious spatiotemporal bias.
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JOINT EFFECT OF BMI ON S-T STRUCTURE AND DIRECTION SELECTIVITY IN LAYER 4. Figure 9 shows that the changes in direction selectivity during blockade of GABAA mediated inhibition largely were accounted for by the alterations in S-T receptive-field structure. Figure 9A plots the percent change in STI versus percent change in DI induced by BMI. Most cells lie in the third quadrant, confirming that a reduced DI was almost always accompanied by a lowered STI. For these cells, reductions in the two measures were well correlated (r = 0.81). A similar analysis comparing conventional linear predictions against actual direction selectivity is shown in Fig. 9B. The change in DI was correlated moderately with the change in predicted DI, although the relationship was more variable (r = 0.67) than between DI and STI.
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EFFECT OF BMI ON RESPONSE AMPLITUDE TO DRIFTING GRATINGS. Because the BMI effects on direction selectivity in layer 4 largely could be accounted for by changes in S-T structure, we wondered whether alterations in response amplitude to drifting gratings could be predicted similarly from the counterphase data. Using the superposition method, we computed a predicted response amplitude for each direction of motion in the control and BMI conditions. Subtraction of the BMI predicted amplitudes from control predicted amplitudes yielded the predicted amplitude change for each direction due to the reduced inhibition. Likewise, the measured amplitudes to drifting gratings in the two conditions were subtracted to yield the observed amplitude change due to BMI.
Figure 10, A and B, plots the predicted versus observed amplitude change for the preferred and nonpreferred directions of motion, respectively. For most cells, BMI increased response amplitudes to drifting gratings for each direction. For both directions, most points in the sample fell on or near the line of unity slope, indicating that the amplitude changes were well accounted for by the predictions. Interestingly, BMI caused a decrease in amplitude in three cells (Figs. 2A and 10A), which were predicted by the changes in S-T structure. In general, BMI had a similar effect on response amplitudes in individual cells to drifting and counterphasing gratings, increasing amplitudes to both stimuli in most cells, and decreasing it to both in the three cells. The stronger responses during BMI were expected given its action in reducing inhibition. The weaker responses in a few cells were surprising, and may reflect the action of BMI on complex neural networks (e.g., disinhibition of inhibitory neurons feeding back on the cell being studied).
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EFFECT OF BMI ON TIMING OF RESPONSE TO DRIFTING GRATINGS. The reduction in direction selectivity often was accompanied by a shift in the phase of response to the drifting grating (e.g., Fig. 2A). To assess whether these timing shifts also could be explained by changes in S-T structure, we performed the superposition analysis as above but focused on response phase. Figure 10, C and D, plots the change in predicted versus observed phase for the preferred and nonpreferred direction, respectively. For most layer 4 cells, BMI induced a response phase lag to drifting gratings; other cells underwent a slight phase lead. Importantly, most of these shifts were well predicted by the concomitant changes in S-T structure.
Although the BMI-induced changes in timings and amplitudes across the receptive field underlie the shifts in timing to moving stimuli, the causal relationships are not obvious from simple inspection of the static plots. Clearly, the response to a drifting grating reflects the convolution of the stimulus profile with the amplitude and temporal structure of the receptive field. For layer 4 simple cells, the superposition method captures essential aspects of these S-T interactions, revealing causal relationships between changes in S-T structure and changes to moving gratings.DOES BMI ALSO AFFECT DIRECTION SELECTIVITY IN LAYER 4 VIA CHANGES
IN THE STATIC NONLINEARITY?
Although S-T structure correlated with DI in control and BMI
conditions, linear predictions underestimated DI in most cells, indicating that nonlinear processes also operate in both conditions. In
layer 4 these processes can be modeled as a static nonlinearityan exponent
that follows linear summation (Albrecht and Geisler
1991
; Heeger 1993
; Murthy et al.
1998
). Here we asked whether reduction of inhibition altered
the exponent and, if so, whether the change contributed systematically
to decreased directional tuning.
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DISCUSSION |
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Numerous studies have shown that iontophoretic application of
bicuculline reduces direction selectivity (Sato et al.
1995; Sillito 1975
, 1977
; Sillito et al.
1980
; Tsumoto et al. 1979
; Wollman and
Palmer 1993
), thus demonstrating an important role for
intracortical inhibition in generating this perceptually important (Pasternak et al. 1985
) response property. However,
translating these results into an understanding of how the inhibition
operates has proved to be more difficult. Our study reveals that, at
least for layer 4 simple cells, inhibition sculpts the spatiotemporal structure of the receptive field, accentuating S-T orientation so as to
produce greater directional tuning. In terms of the linear-static nonlinear exponent model, inhibition appears to operate primarily at
the linear stage of spatiotemporal summation.
Here we discuss the results in light of the spatiotemporal model. We then consider the heterogeneity in the effect of BMI on direction selectivity and S-T structure, and its implications for excitatory mechanisms. Finally, we illustrate a connectional scheme that accounts for our observations. We focus on layer 4 because BMI effects there can be interpreted in the context of the exponent model, although we briefly consider the layer 6 results.
Understanding BMI effects in the context of the LN model
Two aspects of our results clearly show that inhibition
contributes to direction selectivity by altering S-T structure. First, BMI-induced reductions in selectivity nearly always were accompanied by
reductions in S-T orientation, and changes in the two parameters were
well correlated. Second, conventional linear predictions, based on
response timing and amplitude, accounted well for absolute changes in
the amplitude and phase (Fig. 10) of responses to drifting gratings.
The success of these predictions confirms other evidence (Albrecht and Geisler 1991; DeAngelis et al.
1993a
,b
; Humphrey and Saul 1998
;
Jagadeesh et al. 1997
; McLean et al.
1994
; Reid et al. 1991
) for linear
spatiotemporal summation as a critical mechanism of directional tuning
in simple cells. Also, it extends those population studies by showing
how S-T structure and direction selectivity covary in single cells.
Although changes in structure account for much of the reduction in DI, the STI and conventional linear predictions underestimate directional tuning in control and BMI conditions (Fig. 9, C and D). This is expected because, in both conditions, nonlinearities exist that amplify directional biases produced by linear spatiotemporal summation. Although this helps to explain discrepancies between predicted and observed DI, we still must ask why the absolute changes in amplitude to drifting gratings were well predicted by the linear model (Fig. 10, A and B) given the static nonlinearity. The answer may lie in the fact that, for most cells, BMI reduced nCG, our measure of the nonlinearity. Thus any change in amplitude to drifting gratings should have been well predicted by the linear model. In contrast to amplitude, the excellent predictions of response phase changes to drifting gratings are expected because response phase is not influenced by static nonlinearities.
Interpreting the weaker static nonlinearity
(nCG) during BMI application requires knowing
what the nonlinearity reflects biologically. We have modeled it as an
exponent but it may reflect a spike threshold or threshold plus
amplification. In practice, exponents and thresholds produce similar
effects: the accentuation of differences in cell discharge rates to
optimal versus nonoptimal stimuli (Albrecht and Geisler
1991; Tolhurst and Heeger 1997
).
Carandini and Ferster (1998)
recently provided support
for threshold and amplification as processes underlying the static
nonlinearity in simple cells. They measured the modulation of membrane
potentials and discharge rates to drifting gratings. Directional tuning
of the spikes was always greater than that of the potentials
(Jagadeesh et al. 1997
). The firing rates could be
accounted for by applying a simple threshold to the membrane potentials
followed by a linear gain.
These results suggest that the reduction in nCG
may be linked to changes in membrane potential relative to spike
threshold. Given that inhibition acts to keep a cell's membrane
potential below threshold (Berman et al. 1992;
Ferster and Jagadeesh 1992
), reducing inhibition by BMI
should increase the proportion of time the potential rises above
threshold. This will lead to increased firing rates in the preferred
and nonpreferred directions, a change that we observed in most cells
(Fig. 10). However, because the increase in the nonpreferred direction
is proportionately greater, due to its smaller control response,
direction selectivity will decrease. The BMI-induced reduction in
nCG thus may reflect an increase in membrane
potential relative to spike threshold.
Whereas the reductions in nCG help to account
for heightened amplitudes and decreased direction selectivity, the
reductions were not correlated with changes in DI, unlike the changes
in S-T orientation. This indicates that inhibition affects direction selectivity primarily by accentuating S-T orientation. Inhibition secondarily affects the static nonlinearity, probably by lowering membrane potentials relative to spike threshold so as to suppress responses in the nonpreferred direction (Movshon et al.
1978). An additional possibility, which our data cannot
address, is that inhibition also alters the gain of feedforward and
recurrent excitation (Suarez et al. 1995
).
The ability to dissociate the influence of BMI on linear versus static
nonlinear mechanisms rests on the assumption that the nonlinearity does
not affect response phase and hence does not alter S-T orientation.
This assumption is reasonable given our measure of timing: fundamental
response phase. For example, membrane potential fluctuations in
response to sinewave gratings are not always sinusoidal (cf. Figs.
7 and 8 in Jagadeesh et al. 1997). For a waveform that
deviates from a sinusoid, simple DC shifts in the waveform relative to
spike threshold might alter the resulting discharge profile and the
timing of some spikes. However, the phase of the fundamental response
would be affected little, as would be the S-T orientation. Only
temporal shifts of the whole profile would significantly alter
fundamental phase. Additionally, if a static nonlinearity did affect
response phase and S-T orientation, then BMI-induced reductions in STI
should correlate with the reductions in nCG, but
they did not (rs =
0.12). Thus it is unlikely
that changes in the static nonlinearity contributed to the reductions in S-T orientation. Those reductions likely reflect changes in spatiotemporal interactions among cells' inputs.
Heterogeneity in the effect of BMI on direction selectivity
The action of BMI on direction selectivity varied widely among
cells, from small reductions to complete loss. These results conflict
with those of Sillito et al. (1980), who reported that BMI eliminated direction selectivity in all simple cells studied. However, our results are in general agreement with those of
Tsumoto et al. (1979)
, Wollman and Palmer
(1993)
, and Sato et al. (1995)
, who also
reported a wide range of BMI effects on direction selectivity. Numerous
observations indicate that the heterogeneity here was not due to
methodological problems. First, potential variations between electrodes
in reliably ejecting BMI partly were controlled for by performing
titrations to assess the drug's ability to antagonize exogenously
applied GABA. Second, differential effects of BMI were observed even in
single penetrations. For example, in one track four cells were tested;
BMI minimally affected direction selectivity in the first cell but
virtually eliminated it in the last cell. Third, BMI significantly
increased the visually evoked firing rates of nearly all cells
relatively independent of the effect on direction selectivity,
indicating that it effectively reduced some level of inhibition.
Fourth, the strengths of BMI ejection currents were often less (~30
vs. >50 nA) for cells the direction selectivity of which was abolished
than for cells showing little effect. Fifth, for a number cells, after
collecting the main set of data we continued to iontophorese BMI for up
to 60 min and raised the current intensity as high as 200 nA to achieve maximal block. This nearly always resulted in oscillatory, bursty discharges unlinked to the visual stimulus followed by a silencing of
activity. At no time did these prolonged ejections produce any
reduction in directional tuning beyond that observed at lower currents.
Sixth, most inhibitory synapses are located on or near the soma
(Somogyi 1989
) and the concentrations of BMI used should have effectively blocked their action. This is particularly the case in
layer 4, where most cells are relatively small and compact (Martin and Whitteridge 1984
). Taken together, these
results indicate that genuine differences exist among cells in the
contribution of GABAA-mediated inhibition to direction
selectivity and, likewise, to S-T structure.
We did not attempt to manipulate GABAB-mediated inhibition.
However, Baumfalk and Albus (1988) reported that
iontophoresis of phaclophen, a GABAB antagonist, rarely
altered direction selectivity. In addition, intracellular blockade of
both chloride (GABAA) and potassium (GABAB)
channels causes a reduction but not an elimination of direction
selectivity (Nelson et al. 1994
). These, and the results
given here, indicate that the directional tuning and S-T orientation
remaining after blockade of inhibition reflects excitatory inputs onto
simple cells.
Sources of inhibitory and excitatory inputs to direction selective cells
Here we consider the nature and sources of inputs to
direction-selective cells in layer 4. The inhibitory inputs are clearly cortical in origin and most likely arise from other simple cells with
receptive fields that are spatially and temporally offset from their
targets (Maex and Orban 1996; Pollen and Ronner
1981
). This conclusion is supported by the observation that
reducing inhibition changed the temporal structure of layer 4 receptive fields. Complex cells lack spatiotemporally modulated responses, which
are necessary to produce this effect.
Excitatory inputs to cortical cells arise from the LGN and cortex. We
previously showed (Saul and Humphrey 1990) that lagged and nonlagged LGN cells (Mastronarde 1987
) convey to
cortex the range of timings needed to construct S-T oriented receptive
fields. The unique timing signatures of the two afferent groups are
observed readily in simple-cell receptive fields and can account for
the progression of timings across these fields (Saul and
Humphrey 1992a
). Also direction selectivity in many simple
cells varies with temporal frequency in a manner predicted by the
changing phase relationships between lagged and nonlagged cells as a
function of temporal frequency (Saul and Humphrey
1992b
). Further, Ferster et al. (1996)
reported
that cortical cooling, designed to suppress intracortical interactions,
did not reduce directional tuning in layer 4 cells, as measured from
membrane potentials in response to drifting gratings. These studies
thus indicate that geniculocortical inputs play an important excitatory
role in constructing direction-selective receptive fields.
The geniculate inputs likely contribute to S-T structure and
directional tuning in layer 4 both by their direct connections to
simple cells (Bullier and Henry 1979; Ferster and
Lindstrom 1983
; Martin and Whitteridge 1984
) and
by indirect connections via other cortical cells, some of which are
inhibitory. In this regard our results and those of others
(Sillito 1984
) appear to conflict with the conclusion of
Ferster et al. (1996)
that, at least at the membrane
potential level, inhibition is not necessary to produce directional
tuning. However, the discrepancy may be less than it appears. The
average DI in our layer 4 cells during BMI ejection was ~0.4. This is
on the high end of the DIs measured from membrane potential
fluctuations (Jagadeesh et al. 1997
). Perhaps the
residual selectivity in our cells reflects geniculate-based directional
biases of the membrane potentials. We would expect our DIs to be higher
than those observed intracellularly because spike thresholds still
affect the BMI data, accentuating directional biases. Nevertheless, we
also found that inhibition accentuates S-T orientation and simple
changes in spike threshold do not account for this. Therefore we
predict that the removal of inhibition by cortical cooling should
produce changes in S-T orientation that are observable at the membrane
potential level. Unfortunately, no data on S-T structure during cooling
exist to test this prediction. It remains to be determined whether the
BMI and cooling results are compatible with a common interpretation.
Figure 12 provides a simple
illustration, compatible with the present and previous (Saul and
Humphrey 1990, 1992a
) findings, of how an S-T well-oriented
receptive field may be produced by inputs from other simple cells
having certain spatiotemporal relationships. Simulated responses to a
counterphasing grating are shown for an excitatory (A) and
inhibitory (B) simple cell and their target (C).
Only the first half of the grating spatial cycle is illustrated. Although not shown, responses in Fig. 12A are produced by
rectified inputs from two LGN-like units
lagged and nonlagged
having
relative spatial and temporal offsets of 0.1 and 0.15 cycles,
respectively. Profiles in Fig. 12B reflect two other LGN
inputs with similar relative offsets. The cortical receptive fields
that result are each slightly S-T oriented (STIs = 0.23) and share
the same preferred direction of motion. Linear summation of these two
units (C) produces a receptive field with greater S-T
orientation (STI = 0.64) and hence stronger directional tuning. An
arctangent fit to the response phase versus spatial phase data is
shown in Fig. 12D (
). Removing the inhibitory input to
cell C would expose the excitatory structure, resulting in systematic shifts in the cell's response phase and a
reduction in S-T orientation (
), similar to that observed
experimentally (e.g., Fig. 5C). Note that receptive fields
receiving direct LGN input may be more or less S-T oriented than shown
here, depending on the range of response timings among the inputs.
|
This illustration does not address obvious complexities such as
spatially opponent inhibition, excitatory and inhibitory feedback at
all of the illustrated stages, and the large numbers of neurons that
must interact. These and other spatiotemporal interactions have been
modeled by others (Maex and Orban 1996; Suarez et
al. 1995
). Maex and Orban (1996)
have shown that
the S-T interactions can account for many stimulus-dependent behaviors
of direction-selective cells. Here we illustrate excitation and
inhibition as sharing the same preferred direction, but in principle
cross- and nondirectional inhibition could interact with excitatory
inputs to produce S-T-oriented structure. A key difference between our
model (Saul and Humphrey 1992a
) and that of Maex
and Orban (1996)
, however, is the source of the timings that
produce S-T orientation. Clearly, in the cat, the necessary range of
timing delays is present in the LGN relay cells (Saul and
Humphrey 1990
). Their existence precludes the need to create
timing delays in cortex using polysynaptic circuits, N-methyl-D-aspartate receptors (Maex and
Orban 1996
) and/or GABAB receptors (Suarez
et al. 1995
).
Directional mechanisms in layer 6
Unlike layer 4, direction-selective cells in layer 6 display weak
first-order S-T orientation, and even the addition of static nonlinearities does not account for their directional tuning
(Murthy et al. 1998). Similarly, BMI had no consistent
effect on the cells' first-order S-T structure despite reducing
direction selectivity. These results indicate that dynamic nonlinear
interactions predominate in layer 6. Such interactions are detectable
using two bars flashed sequentially across the receptive field
(Emerson and Citron 1992
). The second-order S-T oriented
structures revealed by this indicate that directional tuning reflects
nonlinear facilitatory and suppressive interactions in the preferred
and nonpreferred directions. An obvious prediction is that BMI should
lessen the suppression and reduce second-order S-T orientation.
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ACKNOWLEDGMENTS |
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We thank P. Baker for computer programming, M. Kieler for electronics support, and J. Feidler and A. Saul for helpful discussions. We are particularly grateful to Dr. Kaiqi Sun for instructing us in the manufacture and use of the iontophoresis arrays and for participating in the early experiments.
This research was supported by National Eye Institute Grant EY-06459 and a Core Grant for Vision Research (EY-08098) to the Eye and Ear Institute of Pittsburgh.
Present address of A. Murthy: Dept. of Psychology, 301 Wilson Hall, Vanderbilt University, Nashville, TN 37240.
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FOOTNOTES |
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Address for reprint requests: A. L. Humphrey, Dept. of Neurobiology, University of Pittsburgh, School of Medicine, E1440 Biomedical Science Tower, Pittsburgh, PA 15261.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
1 Response phases also were normalized to compensate for the difference between the spatial phases of the quadrature pairs. Taking spatial phases of 0 and 0.25 as the first or reference pair, we subtracted 0.0625 cycles from the second quadrature pair to simulate spatial alignment. Similarly, 0.125 and 0.1875 cycles were subtracted from the third and fourth pairs, respectively.
Received 1 July 1998; accepted in final form 24 November 1998.
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REFERENCES |
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