Cerveau et Vision, Institut National de la Santé et de la Recherche Médicale U371, 69675 Bron Cedex, France
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ABSTRACT |
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Hupé, Jean-Michel, Andrew C. James, Pascal Girard, Stephen G. Lomber, Bertram R. Payne, and Jean Bullier. Feedback Connections Act on the Early Part of the Responses in Monkey Visual Cortex. J. Neurophysiol. 85: 134-145, 2001. We previously showed that feedback connections from MT play a role in figure/ground segmentation. Figure/ground coding has been described at the V1 level in the late part of the neuronal responses to visual stimuli, and it has been suggested that these late modulations depend on feedback connections. In the present work we tested whether it actually takes time for this information to be fed back to lower order areas. We analyzed the extracellular responses of 169 V1, V2, and V3 neurons that we recorded in two anesthetized macaque monkeys. MT was inactivated by cooling. We studied the time course of the responses of the neurons that were significantly affected by the inactivation of MT to see whether the effects were delayed relative to the onset of the response. We first measured the time course of the feedback influences from MT on V1, V2, and V3 neurons tested with moving stimuli. For the large majority of the 51 neurons for which the response decreased, the effect was present from the beginning of the response. In the responses averaged after normalization, the decrease of response was significant in the first 10-ms bin of response. A similar result was found for six neurons for which the response significantly increased when MT was inactivated. We then looked at the time course of the responses to flashed stimuli (95 neurons). We observed 15 significant decreases of response and 14 significant increases. In both populations, the effects were significant within the first 10 ms of response. For some neurons with increased responses we even observed a shorter latency when MT was inactivated. We measured the latency of the response to the flashed stimuli. We found that even the earliest responding neurons were affected early by the feedback from MT. This was true for the response to flashed and to moving stimuli. These results show that feedback connections are recruited very early for the treatment of visual information. It further indicates that the presence or absence of feedback effects cannot be deduced from the time course of the response modulations.
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INTRODUCTION |
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Figure/ground discrimination
based on motion cues involves two operations: the integration of points
moving at the same velocity and in the same direction, and hence
sharing a "common fate" (Wertheimer 1923), and the
precise segmentation of the object from its background. Both
extracellular recordings (Britten et al. 1992
;
Newsome et al. 1989
) and lesion experiments
(Newsome and Paré 1988
; Rudolph and
Pasternak 1999
) highlighted the role of the visual area MT/V5 in the global process. Another lesion experiment showed that the discrimination of shapes based on a kinetically defined boundary (border between 2 random dot fields moving in different directions or
at different speeds) was specifically impaired when MT was removed in
macaque monkeys (Marcar and Cowey 1992
), suggesting that
the local process would also depend on MT. However, MT neurons do not
code the orientation nor the position of kinetically defined boundaries
(Marcar et al. 1995
), implying that whereas MT is
necessary for indicating the presence of a motion-defined figure and
establishing a reliable indication of its direction of motion, it has
to send this information to other cortical visual areas where further local processing of the kinetic boundary would be performed. Feedback connections from MT to areas V3, V2, and V1 may be involved, as selectivity for the orientation of kinetically defined boundaries has
been observed in V2 (Marcar et al. 1994
) and selectivity
to motion-defined contours exist in V1 (Lamme 1995
).
Low-order areas are in fact quite a logical locus for registering
(Mumford 1993
) the precise position and orientation of
kinetically defined contours, as the neurons have small receptive
fields and are sensitive to high spatial frequencies, thus coding
contours with high resolution. Further evidence comes from
psychophysics, as the motion segmentation mechanism shares its speed
tuning with that of V1 neurons, whereas that of motion detection
mechanisms corresponds to MT neurons (Masson et al.
1999
). In a previous study, we indeed showed that figure/ground
information based on motion cues is fed back to areas V1, V2, and V3 of
the anesthetized macaque monkey (Hupé et al.
1998
).
Figure/ground coding has been shown to influence the late part
of the neuron responses to visual stimuli in V1 (Lamme et al. 1999; Lee et al. 1998
), and it has been
suggested that these late modulations could depend on feedback
connections (Lamme et al. 1998a
,b
). Whereas it is well
documented at the anatomical level that feed-forward projections are
matched by reciprocal feedback connections that have distinctive
lamination patterns (Felleman and van Essen 1991
), the
functional role of these connections has been poorly studied and is not
yet really understood (Salin and Bullier 1995
).
Conspicuously absent from all studies is an analysis of the time course
of the feedback influences. Higher order areas contain neurons whose
responses tend to lag behind those of lower order areas (Nowak
and Bullier 1997
), so a delay for the involvement of feedback
connections would seem logical. In this study, we tested whether it
takes time for MT figure/ground information to be fed back to lower
order areas. Such a delay would have consequences for the timing of the
perception of shapes and contours from motion, and would be consistent
with the longer time course for motion segmentation compared with
motion discrimination (Masson et al. 1999
).
In this paper, we directly address the question of the timing of the
influences of feedback connections on the responses of low-order area
neurons. We measured the latencies of the effects of MT inactivation on
the responses of V1, V2, and V3 neurons to the moving stimuli used in
our previous experiment (Hupé et al. 1998), to
determine whether motion-based figure/ground segmentation modulates the
late part of the responses of neurons in areas V1, V2, and V3. For some
neurons, we also measured the latency of the effects of MT inactivation
on the responses to flashed stimuli. Indeed, one could argue that
responses to moving stimuli are not the appropriate way to examine the
timing of feedback influences because a moving bar activates MT neurons
with receptive fields larger than that of the recorded neuron, and
therefore the influence of the feedback may already be present before
the lower order neuron has started to respond as the bar enters its
receptive field (RF) center. We compared the time course of the
responses of individual neurons to moving and flashed stimuli when MT
was active or inactivated, and failed to find any substantial delay for
the feedback influences in both cases. At the level of the population,
it is important to know the latency of the neurons that are affected by
removal of feedback input, as a delayed modulation could either express
itself on the late part of the responses of neurons with short
latencies, or on the early response of late responding neurons. In the
Lamme et al. experiments, the question could not be addressed as large
clusters of neurons were recorded simultaneously (Lamme et al.
1999
) or the responses of several units with maybe different
latencies were pooled together to see the timing of the modulations
(Lamme 1995
). We measured the latency to flash stimuli
and found that feedback connections act on the early part of the
response even in the case of neurons with short latencies.
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METHODS |
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Animals and recording procedures
Recordings were obtained from two anesthetized, paralyzed
cynomolgus monkeys, which were tested with a series of moving and flash
stimuli while area MT was inactivated. Monkeys were initially anesthetized by an intramuscular injection of ketamine hydrochloride (Imalgene, 15 mg/kg). An intravenous catheter was placed in the cephalic vein, and an endotracheal tube was positioned. During surgery,
the animals were anesthetized by repeated intravenous injections of
0.1-0.2 ml of alphadolone and alphaxolone (Saffan). During recording,
they were paralyzed by a continuous infusion of pancuronium bromide (10 mg · kg1 · h
1, in a solution of lactated Ringer and
glucose 5%) and artificially ventilated with
N2O/O2 (70%/30%).
Anesthesia and analgesia were supplemented by a continuous infusion of
suffentanil (usually 4 µg · kg
1
· h
1). The end-tidal
CO2 level and the heart rate were monitored and maintained at proper levels.
A device sealed to the skull with screws and dental cement held the
animal head. The pupils were dilated by corneal application of 1%
atropine. Refractive lenses were used to focus the eyes at a distance
of 1.14 m from a tangent screen. A craniotomy was performed above
area V1, just below the lunate sulcus, whose blue trace could be
detected through the skull. Penetrations were made in V1 through the
dura matter, to achieve a better stability of single-unit recordings.
We used a device made of two microelectrodes glued together (inter-tip
distance, 300 µm). Recording microelectrodes were tungsten-in-glass
type (Merril and Ainsworth 1972) with typically 10-µm
tips, which could provide multi-unit as well as single-unit recording
of cortical neurons. We used a spike discriminator (MSD, from Alpha
Omega) to extract single units and to monitor the identity of the
neuron under study during periods of control, MT inactivation, and
recovery. Spike activity was recorded with a PC-based system (CED 1401 interface and Spike2 software). Analyses were done on-line, but all the
recordings were also stored on videotapes for off-line analysis. All
the recordings were replayed to check the identity and the stability of
the studied neurons. In addition, isolation index (II) of the spike
traces were systematically calculated for each testing period. The
isolation index is the ratio of the peak value of the histogram of
errors (between the spike template and the recognized spikes) over the
value of the histogram at the rejection threshold. It is now
implemented in the MSD
software.1 A value
of one indicates a perfect isolation; a value of zero indicates
multi-unit activity with no possibility of isolation of a single
neuron. Examples of the use of the MSD and of the isolation index were
published elsewhere (Guenot et al. 1999
).
At the end of the experiment, the animals were killed by an overdose of pentobarbital sodium and perfused with normal saline followed by 4% paraformaldehyde in phosphate buffer. The posterior part of the brain was removed and, after cryoprotection in 30% sucrose, cut at 50 µm on a freezing microtome in the parasagittal plane. On histological Nissl-stained sections, penetrations were reconstructed from lesions placed during the recording (typically 7 µA for 7 s).
Feedback inactivation
Area MT and adjacent cortices were inactivated by circulating
chilled methanol through chronically implanted hypodermic loops that
induce a localized hypothermia and block synaptic function and activity
of neurons (Lomber et al. 1999). We implanted the probes
in the superior temporal sulcus prior to the experiment (Fig.
1). The method and the controls have been
described in detail elsewhere (Hupé et al. 1998
;
Lomber et al. 1999
). Each cooling session lasted less
than 5 min. The total number of cooling sessions done over 4 days on
each animal was 41 and 57. There was no correlation between the number
of cooling cycles and the frequency or the strength of the effects.
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Measurement of response latency
We adapted the method of Maunsell and Gibson
(1992) and Nowak et al. (1995)
to identify the
beginning of the responses to the flashed and moving stimuli.
Peristimulus time histograms (PSTHs) of responses were computed
over 20 repetitions of the stimulus presented in control condition
(before MT was inactivated). The binwidth was typically 5 ms, but
binwidth values ranging from 2 to 20 ms were also used depending on the
response strength. The histogram of the total number of spikes recorded
during a spontaneous activity period (background
distribution2) was
fitted with a Poisson function. Then, for flashed stimuli, the latency
was taken to be the time corresponding to the center of the first bin
after stimulus onset that 1) exceeded a level corresponding
to a probability of P = 0.01 with respect to the background distribution and 2) was immediately followed by a
bin that also reached this criterion and a third that exceeded a level corresponding to a probability of P = 0.05 (Maunsell and Gibson 1992
). These criteria might appear
stringent, and it could be argued that a first weak and transient
component of the response, presumably due to feed-forward activation,
would have been missed. That is the reason why we systematically used
different binwidth sizes and chose the shortest latency, to include any
first weak component in the response, as long at it was no more than 20 ms ahead of the sustained response. Also, if any early response
component existed, it should have been visible on population histograms before the measured latency (see Fig. 8). We did not observe such an
earlier component.
For moving stimuli, we used smaller P values: P = 0.005 and P = 0.025, respectively. The purpose of this modification was to identify specifically the onset of the main response to the moving stimulus. In fact, we observed that responses of some neurons started to slowly grow well before the peak response, suggesting that the bar moved across a region of the RF with low sensitivity.
Empirical controls of the method were carried out. The computed
latencies were first visually checked for many neurons. We also took
advantage of the fact that two sets of 20 repetitions were usually done
in control condition, as a measurement of the stationarity of the
response over time (Hupé et al. 1998). The latency
was measured for both controls, and large differences between both
measurements were systematically checked and documented: in a few
obvious cases, the shortest latency was due to a burst occurring during
the spontaneous activity period and was therefore discarded. Otherwise
the minimal latency measured in both controls was used.
Analysis of effects of feedback inactivation on response strength
Visual stimuli were usually presented for 1 s on a
computer monitor driven by a Truevision Vista Board under the control
of a Matlab program. Intertrial was 0.5 s. Eight moving stimuli
and one flash stimulus were presented 20 times in an interleaved
fashion. A low-contrast textured background (12.7° wide and 8.4°
high, 9-24 Cd/m2) (see Hupé et al.
1998) was always present, either stationary or moving. The bar
was therefore flashed against a stationary textured background. The bar
was centered on the neuron receptive field (located in the central 4°
of visual field) and approximately optimized in spatial phase,
orientation, and size. The length of the bar was between 0.2 and 1°
(mean = 0.65) and the width between 0.05 and 0.15° (mean = 0.09). Responses to the stationary bar were not always good, as the
main purpose of the experiment was the effects of MT inactivation onto
moving stimuli. One hundred five of the 169 neuron recorded in areas
V1, V2, and V3 gave sizable ON responses to the flashing
bars. (OFF responses were also measured but they are not
described in this paper. The results were similar.) The selection of
sizable responses was done first by observation of PSTHs with 100-ms
binwidth. Latencies were then calculated as previously described.
Among the eight moving stimuli, four stimuli were made of the
background moving alone and were used to check that the background alone was not sufficient to elicit a response of the neuron. The speed
of the stimulus was optimized for each site, and was between 0.75 and
7.5°/s (mean = 2.9). As the flashed stimuli did not always elicit a response to the neurons that were studied, the responses to
the four moving stimuli and the response to the flashed bar were
analyzed separately. Spikes were counted during 500 ms for the flash
stimuli or else during the whole period the stimulus was moving
(typically 1 s). The mean spontaneous activity recorded during
each run was then removed. We used the bootstrap Student t-test (Efron and Tibshirani 1993) with
10,000 bootstrap replications. This test allows the comparisons of the
means of two distributions without making any assumptions about the
shape of the distributions and is still valid when the variances and
sample sizes of the two samples are different. (This is not the case of
the classic Student t-test nor of its nonparametric
equivalent, the Mann-Whitney U test.) As four responses to
different moving stimuli were studied simultaneously, there was an
increase of the type I error. The actual error was controlled thanks to
the procedure adapted from Manly (1997)
: instead of
applying the same set of randomizations to the data, we applied the
same set of bootstrap replications. The significance level was
therefore a controlled 5% type I error (see next section). The effect
of MT inactivation on the mean responses of the V1, V2, and V3 neurons
to the moving stimuli were described in a previous paper
(Hupé et al. 1998
) and are presented in detail
elsewhere (Bullier et al. 2000
). The ON
responses to the flashed bar were tested independently, but the fact
that responses to moving stimuli were tested in the same recording session increased the experimentwise type I error (Ludbrook
1991
). We thus decided arbitrarily to take an individual
nominal significance level of 1%, as we had done previously
(Hupé et al. 1998
), to protect us globally against
the alpha risk at a level of about 5% (when the measures are independent).
A test was first done to compare the response strengths between two
control runs of 20 stimulus repetitions each (Hupé et al.
1998). If the test was significant, the neuron was discarded, and the response was considered as not stationary. Ninety-five of the
105 neurons were kept after this first stage of analysis for the
ON flash response, and 154 of the 169 neurons were kept for
the analysis of the effect of cooling on the response to the bar moving
across the stationary background. Tests were then done between the
control runs and the cooling run.
Measurement of the latency of effects of feedback inactivation
There were too few repetitions of the stimulus presentation to allow a statistical measure of the latency of the effects on individual neurons, so we had to pool the responses of the neurons that behaved similarly to increase the signal/noise ratio. Population PSTHs were therefore computed from neurons being similarly affected (i.e., whose response was significantly increased or decreased) by feedback inactivation. Individual PSTHs were first computed with a 5-ms binwidth, then normalized to the peak response (100% of response, arrow 1 on Fig. 2A) and aligned to the beginning of the responses (response latency, arrow 2 on Fig. 2A). The PSTHs were then averaged. The latency and maximum response to the stimulus in control condition (before cooling) served as normalization for all the PSTHs (control, cooling, and recovery runs). Population PSTHs with binwidths of 10 and 20 ms were done from the 5-ms PSTH. The choice of a binwidth for further presentations and tests was done empirically with the criterion that once the response had started, then the PSTH was smooth enough. We preferred this method to the classical Gaussian convolution because we wanted to ensure that the mean responses obtained in each bin were reliable enough to allow statistical tests. In this way, we could also obtain an estimate of the temporal precision of a given data set.
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Wilcoxon tests were done for each bin between the control and the inactivation condition. The choice of replacing the values by their ranks was justified by the previous normalization, which had already eliminated data of the absolute response. Repetitions of tests on the same set of data increase the type I error. Note, however, that these tests were done on responses that were already globally different, as the population histogram was computed only from neurons whose response was different when averaged over 500 ms or 1 s. The question was as follows: from when was this difference significant for the given sample? If for a given neuron, there was a difference of response from the beginning to the end of the response, then the differences in the bins should be highly correlated. If all the neurons behave this way, the type I error would be exactly the same regardless of whether the test was done on the whole response or on any part of the response. We therefore used a multiple comparisons procedure (MCP), which took into account the correlation of the measurements in the successive bins.
Manly (1997) proposed such a procedure for multiple
randomization tests. We took advantage that the Wilcoxon test is just one kind of randomization test to adapt this test. For sample sizes up
to n = 16, all the randomizations of possible signs
were done (exact test), i.e., 2n. For each set
of data (one real, 2n
1 randomized), the
statistic of Wilcoxon was calculated simultaneously for x
variables (x = the number of bins we wanted to test).
{When the [2n] set of data [ ] are ranked
in order according to the minimum significance level obtained from the
[x] variables, it is found that 95% of these minimum
significance levels exceed [P']%. In other words, if the
variables are tested individually at the [P']% level,
then the probability of obtaining any of them significant by chance is
5% (Manly 1997
, p. 111)}. The P' value
computed this way could be compared with the P value
obtained with the classical Wilcoxon test. When the data for all the
comparisons are perfectly correlated, then there is absolutely no
difference between the two results. On the other hand, when there is
absolutely no correlation, this procedure is equivalent to the
Bonferroni procedure, which weights each significance threshold by the
total number n of comparisons (P' = P/n). For samples larger than 16, all the
possible randomizations were estimated by a random sample of 10,000 randomizations (including the observed one), the same sample being of
course used for all the comparisons (Manly 1997
).
All of these calculations were carried out in Matlab 4.2 (MathWorks).
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RESULTS |
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Responses to moving stimuli
Responses of 154 neurons to moving stimuli were recorded in areas
V1, V2, and V3 before, during, and after MT was inactivated by cooling.
The mean response of the neurons was measured. The effects of MT
inactivation on response strength have been described elsewhere
(Hupé et al. 1998). Briefly, two major effects
were observed. First, the response of neurons to a bar moving against a
stationary textured background (stimulus BS) was decreased when MT was
inactivated, indicating that motion information useful for the target
segregation was fed back to these neurons. Second, responses were
compared when the background was stationary and when it was moving
together with the central bar. We were interested in the neurons for
which the response was significantly decreased to the latter stimulus,
as it meant that the neuron responded strongly when there was a motion
contrast between the bar and the background. For such neurons, when
tested at low salience, i.e., when motion was almost the only cue to
detect the bar, the response to the bar moving together with the
background (stimulus BM) was increased when MT was inactivated. This
effect, significant for six neurons recorded in V3, lead to a loss of
the ability of these neurons to perform figure/ground discrimination
based on motion cues (Hupé et al. 1998
).
In the present study, we first examined the latency of the significant decreases of response to BS (51 neurons). Figure 2 shows examples of the time courses of the responses of single neurons recorded in V1 and V2 in 1) control condition before the inactivation, 2) while MT was inactivated, and 3) during the recovery after circulation of the coolant had been switched off. Examples of V3 neurons are presented in the left part of Fig. 4. Typically, the decrease of the response could be observed during the first 20-ms bin of response and could last up to the end of the response. This was most obvious for the cases where there was a total suppression of the response during cooling (Fig. 4A) and also for the majority of the significant but weaker response decreases presented here (Figs. 1, A-F, and 4, C and E).
The average PSTHs for control, cooling, and recovery blocks of trials
were then computed after normalization of response strength and
alignment of latencies (Fig.
3A). The difference between
the average PSTHs during control and cooling is illustrated in Fig. 3B, with the bins showing significant differences indicated
below (only the period between 200 and 200 ms with respect to the
onset latency was tested; the period before the onset latency was
tested independently of the response period). This shows that the
response is affected by inactivation of MT during the first 10-ms bin
following response onset and that the difference has an early peak,
thus suggesting that the feedback effects are extremely rapid. Note that there is no significant change in spontaneous activity before the
onset of the response (we will return to the issue of spontaneous activity below).
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Figure 4 illustrates examples of neurons for which cooling MT decreased the responses to the bar stimulus (BS) while increasing the responses to bar moving together with the background (BM). For both response decrease and increase, the changes are observed from the beginning of the response. The examples of Fig. 4, C-F, are two of the six V3 neurons tested at low salience whose response to BM was significantly lower than the response to BS in control condition. This differential response (background suppression) allows the neurons to code the presence of the bar moving against a stationary textured background (segmentation based on motion cues). We did not compute a population histogram for these six neurons as in most of the cases we were not able to measure the latency of the very small response recorded in the control condition. The other four neurons showed, however, a similar pattern demonstrating an early increase in response when MT was inactivated.
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One way to interpret such a rapid effect of feedback inactivation on the visual responses of neurons in areas V1, V2, and V3 is that it results from a change in the gain control of V1-V3 neurons because cooling decreases the spontaneous activity of MT neurons that send feedback connections to V1-V3 neurons. As a result, the spontaneous activity of V1-V3 neurons themselves should be affected by the inactivation of MT, and this should predict the changes of the evoked responses. To test this possibility we examined the relationship between changes in the spontaneous activity of the neurons in areas V1-V3 and the changes in the evoked responses. The spontaneous activity was measured during the 500-ms periods between the stimulus presentations, as described in METHODS. Figure 5A presents a scattergram of these two variables. In abscissa we plotted the change of V1-V3 spontaneous activity when MT was cooled. The "plus" (+) symbols indicate the neurons for which the change in spontaneous activity was significant (total = 24/155 neurons). Both significant increases and decreases of the spontaneous activity were observed. If a change in the gain control of neurons should explain the effects on the evoked response, then the direction of changes in spontaneous activity should be correlated with the direction of the effect on the response to the stimulus BS. It is clear from Fig. 5A that this is absolutely not the case. The neuron presented on Fig. 2D, and labeled 2D on Fig. 5A, illustrates a case for which a decrease of response is observed despite a strong increase in spontaneous activity. There appears to be a tendency for neurons with significant changes of BS responses to show a significant change of the level of spontaneous activity. However, the proportions are not significantly different between neurons with significant changes in BS and those that showed no changes (16/95 vs. 18/60,3 P > 0.05).
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In Fig. 5B we present the averaged PSTHs for 11 neurons for which changes in spontaneous activity were minimal (1 neuron with 6% change, the other cases below 1%). It is obvious that despite such a stable level of spontaneous activity, the response decrease is marked and is observed very early after the beginning of the response. Further arguments against the effect of cooling on the steady-state gain control of neurons in areas V1-V3 are presented in the DISCUSSION.
Although our results demonstrate an early effect of feedback connections, this may simply reflect the fact that feedback connections preferentially target neurons with late responses to visual stimulation. This was tested by comparing the latencies to flashed stimuli of neurons that showed response decreases to the BS stimulus and neurons showing no significant effects. As evident in Fig. 6, there is no significant difference between these two populations, thus supporting the idea that even the earliest activated neurons may be influenced by feedback stimuli.
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Responses to flashed stimuli
We then examined the timing of the effects of MT inactivation on
the responses of neurons to stimuli flashed in the RF center. Among the
95 neurons for which the ON response was stationary enough
to assess the effects of MT inactivation, 29 neurons were significantly
affected. The effects were decreases of responses (15 neurons) as well
as increases (14 neurons). As reported elsewhere (Bullier et al.
2000), whether increases or decreases are recorded depend on
the area of recording and the salience of the stimulus. Two examples of
significant decreases of the response of V1 neurons are shown in Fig.
7, A and B. The
PSTH traces for control, cooling, and recovery (for A) are
shown, using a binwidth of 20 ms. The decrease of response due to MT
inactivation could be observed from the very start of the response
(A, 60-80 ms; B, 40-60 ms). The neuron of Fig.
7B was recorded in layer 4b of V1, which is reciprocally
connected with MT (Shipp and Zeki 1989
;
Ungerleider and Desimone 1986
). This neuron had the
shortest latency of our sample (42 ms). Similarly, two examples of
significantly increased responses (Fig. 7, C and
D) indicate that the effect of MT inactivation was
present from the very start of the response. For the neuron of Fig.
7C, the increase could be observed in the first bin of response (60-80 ms). In the case shown in Fig. 7D, the
latency of the response is shorter during cooling than in the control condition. For these four neurons, the effect was already significant when the first 100 ms of response were tested (P < 10
4).
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The effects were observed from the beginning of the response for most but not all the neurons: for 3 neurons among the 14 for which the response was significantly increased during cooling, the increase was only present after about 100 ms of response. Similarly, for 3/15 neurons, the response seemed to be decreased only after more than 50 ms of response.
Average PSTHs for flashed stimuli were computed with similar methods as
described for moving stimuli and are shown in Fig. 8, A and B, for
response decreases and Fig. 8, C and D, for
response increases. Only 13 of the 15 significantly decreased responses (and 12 of the 14 significantly increased responses) were used to
compute the population histograms; as for the other neurons, the flash
response in control condition was too small or sluggish to measure the
latency reliably. The mean decrease and increase of response during
cooling was large and present from the beginning of the response. The
recovery of the control activity was in average almost perfect, as can
be seen from the superposition of the control and recovery traces. The
differences between normalized control and cooling responses were
computed (Fig. 8, B and D). The level of
significance for the response changes was computed between 120 and
120 ms with respect to the onset latency. Significant decreases are
observed at the beginning of the response (the 1st 10-ms bin of
response is significant, Fig. 8B). The mean increase is even
seen and significant before the response onset (20-ms bin before the
control response onset, Fig. 8D), since, as observed on Fig.
7D, the latency of response could be shorter during cooling than during the control. The effect lasts until the end of the response. As in the case of moving stimuli, no significant change was
observed during the period of spontaneous activity.
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One could imagine that only neurons with late latencies are affected by the feedback from MT, with the consequence that for the population of neurons of one area, the influences of feedback from MT would be late, even if the affected neurons were affected at the beginning of the response. This is not the case, as can be observed on the histograms of the latencies of the neurons tested in V1, V2, and V3 (Fig. 9). There is no tendency for neurons with late latencies to be more frequently affected by MT inactivation.
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DISCUSSION |
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Our results show that effects of inactivating area MT on the responses of neurons in areas V1-V3 can be observed on the earliest part of the response, and they can last the whole duration of the stimulus response. We observed this result for both moving (Figs. 2-6) and stationary, flashed stimuli (Figs. 7-9). At the population level the increases and decreases of responses were always significant 10 ms at the latest after the response onset. This interval corresponds to the precision of our latency measurements, given the variability of the responses and the limited number of stimulus repetitions. One possibility that we explored is that feedback from MT acts on the early part of the responses of those V1-V3 neurons that generate longer latency responses to visual stimulation. While this possibility holds for some of our sample, there were several other examples of neurons that were very rapidly activated by visual stimuli, and their earliest activities were also influenced by the MT inactivation. Thus it appears that the effects of MT inactivation on neurons in areas V1-V3 are not or barely delayed with respect to visual responses. This conclusion is reached for both individual neurons and the population.
Absence of effects on the spontaneous activity
One possible mechanism to account for such a rapid onset of feedback influence is that the effect does not depend on the visual responses of MT neurons per se but acts through a gain control mechanism that is regulated in some way by the spontaneous activity in both MT and V1-V3. We think that such an interpretation is unlikely for five reasons. 1) As demonstrated in Fig. 5, there is no relationship between changes in spontaneous activity and changes in evoked responses of neurons in areas V1-V3. If there was a relationship, we would expect to consistently study neurons that show a response decrease and exhibit a decrease in spontaneous activity during MT inactivation. However, we observed several examples of spontaneous activity increase conjoined to visual stimulation decrease in activity (Fig. 2D). 2) For the subset of neurons for which there is no change in spontaneous activity, there is a clear early decrease of response to visual stimulation (Fig. 5B). 3) In a number of neurons (e.g., Fig. 4), there was a decrease of the response to the BS stimulus and an increase of the response to the BM stimulus. If the results of cooling MT were due to a change in gain control related to the lowering of spontaneous activity in MT, it is difficult to see how this steady-state gain control could cause different effects for different visual stimuli. 4) There were examples of neurons for which the initial and later phases of the response were differentially affected by MT inactivation (Fig. 2, D and F). This could result from the fact that the effect of the feedback was stronger for certain parts of the RF of the lower order neuron and weaker or nonexistent in other regions. Such observations are difficult to explain on the basis of a gain control change due to the decrease of spontaneous activity in MT neurons. 5) Finally, there were examples of V1-V3 neurons for which the spontaneous activity changed between acquisition of two control measures, or between control and recovery, but there was no change at all in the response to the stimulus (when MT was active), even though MT inactivation induced a change in response (Fig. 2E).
We believe that these observations effectively counteract the suggestion that the MT influences over area V1-V3 neurons is mediated by changes in gain control based on spontaneous activity.
Mechanisms
For flash responses, when MT is inactivated, response increases of
area V1-V3 neurons occur earlier than response decreases (Figs. 7 and
8). These observations suggest that disynaptic inhibitory influences
are transmitted very rapidly from MT. This view is consistent with
early inhibitory dips in neuronal responses in V1 and V2 (Nowak
et al. 1999) and with the fact that inhibitory neurons appear
to be the first activated neurons in sensory cortex (Swadlow
1995
). Our extracellular recordings supposedly targeted preferentially excitatory neurons. In addition, this shortening of the
latency during inactivation of the feedback could suggest that the MT
feedback was present even before the feed-forward activation arrived.
This could be also the case for neurons with longer latencies. However,
our extracellular recording does not allow to test this hypothesis, and
it could also be that the feedback is precisely timed with the
feed-forward input.
For moving stimuli, a comparison of the effects of inactivation on the
neuron activity before and after the stimulus has entered the RF center
provides interesting clues as to the type of influences that feedback
connections have over lower order area neurons. As is evident in the
examples presented, hardly any change was observed in the neuron
response before the stimulus activated the main discharge center of the
V1-V3 RF. We know that feedback connections are strongly convergent
(Perkel et al. 1986; Salin et al. 1992
)
and RF centers of MT neurons are much larger than those of neurons in
V1-V3 (Gattass and Gross 1981
). If we assume that the
convergence is such that the RF of MT neurons overlap at least
partially those of their target neurons (Salin et al. 1992
), then the space-shift would be 3° (the average RF
diameter of MT neurons at 2° eccentricity). With a maximal speed of
7.5°/s (typical speed was 3°/s), the time-shift in arrival time of
feedback influences is 3/7.5 = 0.4 s. When the bar stimulus
moves toward the RF center of a neuron under study in V1-V3, this
time-shift is long enough to activate many MT neurons that could
provide feedback input to this neuron. Despite this presumably strong excitatory input (Hupé et al. 1998
), no clear
response is evoked in the neuron, as evidenced by the fact that the
activity before the main response is hardly changed by MT inactivation
(Figs. 2-5). This suggests that feedback connections act in a
nonlinear fashion, boosting responses evoked by feed-forward inputs but not evoking responses per se. This conclusion is reminiscent of that
made in an earlier publication (Salin and Bullier 1995
)
concerning the feedback connections from MT to V2. No response was
evoked in V2 neurons when V1 was inactivated (Girard and Bullier
1989
; Schiller and Malpeli 1977
) despite the
extensive feedback input from MT to V2 carrying strong residual
activity in MT when V1 is inactivated (Girard et al.
1992
; Rodman et al. 1989
). A comparable amplification of convergent inputs has been demonstrated for
multisensory neurons in the colliculus (Meredith and Stein
1983
; Wallace et al. 1998
). Interestingly
enough, the potential for response amplification was greatest when
responses evoked by individual stimuli were weakest (Meredith
and Stein 1986
), as in our case, responses to low salience
stimuli most gain from the feedback from MT (Hupé et al.
1998
).
How is such a rapid effect of feedback possible?
Based on hierarchical schemes of cortical connectivity and widely
held beliefs on timing of top-down influences, rapid effects of
feedback connections are unexpected. However, a number of arguments are
consistent with rapid feedback effects. First, it is known that the
spectrum of response latencies of neurons in higher-order areas overlap
very broadly with the response latencies of lower order neurons, and
many are not longer than the latencies of the lower order neurons. For
example, single neuron studies and current source density analyses show
that neurons in area MT have latencies that, on average, are shorter
than the latencies of neurons in V2, and only a few milliseconds longer
than the latencies in areas V1 and V3 (Maunsell 1987;
Nowak et al. 1995
; Raiguel et al. 1989
; Schmolesky et al. 1998
; Schroeder et al.
1998
). It is therefore possible that at least some of the MT
neurons responding with short latencies are specifically involved in
feedback connections. Another point of interest concerns the speed of
feedback connections. Since feed-forward and feedback fibers have
similar conduction velocities (Nowak et al. 1997
), and
feed-forward projections from V1 to MT are extremely rapid
(Movshon and Newsome 1996
), it could well be that some
signals are transmitted from MT to V1-V3 in only 1 or 2 ms. Thus given
the early activation of MT neurons by visual stimuli and the fast
conduction velocity of feedback connections, it is not surprising that
feedback connections could act very rapidly (within 10 ms) on the
responses of neurons in lower order areas. In fact, given the strong
pressure to reduce the number of thick (and thus rapid) rapid
cortico-cortical axons in higher order vertebrates (Murre and
Sturdy 1995
), the very fact that feedback connections are fast
conducting suggests that their actions must be rapid.
It has been argued that the early responses of neurons in area MT could
be explained by a fast parallel pathway that bypasses V1
(Ffytche et al. 1995). Anatomical studies have revealed
anatomical pathways that pass through the superior colliculus and
pulvinar (Standage and Benevento 1983
;
Ungerleider et al. 1984
), or directly through the
lateral geniculate nucleus (Fries 1981
; Yukie and Iwaï 1981
), to reach the superior temporal sulcus. This
pathway likely supports the persistent activity of MT neurons when V1 is inactivated (Girard et al. 1992
) or lesioned
(Rodman et al. 1989
). Moreover, this pathway may be very
fast because visual latencies as short as 30 ms have been reported in
the inferior pulvinar (Benevento and Port 1995
) and in
its target layer 2 of area MT (Raiguel et al. 1999
). It
remains to be tested directly whether this transcollicular-pulvinar
route supports the short visual latencies in MT/V5 to flashes and fast
stimuli, as suggested by the studies of the human brain (Ffytche
et al. 1995
).
Knowledge of pathways and speed of transmission are important both for
guiding interpretation of our results and for the generality of our
conclusions. If the by-pass pathway to MT is shown to be fast, the
significance of our results on feedback effects is attenuated because
the results could equally well be interpreted in terms of blockade of
one of two pathways that converge. Even though such results are
interesting in their own right, they have little significance for
signals interactions in other cortical areas. However, we are inclined
to believe that the effects we have identified are mediated by true
feedback pathways. In the following paper (Hupé et al.
2001), we show that a similar observation is made on the
responses of V1 neurons when area V2 is inactivated by GABA: effects
are observed on the early part of the responses to flashed stimuli,
even for neurons with short latencies to visual stimuli.
Conclusion
The consequence of our results is that the visual cortex should be
considered as temporally compact. One possible function of rapid
feedback influences is to allow neurons to produce their most
significant response (Heller et al. 1995; Tovee
et al. 1993
) after the signals have also been treated by higher
cortical areas and fed back to the lower areas. The latter signals may
be highly relevant to dynamic temporal and spatial aspects of RF
properties (DeAngelis et al. 1995
; McLean et al.
1994
; Ringach et al. 1997
). Thus the fast
feedback connections ensure interactions between the activating and
feedback signals and that include the early part of the response.
Other authors suggested that feedback effects be relegated to the later
phase of the response (Lamme et al. 1998a,b
). Our results do not preclude the possibility that feedback signals also influence the late part of the responses. In fact, we
observed in a few neurons a late effect of MT inactivation. More
surprising is the fact that Lamme and colleagues (Lamme et al.
1999
; Lee et al. 1998
) observed that the late
part of the response of V1 neurons (beyond 100 ms after response onset)
was modulated for the coding of figure/ground segmentation. Our stimuli
tested also figure/ground discrimination (Hupé et al.
1998
), and we did not observe such a delay. Several
methodological reasons may explain these contrasting results. First,
their experiments used awake monkeys. Some of the late modulations
might be due to attention. The fact that anesthesia suppressed the late
figure/ground component (Lamme et al. 1998a
,b
) indicates
that we did not test similar properties of the neurons. Second, they
used flashed stimuli, whereas we used a bar that was already moving
before entering the RF of the neuron under study.
In our paradigm, we were in a situation where a moving target could be
continuously identified when partly hidden in a background, a situation
that often occurs. The fact that in any given part of the visual field
the motion cue was always available to V1, V2, and V3 neurons as soon
as they start to fire is an important result that helps us to
understand why motion is such a powerful cue for precise and sustained
segmentation. In our view, V1, V2, and in a lesser extent V3 neurons
treat local information with their small RF. The early feedback
influences from neurons with larger RF allow the V1 neurons to
integrate also global information, as in the case of figure/ground
discrimination (Hupé et al. 1998). This
local/global integration by neurons of low level areas could also be
involved in the processing of kinetic boundaries (Marcar et al.
1994
, 1995
). Our results further suggest that
the time course for motion segmentation may not be longer than for
motion discrimination, contrary to what was reported in a
psychophysical study (Masson et al. 1999
). But their
task required the three-dimensional perception of multiple surfaces
moving through or over each other, and involved therefore more complex
and obviously different mechanisms than those addressed by our simple
stimuli. Using a simpler figure/ground discrimination task,
Moller and Hurlbert (1996)
observed an improvement of
motion segmentation with stimulus duration; yet with stimulus duration
of <60-80 ms, their broadest segmentation targets (0.72°) were
detected at a lower speed threshold than that required for motion
detection, thus demonstrating a fast mechanism for detecting relative
motion between target and background (Moller and Hurlbert 1996
).
Our results are also very important for the interpretation of many
physiological results. Briefly, when complex properties of neurons are
found that lag the onset of the response, this does not mean that
feedback connections are involved (Hupé et al.
2001). When complex properties do not lag the onset of the response, this also does not mean that these properties are
obligatorily shaped by their feed-forward inputs (Hupé et
al. 1998
). However, late modulations (Lamme et al.
1999
) can be due to feedback connections. Some complex stimuli
that we did not test in this study, or attention, could also lead to a
delayed involvement of feedback connections. The role of feedback
connections can therefore not be suggested by the temporal properties
of lower order neurons. This further underlines the importance of
reversible inactivation studies for understanding the logic of
cortico-cortical connections (Wanduffel et al. 1997
) in
the visual cortex, that must be conceptualized as a network of
interacting areas responding with near-simultaneity, rather than as a
pipeline-type architecture.
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ACKNOWLEDGMENTS |
---|
We thank N. Chounlamountri for help with the histology and G. Clain for the care of the animals.
This work was supported by Biomed Grant BMH4-CT96-1461, North Atlantic Treaty Organization Grant CRG950907, and by the National Science Foundation (S. G. Lomber).
Present addresses: A. C. James, P. Girard, and J. Bullier, Centre de Recherche Cerveau et Cognition, Centre National de la Recherche Scientifique- Université Paul Sabatier U5549, 133 route de Narbonne, 31062 Toulouse Cedex, France; S. G. Lomber and B. R. Payne, Laboratory for Visual Perception and Cognition, Center for Advanced Biomedical Research, Boston University School of Medicine, 700 Albany St., Boston, MA 02118.
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FOOTNOTES |
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1 The II value produced by the MSD software is however not reliable when few spikes are recorded, as the binwidth of the histogram can then be too small. We systematically saved the whole error histograms and calculated the real isolation index from a smoothed histogram computed in Matlab.
2 The spontaneous activity was estimated from the 500 ms periods between the stimulus presentations. This measure was not always very precise, as it could occasionally be contaminated by some late sustained response, or by some rebound from inhibition. It was checked however that this background activity was relevant to measure the onsets of responses to stimuli.
3 The proportion of neurons for which the spontaneous activity changed significantly during cooling could seem important. However, the spontaneous activity already changed frequently (significantly for 25 neurons) between the two controls. This poor stationarity of the spontaneous activity may be due to the short period of measure used (see METHODS).
Present address and address for reprint requests: J.-M. Hupé, Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003 (E-mail: hupe{at}cns.nyu.edu).
Received 20 March 2000; accepted in final form 6 September 2000.
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