1Laboratorium voor Neuro- en Psychofysiologie, School of Medicine, Katholieke Universiteit Leuven, Campus Gasthuisberg, B-3000 Leuven, Belgium; 2School of Psychology, University of St. Andrews, St. Andrews KY16 9JU, Scotland, United Kingdom; and 3Kinder Klinik Magnetische Resonanz, CH 8032 Zurich, Switzerland
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ABSTRACT |
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Raiguel, S. E., D.-K. Xiao, V. L. Marcar, and G. A. Orban. Response Latency of Macaque Area MT/V5 Neurons and Its Relationship to Stimulus Parameters. J. Neurophysiol. 82: 1944-1956, 1999. A total of 310 MT/V5 single cells were tested in anesthetized, paralyzed macaque monkeys with moving random-dot stimuli. At optimum stimulus parameters, latencies ranged from 35 to 325 ms with a mean of 87 ± 45 (SD) ms. By examining the relationship between latency and response levels, stimulus parameters, and stimulus selectivities, we attempted to isolate the contributions of these factors to latency and to identify delays representing intervening synapses (circuitry) and signal processing (flow of information through that circuitry). First, the relationship between stimulus parameters and latency was investigated by varying stimulus speed and direction for individual cells. Resulting changes in latencies were explainable in terms of response levels corresponding to how closely the actual stimulus matched the preferred stimulus of the cell. Second, the relationship between stimulus selectivity and latency across the population of cells was examined using the optimum speed and direction of each neuron. A weak tendency for cells tuned for slow speeds to have longer latencies was explainable by lower response rates among slower-tuned neurons. In contrast, sharper direction tuning was significantly associated with short latencies even after taking response rate into account, (P = 0.002, ANCOVA). Accordingly, even the first 10 ms of the population response fully demonstrates direction tuning. A third study, which examined the relationship between antagonistic surrounds and latency, revealed a significant association between the strength of the surround and the latency that was independent of response levels (P < 0.002, ANCOVA). Neurons having strong surrounds exhibited latencies averaging 20 ms longer than those with little or no surround influence, suggesting that neurons with surrounds represent a later stage in processing with one or more intervening synapses. The laminar distribution of latencies closely followed the average surround antagonism in each layer, increasing with distance from input layer IV but precisely mirroring response levels, which were highest near the input layer and gradually decreased with distance from input layer IV. Layer II proved the exception with unexpectedly shorter latencies (P < 0.02, ANOVA) yet showing only modest response levels. The short latency and lack of strong direction tuning in layer II is consistent with input from the superior colliculus. Finally, experiments with static stimuli showed that latency does not vary with response rate for such stimuli, suggesting a fundamentally different mode of processing than that for a moving stimulus.
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INTRODUCTION |
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Although the primate brain analyzes incoming
visual information with surprising rapidity, there is nonetheless a
finite delay, or latency, between the time that a visual stimulus
appears on the retina and the time that a neuron in the visual system
begins to spike in response to that stimulus. This delay arises from a
number of factors, including photoreceptor transduction, neural conduction time, synaptic delay, and spike integration time, and tends
to increase in higher cortical areas with each successive stage adding
its processing time before passing the signal along to the next, higher
area (Nowak et al. 1995; Raiguel et al.
1989
; Schmolesky et al. 1998
; Vogels and
Orban 1994
). Although it is perhaps not surprising that
latencies can vary considerably among individual neurons within a
visual area given the number of paths by which information can arrive,
the breadth of that range can be remarkably wide (for review, see
Nowak and Bullier 1997
), far exceeding the average
differences between hierarchically adjacent visual areas. Area
MT/V5 is no exception, and the range of latencies measured for
individual neurons there easily exceeds 100 ms (Maunsell 1987
; Raiguel et al. 1989
).
The range of latencies in MT/V5 is perhaps more striking insofar as
this area receives a restricted neural input almost entirely magnocellular in origin (Maunsell et al. 1990) and
comprises but a single retinotopic map composed of a relatively
homogeneous population of neurons giving directionally selective
responses to translational motion. (Desimone and Ungerleider
1986
; Maunsell and Van Essen 1983a
). Other
factors must therefore account for the wide range of latencies observed
in this cortical area: first, although the input may be predominantly
magnocellular, that contribution can arise from primary visual cortex
by either direct afferents from V1 or indirectly through V2
(Ship and Zeki 1989a
,b
). Other pathways bypass striate
cortex completely (ffytche et al. 1995
), either passing
through the superior colliculus and pulvinar (Standage and
Benevento 1983
; Ungerleider et al. 1984
) or
using direct connections with the LGN (Fries 1981
;
Yukie and Iwai 1981
). Reciprocal connections with areas
V3 and V4 also have been described (Maunsell and Van Essen
1983b
). Second, the axons within a pathway may consist of subtypes that vary considerably in diameter (Rockland
1995
), thus affecting the conduction times. Finally, in any
given cortical area, interlaminar conduction and signal processing will
delay further the appearance of spikes in the neurons furthest removed from the arborizations of afferent axons. In both cat (Best et al.1986
) and monkey (Maunsell and Gibson 1992
)
primary visual cortex, the average latency is highest in the deep and
superficial layers lying most distant from the input in layer IV.
Although it is probable that MT/V5 follows this same general pattern,
few studies have examined latency in MT/V5, and none have attempted to
identify the specific factors giving rise to those latencies.
The extent to which signal processing and feature extraction produce
delays in spike activity has not been established, and there are in
fact two distinct issues here: the first is the degree to which the
latency of a single neuron varies when one or more stimulus parameters
are varied, the second involves the relationship between stimulus
selectivities and latencies over a population of cells stimulated with
their optimum stimuli. There is little question that the latency of any
given neuron is affected by stimulus parameters such as orientation
(Celebrini et al. 1993; Gawne et al.
1996
), contrast (Gawne et al. 1996
;
Maunsell and Gibson 1992
), size (Boltz et
al.1982
), speed (Lagae et al. 1994
;
Lisberger and Movshon 1999
), and luminance (Boltz
et al.1982
; Maunsell et al. 1999
). Stimulus
specificity, by definition, affects response rates, and much of the
effect of stimulus parameters on individual latencies may simply be due
to the lower responses elicited by nonoptimal stimuli (Boltz et
al.1982
; Maunsell and Gibson 1992
). In this case, stimulus-latency dependencies reflect information flow through the circuitry underlying the selectivity under investigation and cannot
address the larger question of the number of synapses, i.e., the
circuitry itself, that may be involved in generating that selectivity,
or the delay that such processing entails. At the population level, it
appears obvious that the creation and elaboration of stimulus
selectivities should require increasingly complex circuitry, yet a
higher degree of stimulus selectivity is not invariably reflected in
longer average latencies. On one hand, Nowak et al.
(1995)
have found that color- and orientation-selective cells
in V2 have significantly longer latencies than nonselective cells, yet
both those investigators and Celebrini et al. (1993)
have reported that V1 cells with longer latencies have no more tendency
to be orientation selective than those with shorter latencies. The
degree to which latency is associated with stimulus selectivity and
tuning therefore may vary according to both the type of selectivity and
the cortical area where the processing takes place.
An association between latency and processing is suggested by the
structure of the cortex itself. The tendency for longer latencies in
cortical layers at greater removes from the input layers appears to
reflect an increasing complexity in the circuitry, and cells in layers
most distant from IV are indeed more likely to receive polysynaptic
input (see Gilbert 1983 for review). The presence of
these additional synapses will necessarily produce longer signal
delays, as will conduction time over cell processes and any reductions
in the signal strength that may be imposed if a significant fraction of
the synapses in these circuits are inhibitory in nature. Ringach
et al. (1997)
have presented evidence that orientation
selectivities in the output layers II-IVb and V-VI of area V1 have
sharper tunings and more complex orientation properties than those in
the input layers 4C
and 4C
, implying an evolution of neuronal
properties that parallels the observed increase in latency from layer
to layer. Area MT/V5 demonstrates an analogous elaboration of
receptive-field properties in the sense that neurons lying in the input
layer more often have weak or nonexistent antagonistic surrounds
(Born and Tootell 1992
; Lagae et al.
1989
; Raiguel et al. 1995
). We have speculated
(Raiguel et al. 1995
) that MT/V5 neurons with
antagonistic surrounds probably represent a later stage in processing
than nonsurround neurons, suggesting that there should be a consistent
relationship between latency and surround quite apart from any
response-rate-related differences imposed by the surround inhibition.
To demonstrate this, however, requires that the two sources of response
latency be distinguishable.
One way to identify response latency not associated with response rate
is to scrutinize the relationship between response and latency over the
entire range of responses using the optimal stimulus for each cell. If
the observed range of latencies is due solely to differences in
response rates, there will be a single, consistent relationship between
the two. Intrinsic effects, such as those due to differences in the
neural circuitry, in contrast will depart from that relationship,
depending on the type or degree of selectivity shown by a given neuron.
The stimulus parameters we selected for this purpose were speed and
direction, selectivity for which is well established in area MT/V5
(Lagae et al. 1993; Maunsell and Van Essen
1983a
; Tanaka et al. 1986
; Zeki
1974
). The third property included in this investigation was
the surround antagonism associated with MT/V5 neurons (Allman et
al. 1985
; Raiguel et al. 1995
; Tanaka et
al. 1986
). Because this property differs from speed and
direction selectivity in that it involves influence from outside the
classical receptive field, it may represent a fundamentally different
neural mechanism from speed and direction selectivity. The
well-established laminar pattern of surround inhibition also provides a
neuronal property for which the laminar disposition could be compared
directly with the latencies observed in those layers. Such
receptive-field properties, whether surround properties or speed and
direction tuning, bear on the relationship between latency and
circuitry across populations of cells and thus were examined by testing
with the optimum stimulus of each cell. The secondary issue, concerning
the relationship between latency and the degree to which a stimulus
matches the optimal stimulus in single cells, similarly can be
addressed by examining the response-latency relationships resulting
when nonoptimal stimuli are also tested. By measuring latencies in a
large number of MT/V5 neurons over a range of responses generated by
both optimal and nonoptimal stimuli, we have attempted to determine in
what way stimulus selectivity contributes to latency and how much of
this may be effected through the relatively trivial mechanism of
response level.
The intent of this study, then, was to investigate the extent to which the latency of MT/V5 neurons is associated with the evolution of specific receptive-field properties by examining the relationships between latency and various neuronal attributes, including stimulus selectivity, laminar distribution, and response rates. Once the sources of latencies are understood, then the delay between stimulus onset and the appearance of the response becomes a clue to the nature of the neural machinery involved in the visual process.
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METHODS |
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The basic animal preparation, experimental, and testing
procedures employed in this study are described in greater detail in
previous reports analyzing other aspects of the present test results
(Raiguel et al. 1995, Xiao et al. 1997
,
1998
). Single-unit extracellular recordings were made in area
MT/V5 of 22 anesthetized (sufentanyl; Sufenta Forte, 5 µg · kg
1 · h
1) and
paralyzed (pancuronium bromide; Pavulon, 0.4 mg · kg
1 · h
1 ) male
macaque monkeys (Macaca fascicularis) weighing between 3.2 and 5.4 kg.
Visual stimuli were circular patches of moving random dots consisting of white (48 cd/m2) dots on a dark (0.2 cd/m2) background moving coherently in the frontoparallel plane. Dots measured 0.35° in diameter with a density of 2.5 dots per square degree at the testing distance of 0.57 m. All stimuli were preconfigured and stored as sequences of 512 × 512 images on a Microvax II Workstation. Image sequences were displayed at 100 Hz using a Gould IP 9545 image computer and presented in pseudorandom order. Random dots filled the entire 25.6 × 25.6° area of the monitor at all times, but only the dots within the stimulus itself moved during presentations. Because the random dots already were present over the receptive field when motion began, motion onset coincided with the appearance of the first frame of the motion sequence.
Penetrations were made in the parasagittal plane between the superior
temporal and lunate sulci, 13-17 mm lateral to the midline and at an
angle of 25-30° from the vertical, pointing slightly rostrally and
parallel to the superior temporal sulcus. Magnetic resonance imaging
(MRI) images of individual brains generally were used to facilitate
planning the penetrations. Electrolytic lesions made during the course
of each penetration aided reconstruction of the electrode path and in
the identification of the cortical area and layer of the recorded
neurons in Myelin- and Nissl-stained sections. MT/V5 was identified on
these sections by the extent of the heavily myelinated region
(Ungerleider and Desimone 1986; Van Essen et al.
1981
) and was readily identifiable during the experiment by the
high proportion of directionally selective cells and the retinotopic
organization of the receptive fields (RFs). Cortical layers were
defined according to Garey (1979)
, but with layer III
arbitrarily subdivided into three sublaminae, IIIa, b, and c, of equal
thickness (Raiguel et al. 1995
). Cells were stimulated
monocularly, using the eye giving the stronger response. Spikes were
recorded over a total of 1050 ms per presentation, including 250 ms
before the onset of stimulus motion, the 300 ms of stimulus movement,
and 500 ms after the stimulus had stopped. On-line analysis of
responses provided feedback during the experiment.
Two quantitative tests were employed in this investigation. First the
influence of the direction and speed of stimulus motion was examined
using the direction test, which consisted of 48 stimulus conditions comprising 16 directions from 0 to 357.5° and three speeds
of 5, 20, and 40°/s. The size of the stimulus used in this test was
selected on the basis of the handplot. After the optimum speed and
direction had been determined with this direction test, a
two-dimensional position test (Lagae et al. 1994;
Raiguel et al. 1995
) was used to precisely center the
stimulus display over the center of the RF before proceeding to the
summation test that followed.
The second quantitative test, the summation test, examined the relationship between stimulus size and response and determined the presence and strength of any antagonistic surround. This test presented 11 concentric, circular stimuli centered on the RF and presented at the optimum speed and direction of motion for the neuron. These stimuli encompassed a range from 1.6 up to 25.6° in diameter, sufficient to cover the entire center and surround. A decrease in response as the stimulus size increased beyond a given, optimum diameter indicated the presence of an antagonistic surround. The amount of this decrease at the largest stimulus size, expressed as a percent of the maximum response, was used as a measure of the strength of that surround.
A subset (n = 66) of the cells was also tested using static gratings and edges. Contrast and luminance of these stimuli were identical to those in the motion tests and consisted of luminance edges and square-wave gratings with frequencies of 1, 0.5, 0.25, and 0.16 visual degrees (at 57 cm) presented at all orientations, encompassing a full 180° at 22.5° intervals. The stimuli were flashed onto a uniform screen of equal mean luminance following the same presentation pattern as that used for motion stimuli: a 300-ms presentation time with 750 ms between presentations. The stimulus giving the strongest response was selected for comparison with the motion response.
For statistical comparisons of responses, the response evoked by a
given stimulus condition was defined as the average discharge rate
during all presentations over a time period equal to the stimulus in
duration but beginning at 50 ms after the stimulus onset. Spike data
were analyzed as cumulative peristimulus time histograms (PSTHs) with
10-ms bins. Preliminary data analysis using a series of binwidths from
5 to 25 ms showed that the choice of binwidth had no effect on the
resulting latency measurements, confirming what others have found
(Nowak et al. 1995). Latencies were determined using
cumulative sum analysis (Ellaway 1978
), applying
statistical criteria similar to those of Maunsell and Gibson
(1992)
and Vogels and Orban (1994)
to identify
response onset. First the mean and standard deviation of the
spontaneous spike rate was determined from the 150-ms periods preceding
stimulus onset in all runs, then the onset of the response was defined as the first bin after motion or flashed stimulus onset where the bin
exceeded the spontaneous discharge rate by two standard deviations and
which was followed by at least two successively increasing bins. To
examine responses across the entire cell population for a given test, a
population PSTH was created by combining the histograms of the
individual neurons. To do so, each histogram first was normalized by
setting the highest bin of the optimum condition of a test equal to 1, thus equalizing the contributions of cells with high and low firing rates.
The optimum speed of a neuron was simply the speed giving the strongest
response. The preferred direction at a given speed was defined as the
vector sum of the responses in all directions tested rounded to the
nearest of the 16 directions. The sharpness of the tuning was expressed
as the selectivity index (SI), as defined by Vogels and Orban
(1994)
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RESULTS |
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Our sample consisted of 310 MT/V5 neurons successfully tested with
the direction test, ranging in eccentricity from 0.8 to 38° with a
mean of 12.6° and giving a mean response of 43 spikes/s at optimum
speed and direction. All cells included in this study had a optimum
response of 10 spikes/s. Most (222) of these neurons also were tested
with the summation test. The data in the present sample largely overlap
with the 237 summation tests investigated in Raiguel et al.
(1995)
, but data from the earliest tests (71 neurons), which
used a different data format and testing sequence were not included
here, whereas data from three subsequently recorded animals were added
(56 neurons).
Relationship of speed and direction tuning to response latency across the population
Most of the cells tested were tuned for a given direction of motion, with a mean SI at optimum speed of 0.49 ± 0.26 (mean ± SD). Of the 310 cells tested, 65 gave their strongest response at 5°/s, 109 at 20°/s, and 136 at 40°/s. The latencies of responses to the optimum speed and direction of each neuron ranged from 35 to 325 ms, with a mean of 87 ± 45. The distribution of these latencies is shown in Fig. 1. Although the distribution is nearly symmetrical, the range is narrower than would be expected of a true normal distribution (P < 0.01, Lilliefors test for normality).
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There was no great variation in the response latencies among cells of
different speed tunings, although the mean latency, at optimum
stimulus, for neurons tuned to the slowest speed, 90 ± 40 (SD) ms
was slightly longer than those tuned for medium (86 ± 45 ms) and
fast speeds (86 ± 42 ms). Because response latency can depend on
response strength, however (Boltz et al. 1982;
Celebrini et al. 1993
), it is important to assess the
contribution that varying response levels may have to any observed
differences in latencies. It is possible to distinguish between more
meaningful differences among classes and those due to systematic
variations in firing rates by examining plots of latency as a function
of response rate. Any differences among classes in the curves
describing that relationship indicates a specific effect of the speed
preference on latency.
Figure 2A plots the log
latency as a function of log response strength for all cells at the
optimum speed and direction. Linear regression lines fitted to the
log-transformed data for the three speed tuning classes illustrate that
the relationship remains constant across these groups (slopes:
F2, 500 =0.016, P 1;
intercepts, F2, 500 = 0.18, P > 0.5, ANCOVA), suggesting that the longer latencies observed in
slow-tuned neurons are simply due to the generally lower response
levels shown by the cells of this group.
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As one might expect, the preferred angle of the direction tuning bore
no relationship to the latency of a neuron (P > 0.6, ANOVA) nor was it related in any way to the firing rate
(P > 0.8, ANOVA). However, there was a very
significant (P < 106, ANOVA)
inverse relationship between the width of direction tuning, as
quantified by the SI and latency (Fig. 2B). Although this is to some extent explainable by a tendency (P < 0.002, ANOVA) for higher response rates in more sharply tuned neurons, the
relationship between SI and latency remains strongly significant
(P < 4*10
5, ANCOVA) even if
the response is considered as a cofactor. The implication is that if
the most sharply tuned neurons are those that respond most quickly,
then the population tuning should broaden somewhat over time as less
sharply tuned neurons begin to contribute. Figure
3A shows that this is indeed
the case and that within the period between the first appearance of
spike activity in the population response, at 40 ms, and the point
where the maximum response rate is reached, ~80 ms, the average
tuning curve becomes visibly wider. Although at least some of the
broadening may simply be due to the weak nature of the initial portion
of the responses, like the tip of an iceberg, it is obvious that the
population response is sharply tuned for direction from its very onset.
Figure 3B illustrates, for the MT/V5 population, the
relationship between the evolution of direction tuning, quantified by
the SI, and spike activity, here scaled so that their maxima are
comparable. The SI depends on response rate and therefore rises over
time: However, it can be seen that the rapid rise in SI precedes the
rise in spike activity by some 10-20 ms, indicating that, initially,
the SI is determined primarily by the narrow width of the tuning, but
that the strength of the response gradually becomes the dominant factor. Thus the very first few spikes to appear are, in this sense,
the most narrowly direction tuned.
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Because direction selectivity in the two directions of the preferred
axis of motion is a subset of direction tuning, it is unsurprising that
direction selectivity expressed as the DI also was related to response
latency (P < 106, ANOVA). The
trend (not shown) followed that of the SI, with higher latencies
associated with lower DIs, but because most MT/V5 cells were generally
very directionally selective, the data were much less evenly
distributed, with the majority of the DIs falling into the 80-100 range.
A consistent (P = 0.01, ANOVA) relationship was found
between eccentricity and latency. Neurons the receptive fields of which were near the fovea had latencies averaging almost 20 ms longer than
those located more peripherally. Closer inspection, however, reveals
that at least part of this effect is explainable in terms of response
levels, and when this covariate is taken into consideration, the
relationship is no longer significant (P = 0.08).
Because peripheral receptive fields tend to be tuned for higher speeds (Lagae et al.1993), they simply reflect the overall
tendency, discussed in the following text, for higher response rates in "fast" cells. Previous work using moving light and dark bars also has reported higher average response rates in peripheral neurons (Lagae et al.1993
).
Effect of relative stimulus speed and direction on response latency within individual neurons
Speeds slower or faster than a given neuron's optimum simply
produced longer average latencies (+4 and +5 ms, respectively), commensurate with the weaker responses, as did motion that was nonoptimum in direction (e.g., +3 and +6 ms for deviations of 22.5 and
45° from the preferred axes of motion, respectively). Others
(Lagae et al. 1994; Lisberger and Movshon
1999
) who have tested over wider ranges of stimulus speed,
2-50 and 0.5-100°/s, respectively, have found differences of
30
and 100 ms in manually measured latencies at the two extremes. However,
Kawano et al. (1994)
found that speed had a much more
modest effect (<10 ms) on latencies of individual neurons in area MST
despite the fact that MST receives direct input from MT/V5. Although
the effect of the factor stimulus speed was strongly
significant (P < 0.007, ANOVA), slopes of regression
lines describing response versus latency for the two nonoptimum speeds
were statistically indistinguishable from the optimum
(P > 0.5, ANCOVA) and indicate no variation in the
latency with stimulus speed that cannot be accounted for by differences
in the response strength. Direction of motion produced an even stronger
effect on latency (P < 10
6,
ANOVA; Fig. 4); but once again, this is
an obvious consequence of stimulus tuning, and if the contribution of
response strength is removed as a cofactor, the main effect, relative
stimulus direction, is no longer significant (P = 0.06, ANCOVA).
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Latencies within the sublaminae of area MT/V5
We also examined the response latencies in individual laminae of
MT/V5 cortex. Because cells in layers most distant from IV receive
largely polysynaptic input (Gilbert 1983), it is logical to assume that such polysynaptic pathways would be associated with both
more sophisticated receptive-field properties and longer signal delays.
Of our original sample of 310 neurons tested with the direction test,
we had lamination data for 279. Of these neurons, 12 were found in
layer II, 24 in IIIa, 36 in IIIb, 71 in IIIc, 75 in IV, 46 in V, and 15 in VI. The difficulty of finding and holding cells in the most
superficial layers resulted in relatively low numbers of cells being
recorded in layer II, and deeper layers were not always reached, hence
the central laminae tend to be somewhat overrepresented in this sample.
The latencies in MT/V5 did indeed show a distinctive laminar pattern. As one might predict on the basis of synaptic connection patterns, the overall tendency was for higher average latencies at increasing displacements from the input region around layer IV, such that lamina IIIa lags IV >40 ms. However, layer II constituted an exception to this trend, showing a remarkably short average latency (Fig. 5A) that was statistically distinct from the adjacent layer, IIIa, at the 0.02 level (ANOVA). The uppermost lamina in fact proved distinctive with regard to a number of properties, although any conclusions must be tempered somewhat in consideration of the small size of the sample.
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The next logical question concerns the origin of latency differences
across layers. Is it a product of lower response rates or is there an
intrinsic delay imposed by additional processing and conduction times
at greater removes from the input? Response level can explain some of
the effect of the variable layer because statistical
significance falls from P < 106 to P < 0.002 (ANCOVA) when
response level is taken as a cofactor; but the effect is still quite
significant. If the average response is plotted per layer, we see that
the pattern is virtually the inverse of that shown by the latency and
that responses tend to decrease in strength with increasing
displacement from the input layers. Again, the uppermost lamina
constitutes the exception to the overall trend with a firing rate, no
higher than that in adjacent layer IIIa, that fails to mirror the much
lower latency, implying that factors other than spike rates are
responsible for the anomalous latency. The population PSTHs of cells
tested in layers II, IIIa, and IV (n = 9, 24, and 51;
earliest data were not recorded in a format accessible to histogram
analysis) are compared in Fig. 5B. Because the histograms
are normalized, the influence of response rate on latency is largely
obscured so that onsets in layers IIIa and IV become indistinguishable,
yet a delay on the order of 10-20 ms persists between the population
responses of lamina IIIa and II.
If much of the activity in layer II does indeed arise from direct
subcortical input, then one consequence should be a reduced directional
selectivity in both the sense of a broader directional tuning width and in the sense of directional
selectivity along the preferred axis of motion because both
properties are weak to nonexistent in the pulvinar and colliculus
(Bender 1983; Goldberg and Wurtz 1972
,
Schiller 1972
). We found that direction tuning, as
quantified by the SI, is indeed lowest in layer II (P < 0.02, layer II vs. all others, ANOVA; Fig.
6A). This pattern is echoed to
a certain extent by the optimum-axis direction selectivity, but laminar
trends are rather less consistent (Fig. 6A). Earlier experiments that measured DIs using a single small stimulus placed in
the most responsive part of the receptive field gave values that were
higher and varied less from layer to layer but nonetheless showed a
slight dip in average DI in layer II (Raiguel et al. 1995
). A plot of the SI evolution over time, compiled from the averaged responses of layer II neurons (Fig. 6B), confirms
that the SI reaches a maximum level only about half that of all layers combined (see Fig. 3B), but that it begins to rise at least
as early as that of the remaining layers, reaching a comparatively low
peak at ~70 ms. A comparison with the curve for MT/V5 as a whole
(Fig. 3B)shows that this peak occurs
30 ms earlier in
layer II. The population response histogram (Fig. 6B)
follows a similarly early onset, with a transient component that rises
to a peak at 70 ms, then quickly falls to about half its maximum value
by 140 ms. This response is consistent with the sort of transient spike activity in MT/V5 that remains after a V1 lesion and apparently arises
from collicular input (Rodman et al. 1989
, 1990
).
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In view of reports that the signals that arrive at MT/V5 via
pathways bypassing V1 are generated preferentially by faster motion
(ffytche et al. 1995), we compared the speed tunings of layer II cells with those of other layers. We found no evidence that
the short-latency neurons in layer II of MT/V5 respond preferentially to faster stimuli. The proportions of cells preferring fast, medium, or
slow speeds (25, 33, and 42%) are about equal to those from the
remainder of the sample (21, 34, and 45%).
Antagonistic surround and response latency
One of the well-known properties of area MT/V5 neurons is the
presence of antagonistic surrounds associated with most of their receptive fields (Allman et al. 1985; Raiguel et
al.1995
; Tanaka et al. 1986
). It seems likely
that a neuron possessing an antagonistic surround also would display a
longer latency because we have speculated that surround cells represent
a later stage in motion processing than nonsurround cells
(Raiguel et al.1995
); thus entailing a greater number of
synapses between the retinal input and the cell in question. To
investigate the possible relationship between surround and latency, we
first compared the latencies, as measured at optimal size in the
summation test, within the two extremes of the sample: neurons in which
surround antagonism produced no more than 15% inhibition at the
largest stimulus (n = 22) and those in which the
response was completely inhibited (n = 48) by the
largest stimulus. It should be emphasized that data presented here were
obtained at optimum stimulus size and that the relationship among
stimulus size, surround inhibition, and latency is an additional topic
that will not be taken up in the present report. The distributions of
the latencies, shown in Fig. 7, clearly
are shifted (P = 0.01, ANOVA) with respect to one
another, with means of 66 ± 24 and 87 ± 26 ms for the low-
and high-inhibition cells, respectively. Once again, however,
differences in latency appear to reflect overall response levels in the
two groups because the strong-surround group has a mean response rate
at optimum of 32 spikes/s, whereas the group with little or no surround
antagonism has a median response rate of 50 spikes/s.
|
Can attenuation of the response by the surround antagonism completely account for the observed differences in response latency between neurons with different levels of surround antagonism, however? To address this question, we must again examine the relationship between response and latency, this time at different levels of surround antagonism. If factors other than response rates come into play, then this relationship may be expected to differ depending on the level of surround influence. For this purpose, the sample was divided into four categories from 0 to 100% inhibition in 25% increments. Scatterplots were prepared of the log response versus log latency, and linear regressions were calculated on the log-transformed data for each of the four categories. These regression lines are depicted in Fig. 8. Statistical analysis of these regressions (ANCOVA) showed that although the slopes of the relationships were not statistically distinguishable (P > 0.5), the intercepts were significantly different (0.001<P < 0.002) and that neurons with higher levels of surround antagonism tended to have inherently longer latencies that cannot be completely attributed to the lower responses in those cells. The difference corresponds to an average increase of ~15 ms in the latencies of neurons with the strongest antagonistic surrounds (75-100% suppression) over the next-highest class (50-75% suppression).
|
Laminar effects of surround on latency
As we and others (Born and Tootell 1992,
Lagae et al. 1989
; Raiguel et al. 1995
;
Tanaka et al. 1986
) have reported previously, there is a
marked variation in the average level of surround antagonism from layer
to layer. Figure 9 summarizes the
relationships among latency, response, and surround antagonism. The
latency and response-level patterns across the cortical thickness
reiterate those of Fig. 5A, substantiating the virtually
identical results obtained in the direction tests. This figure also
emphasizes the relationship that exists between the average latency in
a given layer and the corresponding surround inhibition, which follow
almost identical patterns: low around the input layers and higher in
the infragranular and supragranular layers, with the exception of II,
where latency and inhibition are again rather low. In this uppermost
layer, however, the expected concomitant rise in response rates does not occur.
|
Statistical analysis shows that the effect of the laminar position is indeed a significant factor (P < 0.02, ANOVA) with respect to latency. If the surround inhibition is considered as a cofactor, however, then the effect of laminar position is no longer significant (P < 0.20) nor is it significant if response is considered as a cofactor (P < 0.30). This suggests that laminar effects are largely a consequence of response levels, which in turn are the result of varying levels of surround antagonism in the different layers. This idea receives some support from the finding that the inhibition class (Fig. 8) significantly affects response levels (P < 0.01, ANOVA) and implying that the strength of the inhibitory surround somehow remains a factor in determining the response level, despite the use of optimal-sized stimuli in the testing procedure.
Comparison of responses with static and moving stimuli
Because our comparisons of latencies assume a consistent
relationship between response strength and latency, it is logical to
wonder how general this relationship might be and whether the same
relationship might hold for a completely different sort of stimulus,
e.g., a flashed, static grating or edge. Because many experiments often
are performed on the same units, 66 of the earliest neurons in our data
set also had been tested using static stimuli, and 56 of these gave
measurable responses to one or more of the static stimuli. Responses to
the optimum static stimuli produced average latencies some 5 ms shorter
(P < 3*106, paired
t-test) than those to moving stimuli. No discernable relationship was found between the latencies as determined with the two
types of stimuli (R2 < 10
2). Although our initial assumption had been
that the relationship between response and latency was a universal,
Poisson phenomenon, we were surprised to learn that the latency for the
flashed stimulus is more or less constant, with a log-log slope of only
0.014 (Fig. 10) compared with the
corresponding slope for the motion stimulus of
0.26
(F2,100 = 4.8, 0.01<P < 0.02, ANCOVA).
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DISCUSSION |
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Significance of latencies and sources of variation
The latency of neuronal responses measured in the MT/V5 population
varied with the evoked discharge rate in a relationship that remained
consistent over a wide range of response rates for a given stimulus
type (Figs. 2 and 8), as has been observed in retinal ganglion cells
(Boltz et al. 1982). It has been emphasized that
there is no single value, however determined, that can adequately represent the absolute latency of a neuron (Nowak
and Bullier 1997
). This makes comparisons between studies
difficult, yet comparisons of latencies across stimulus parameters
within a study nonetheless can be meaningful for a given set of
criteria. Indeed, it should be pointed out that latency-response
relationships in general may be highly stimulus-specific, as our
comparison of responses to flashed and moving stimuli (see last section
of DISCUSSION) would indicate.
The average latency of 87 ms in the present study is in good agreement
with the 94 ms previously reported in MT/V5 using moving bars
(Raiguel et al. 1989), but longer than the 72 ms
recently found (Schmolesky et al. 1998
) using flashed
bars which typically produce transient responses with shorter rise
times and shorter latencies (Maunsell 1987
; Nowak
and Bullier 1997
). Although ranges (10-90th percentile) of
100 ms are common for extrastriate areas (see Nowak and
Bullier 1997
for review), a narrower range might be expected
for area MT/V5, considering its restricted input (Maunsell et
al. 1990
, Movshon and Newsome 1996
; Shipp
and Zeki 1989a
,b
). The range of latencies in our experiments,
only 80 ms if expressed as the 10-90th percentile range, suggests area
MT/V5 indeed lies toward the lower end of the spectrum for extrastriate
cortex. The existence of a homogeneous input eliminates at least one
source of variability (Nowak and Bullier 1997
), making
MT/V5 ideal for investigating the remaining variables that are
associated with the polysynaptic nature of the signal processing
itself, such as synaptic delays, integration time, and feedback from
other cortical areas.
One obvious source of variation in the observed latency is certainly
the number of routes by which input may reach MT/V5. Although the small
number of cells recorded in the most superficial layers precludes any
definitive conclusions, all the properties of these neurons that were
investigated, including short latencies (Finlay et al.
1976), laminar position (Benevento and Rezek
1976
), and direction selectivity (Bender 1983
;
Goldberg and Wurtz 1972
; Schiller 1972
)
are consistent with a collicular input. Moreover, these distinctions
achieve statistical significance despite the small numbers of recorded
cells. Lesion studies (Rodman et al. 1989
, 1990
) have
confirmed that area MT/V5 receives a fairly substantial input from the
superior colliculus, implying a high probability of encountering
neurons receiving such input. Collicular neurons are also poorly tuned
for the axis of motion, and we found that directional tuning for a
particular axis of motion is correspondingly weakest in layer II.
However, the responses that remain after striate lesions (Rodman
et al. 1989
) or reversible inactivation (Girard et al.
1992
) retain much of their direction tuning, leading to
speculation that this selectivity might be generated, or at least
refined, within MT/V5 itself (Girard et al.,1992
;
Gross 1991
; Rodman et al. 1989
,
1990
). Although the early onset of direction tuning in layer II
neurons (Fig. 6B) suggests that there is some degree of
direction selectivity already present in the input, this is nonetheless
much weaker than in deeper layers, the neurons of which may well act to
sharpen that tuning.
There are other sources of rapid-onset neurons in MT/V5, and the vast
majority of early spike activity outside layer II probably arrives via
more conventional intercortical pathways. One such source of
short-latency input may be the direct afferents from V1 having large
fibers and boutons (Rockland 1989) and axonal conduction
times on the order of
2 ms (Movshon and Newsome
1996
). This input appears to be confined to layers 3, 4, and 6 (Rockland 1989
) and thus could not account for
the short-latency spike activity observed in the layer II, but the
extensive arbors of these axons may provide the basis for early spike
activity in the deep and middle layers.
Visually evoked potentials measured in human subjects have suggested
that fast-moving stimuli (>22°s) activate MT/V5 first (ffytche et al. 1995), whereas slow-moving stimuli
(<6°/s) initially activate V1. Those investigators concluded that
slow stimuli are processed by a pathway that includes V1, whereas
faster stimuli use a separate pathway bypassing V1, implying that cells
with faster tunings in MT/V5 should have shorter latencies. Our failure to find faster speed tunings associated with layer II neurons suggests
that the short-latency input into this layer that we observe cannot be
this proposed pathway. Although we did find an association between
shorter latencies and faster speed tunings in MT/V5 as a whole, the
shorter latencies appeared to be explainable on the basis of response
strengths. Because the majority of cells in MT/V5 respond best to
faster speeds (Kawano et al. 1994
; Lagae et al.
1993
), faster motion will produce higher response rates with
correspondingly shorter latencies. V1, with a high proportion of cells
tuned for speeds <10°/s (Orban et al. 1986
),
would respond poorly at faster speeds while responding to slow stimuli
vigorously and with correspondingly short latencies. Moreover, the
layers in V1 that project to MT/V5 have been found to contain few
low-pass cells (Orban et al. 1986
), so that slow stimuli
presumably would elicit only modest responses, with longer latencies,
in area MT/V5. Thus the results reported by ffytche et al.
(1995)
are also explainable on the basis of the speed-response
curves of V1 and MT/V5 without the necessity of evoking separate pathways.
Antagonistic surround and latency
The existence of a relationship between the level of surround
inhibition expressed by a neuron and the latency of its response might
not be unexpected because the former, by definition, sharply affects
response. However, the data presented here were measured using stimuli
of optimum diameter that presumably include little or none of the
surround. Moreover, even if there was overlap between center and
surround regions, such that the surround exerted an influence on the
response levels, and hence, latency, that influence should be accounted
for and factored out by analysis of covariance, which was not the case.
The 15-ms increase in response latency associated with the presence of
a surround thus appears to be an intrinsic property of surround neurons
in MT/V5 and suggests that neurons with strong, well-developed
surrounds may represent a later stage in processing than those which
have no or only weak surrounds. Such neurons presumably could be
created either by combining intracortical input from lower-order
neurons or from other nearby neurons at the same hierarchical level or
by combining local representations of receptive fields with feedback
from higher areas (Tanaka et al. 1986).
Significantly longer onset latencies in the surround compared with the
center would favor feedback as the source of the antagonistic surround.
Previous studies examining antagonistic motion surrounds and latency in
MT of the owl monkey have reported that the onset of the surround
antagonism began <40 ms (the bin size in the experiment) later than
that of the center response (Allman et al. 1985). Later studies suggest that the difference is probably no more than 10-15 ms
in the macaque (Orban 1998
; Raiguel et
al. 1998
), corresponding to one or two intervening synapses and
suggesting that the surrounds are created by combining signals from
within MT/V5 itself rather than being imposed by feedback from higher areas.
Neurons in area V1 of the macaque also possess antagonistic surrounds.
These react to stimulus qualities such as orientation, texture, color,
luminance, and disparity (Knierim and Van Essen 1992;
Sillito 1995
) and tend to suppress responses when the
stimulus in the RF matches that of the surround, in a manner analogous to the way antagonistic surrounds react to motion in area MT/V5. Onset
delays ranging from 7 to 50 ms with respect to response onset have been
reported for surround influences in V1 (Knierim and Van Essen
1992
; Lee et al. 1998
; Zipser et
al.1996
), but no correlation has been reported in V1 between
the strength or presence of such surrounds and the latency of the
response (Knierim and Van Essen 1992
) as we have found
in MT/V5. Although some investigators have reported that many of the
modulatory effects elicited by the surround can disappear under
anesthesia (Lamme et al. 1998
), suggesting feedback from
higher areas, others (Hupé et al. 1998
) have
shown, through inactivation studies, that that feedback to V1 from
MT/V5 largely amplifies responses in V1, rather than inhibiting responses as antagonistic surrounds do and arguing that feedback, from
MT/V5 at least, does not give rise to V1 surrounds. Perhaps surrounds
in V1, like the neurons themselves, represent a more heterogeneous
population than in MT/V5, with some generated by feedback from V2 or
higher areas, whereas others arise locally through lateral or
feedforward connections.
Laminar influences
With the exception of layer II, as discussed in the preceding
text, the distribution of latencies across layers closely follows that
described in V1 by Maunsell and Gibson (1992): lowest in the input layers and slowly rising with increasing vertical
displacement from layer IV. The generality of this distribution is
demonstrated by cat primary visual cortex, which follows a similar
pattern save that in that species, afferents into layer VI reduce
average latencies in this layer to levels approaching that of IV
(Best et al. 1986
). The increase in latencies observed
across the thickness of the cortex probably has its rather
straightforward origin in the polysynaptic input to the more
superficial layers (Levitt et al. 1996
), and the
synaptology of MT/V5 almost certainly follows a similar pattern. Each
neuron in the sequence will add ~5 to 10 ms of integration time
(Nowak et al. 1995
; Nowak and Bullier 1997
), so
that the 20-ms delay in activity in the upper layers (Fig. 9) would
correspond to two to four intervening synapses. This is similar to what
has been reported for areas V1 and V2, both in terms of latency
(Maunsell and Gibson 1992
; Nowak et al. 1995
) and synaptology (Levitt et al. 1996
).
Much of the latency increase associated with the upper laminae may be
attributable differences in response levels, however, and thus it is
not obvious how much may be due to synaptic delays and conduction time
per se and how much may simply be due to lower response levels. Yet
this need not be a simple either/or proposition but simply may
represent two aspects of the same phenomenon. Lower response levels in
fact could be a byproduct of passing the information from neuron to
neuron, particularly if the stimulus specificities of the classical
receptive field are generated largely through inhibitory mechanisms as
many have suggested (Bishop et al. 1971; Bonds
1989
; Ferster and Lindström 1983
;
Sillito et al. 1980
; Wörgötter and
Eysel 1991
).
A second element that may provide the link among layer, response, and
latency is the evolution of response properties involving additional
selectivities for parameters not specifically tested here, such as
depth, orientation, or disparity. Recent evidence suggests that the
surround configurations may be more complex than previously suspected
(Xiao et al. 1995, 1997
, 1998
) and that they
are capable of specifying more sophisticated stimulus properties, such
as the direction of a speed gradient, that our testing procedure did
not consider. In other words, the generally stronger surround antagonism in neurons at more advanced stages of processing may parallel an increase in the selectivity of those neurons for specific, but unknown stimulus characteristics with a consequent decline in
response levels. In this regard, any "standard" stimulus will produce a range of response levels, and hence latencies, depending on
the degree to which it matches these unknown specificities. A second
consequence of these emergent selectivities will be an increased
overall scatter in the latencies of any given layer because the
stimulus may or may not match the tuning of a particular neuron for
those properties, as chance dictates.
Computational issues and direction tuning
The strong direction tuning from the very onset of the spike
trains indicates that MT/V5 neurons should have the capacity to specify
the direction of motion in even the earliest part of the response. It
has been found using information theory (McClurkin and Optican
1996; Tovée et al. 1993
) that the
information available during the first 20-50 ms of firing is
sufficient to specify most of the information carried by the spike
train. The availability of such information is reflected in the rapid
rise in the SI, which actually precedes the rise in spike rate observed
in our sample. The extension of the spike period beyond this initial discharge increases the overall information content of response (Tovée et al.1993
), as shown by the fact that the
SI continues to rise despite the slight broadening in the directional
tuning width. The initial sharply tuned but statistically weak portion of the signal corresponds to the "fast brain" aspect of the neural circuitry (Nowak and Bullier 1997
), comprising
those processes that depend on precise temporal relationships and
require rapid conduction and processing, whereas the later part of the
response, where distinctions between responses to optimal and
nonoptimal stimuli are maximal (Oram and Perrett 1992
),
differentiate complex spatial or spatiotemporal patterns using feedback
circuits and entailing longer latencies (Maunsell 1987
).
Temporal "smearing" of the response, moreover, permits interaction
with other neurons higher up in the processing hierarchy and provides
an opportunity for additional stimulus specificities to evolve
(Knierim and Van Essen 1992
) and for finer
discriminations to take place (Zohary et al.
1990
).
Like orientation tuning (Celebrini et al. 1993;
Ringach et al. 1997
; Somers et al. 1995
),
direction selectivity could arise from feedforward mechanisms or could
additionally involve recurrent intracortical feedback (Maex and
Orban 1996
; Murthy and Humphrey 1999
).
Feedforward models emphasize convergence or synchronization of input
(Gawne et al. 1996
; Maunsell and Gibson
1992
; Nowak and Bullier 1997
) onto neurons that
behave as coincidence detectors (König et al.
1996
), such that those sharing similar tunings for a given
characteristic are mutually reinforcing (Löwel and Singer
1992
; Toyama 1988
). This model can just as
readily apply to MT/V5 because V1 input is already directional
(Movshon and Newsome 1996
) and would account for the
tendency for higher firing rates to be associated with sharper tunings.
Combining slightly different optima to create a broader tuning would
mean that the stimulus is not optimal for some components, resulting in
a signal that is not only weaker from the outset but is relatively
desynchronized due to the different response latencies of the
components. On the other hand, rapid, local intracortical feedback
could further sharpen direction tuning in MT/V5 through excitatory
connections from layer VI onto layer IV neurons (Grieve and
Sillito 1991
), producing tuning that develops over a very short
time course and firing rates that are highest in input layers and in
cells that are more sharply tuned. The amplification of layer IV
responses need not necessarily come from neurons in other layers but
even could be provided by other afferent axons (Rockland 1989
,
1995
) in a feedforward arrangement. Such a mechanism has the
additional benefit of reamplifying the signal at each succeeding
cortical area and would result in the laminar response patterns we observe.
Latencies in static versus moving stimuli
Analysis of data comparing flashed and moving stimuli in MT/V5 and
preliminary work in V1 and V2 (unpublished results) indicate that the
latencies of responses to these two types of stimuli differ
significantly in their relationships to the strength of those
responses. Moving stimuli reveal a dependence on response strength in
all three areas that is largely or entirely lacking using flashed
stimuli. Others have reported that in V1, average latencies to such
stimuli appear to remain constant across cells (Richmond et al.
1997), at least when neurons are tested with their optimum
stimulus (Celebrini et al.1993
). Part of the distinction between flashed and motion responses may lie in the mechanics of
stimulus detection. A flashed stimulus can be registered by input from
a single retinal locus, whereas detection of movement necessarily
involves many inputs, (see Computational issues) scattered across visuotopic space. On one hand, differences in spacing between inputs will induce timing differences corresponding to the variable component of latency described by Lisberger and Movshon
(1999)
and related to the distance that must be
traversed before a motion response is initiated. On the other hand,
differences in synchronization among inputs will induce differences in
both latency and in response strength because increasingly synchronized
inputs will lead to shorter latency and stronger responses. The second
part of the explanation is that the time courses of the static
responses themselves are restricted. Evidence for this comes from
recent whole cell patch-clamp experiments showing that nonlinear
shunting inhibition shapes inputs from on and off subregions,
constraining responses to flashed stimuli to a predetermined time
envelope (Borg-Graham et al. 1998
). In effect, this
means that the latency of the excitatory response reflects the offset
of the shunting inhibition more than the dynamics of the
depolarizations, and hence latency will be independent of response strength.
The distinctive neuronal response dynamics for moving and flashed
stimuli may well constitute the neural basis for the motion extrapolation of moving but not flashed stimuli revealed rather dramatically by psychophysical experiments (Nijhawan
1997). Under the scheme described here, only the response onset
times of moving stimuli can be adjusted to achieve the precise degree
of motion extrapolation necessary to represent a moving object where it is rather than where it was before the intervening processing time.
Hence the consistent relationship found between response strength and
latency for moving stimuli may be no epiphenomenon but may constitute
an actual mechanism for controlling the timing of visually guided behavior.
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ACKNOWLEDGMENTS |
---|
The technical assistance of P. Kayenbergh, G. Meulemans, and G. Vanparrijs is gratefully acknowledged. We also convey our gratitude to Janssens Pharmaceutica (B-2340 Beerse, Belgium), which supplied the sufentanil used in these experiments.
This work was supported by grants from the National Research Council of Belgium (FGWO 9.0225.95), the Regional Ministry of Education (GOA 95/99-6), and the Federal office for Scientific, Technical, and Cultural Affairs (IVAP 4/22).
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FOOTNOTES |
---|
Address reprint requests to S. E. Raiguel.
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.
Received 25 September 1998; accepted in final form 27 May 1999.
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REFERENCES |
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