A R T I C L E S

A R T I C L E S
© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience
ARTICLES
Adaptation changes the direction tuning of macaque
MT neurons
Adam Kohn & J Anthony Movshon
Prolonged exposure to a stimulus, called ‘adaptation’, reduces cortical responsiveness. Adaptation has been studied extensively
in primary visual cortex (V1), where responsivity is usually reduced most when the adapting and test stimuli are well matched.
Theories about the functional benefits of adaptation have relied on this specificity, but the resultant changes in neuronal tuning
are of the wrong type to account for well-documented perceptual aftereffects. Here we have used moving sinusoidal gratings to
study the effect of adaptation on the direction tuning of neurons in area MT in macaques. Responsivity in MT is maintained best
in the adapted direction and is strongly reduced for nearby directions. Consequently, adaptation in the preferred direction
reduces the direction-tuning bandwidth, whereas adaptation at near-preferred directions causes tuning to shift toward the
adapted direction. This previously unknown effect of adaptation is consistent with perceptual aftereffects and indicates that
different cortical regions may adjust to constant sensory input in distinct ways.
Prolonged inspection of a strong visual stimulus produces vivid visual
aftereffects, in which stimuli similar to the adapting stimulus are perceived to be more different from the adapter than they truly are.
‘Repulsive’ aftereffects of this type are well known for stimulus orientation1,2, curvature3, position4, spatial frequency5 and direction of
motion2,6–8. Adaptation reduces neuronal responsivity in V1, where
the strongest effects are generally observed for stimuli that are similar
to the adapter. A consequence is that adaptation on the flank of a tuning curve causes a repulsive shift in neuronal tuning away from the
adapted value. Such shifts have been found in V1 for spatial frequency9,10, temporal frequency11 and orientation tuning12–16.
Although it might seem that these repulsive neuronal aftereffects
match the repulsive perceptual effects, the neuronal findings are in fact
opposite to those needed to predict the perceptual phenomena17–19.
Consider the distribution of activity elicited by a particular stimulus across a population of tuned cells. Before adaptation, peak activity
is elicited from cells that are tuned to the stimulus. Suppose that adaptation with a nearby stimulus shifts the tuning curves of these cells
away from the adapter. The stimulus that activates them most
strongly will now be further from the adapter than it was before adaptation. If neurons always carry the unadapted perceptual ‘label’, then
more remote test stimuli will be perceived as though they are closer to
the adapter. Thus, repulsive neuronal aftereffects predict not repulsive
but attractive perceptual effects, which are almost never observed.
Partly because of these discrepancies, current theories about the role
of adaptation in sensory information processing have focused on
potential improvements in representational efficiency offered by
repulsive shifts in neuronal tuning20–23 rather than on exploring the
relationship between physiological and psychophysical effects.
Repulsive shifts in tuning have been found only in V1, and the
effect of adaptation on neuronal selectivity in higher cortical areas is
unknown. Here we report on the direction specificity of adaptation in
area MT, an extrastriate area containing a high proportion of direction-selective cells24,25 that has a clearly established role in the perception of visual motion26–28. We have previously found that adaptation
in MT alters neuronal contrast sensitivity but has relatively little effect
on response magnitude29.
In evaluating the direction specificity of adaptation, we found that
adaptation affects MT differently from V1: responsivity is maintained
best for stimuli moving in the adapted direction and is strongly
reduced for nearby directions. As a result, after adaptation near the
preferred direction, direction tuning becomes significantly narrower.
Adaptation on the flank of the tuning curve causes an attractive shift
of tuning toward the adapted direction. This previously unknown
effect of adaptation in MT predicts strong repulsive perceptual aftereffects and suggests that current theories about the functional benefits of adaptation, and mechanistic explanations of their basis, are
incomplete because they are based on effects observed in V1 that may
not be found in other cortical areas.
RESULTS
We studied 71 well-isolated MT units in 12 anesthetized, paralyzed
macaque monkeys. All cells had receptive fields centered within 25°
of the fovea, and most receptive fields were within 15°. We evaluated
the effect of adaptation on direction tuning by comparing the
response to full-contrast sine-wave gratings drifting in 16 test directions (in 22.5° steps) before and after 40 s of adaptation (Fig. 1). Topup adaptation stimuli (5 s) were presented between each pair of
Center for Neural Science, New York University, 4 Washington Place, Room 809, New York, New York 10003, USA. Correspondence should be addressed to A.K.
([email protected]).
Published online 13 June 2004; doi:10.1038/nn1267
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direction of the cell caused the peak response to decrease from 89
impulses per second (ips) to 72 ips. The response to nearby directions was reduced more strongly: from 48 to 4 ips at −22° and from
78 to 21 ips at +22°. For the cell in Figure 2b, the response to the
preferred stimulus increased after adaptation, whereas the response
to other directions was reduced.
To quantify the effect of preferred adaptation on direction tuning,
we fitted the pre- and postadaptation data of each cell with von Mises
functions (Methods). From these fits, we extracted three parameters:
the direction evoking the strongest response (preferred direction),
the full width of the peak at half height (tuning bandwidth) and the
difference between the maximum response and the spontaneous
activity (responsivity). The fits for pre- and postadaptation
responses for the cell shown in Figure 2a indicated that adaptation
had little effect on the preferred direction (a shift of 3.7°) or responsivity (reduced from 87 to 70 ips) but reduced the tuning bandwidth
from 62° to 32°. For the cell in Figure 2b, the preferred direction
shifted by 1.1°, responsivity increased from 32 to 39 ips and bandwidth decreased from 122° to 56°.
Our ‘preferred’ adapting stimuli were not always exact and could
be up to 20° from the true preferred direction. Although the preferred direction of most cells was not substantially altered by preferred adaptation, there was a slight tendency for the preferred
direction to shift toward the adapted direction: the mean shift was
2.3° ± 1.2° in the attractive direction (indicated by negative values in
Fig. 2c and hereafter; P = 0.07 for difference from a shift of 0).
On average, adaptation reduced the responsivity of MT cells
slightly. The geometric mean ratio of post- to preadaptation responsivity was 0.70 (Fig. 2d; P = 0.004 for difference from 1), with the
mean response reduced from 45 to 31 ips. Responsivity in nearby
directions, however, was reduced more strongly. For example, for
stimuli 12–33° from the preferred direction, the average response
decreased from 38 to 13 ips; and for stimuli 34–56° from the preferred
direction, it decreased from 24 to 6 ips (data not shown). The consistently stronger reduction in responsivity for nearby directions caused
a substantial and consistent narrowing of tuning bandwidth, which
Figure 1 Diagram of the adaptation protocol. Responses to gratings
drifting in 16 different directions were measured before and after
adaptation to a grating drifting in the preferred, flank or null direction for
40 s; the adaptation level was maintained by 5-s ‘top-up’ stimuli preceding
each test stimulus.
postadaptation test stimuli. The size, spatial frequency and drift rate
of the test, adaptation and top-up gratings were identical and were
optimized for each cell.
We explored the effect of adapting at a range of directions relative to
the preferred direction of MT cells. Below, we present the effect of
adapting near the peak preference of the cell, on the flank of the tuning
curve, or in directions that failed to evoke a significant response in the
cell. We conclude by combining these data sets to show how MT tuning is affected by adaptation over a continuous range of directions.
Preferred adaptation
We defined preferred adaptation to be when the adaptation grating
was within 20° of the cell’s preferred direction. For the MT cell
whose data are shown in Figure 2a, adaptation in the preferred
a
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Figure 2 Effect of adaptation in the preferred
direction on the direction tuning of MT cells.
(a) Direction tuning of an example MT cell before
() and after () preferred adaptation (direction
indicated by arrow). Dashed and dotted line
indicate the spontaneous firing rate before and
after adaptation, respectively. Adaptation reduces
responsivity slightly and causes a narrowing of
tuning bandwidth. Error bars indicate the s.e.m.
(b) Preferred adaptation in a second MT cell.
Responsivity in the adapted direction increases,
and tuning bandwidth decreases substantially.
(c) Histogram of shifts in preferred direction after
preferred adaptation for a population of MT cells
(n = 22). Arrowhead indicates the mean shift,
which is not statistically distinguishable from 0.
Negative values indicate a shift toward the
adapted direction (see text). (d) Distribution of
responsivity ratios (postadaptation/preadaptation),
which on average are below 1. (e) Distribution of
tuning bandwidth ratios (postadaptation/
preadaptation) shows that preferred adaptation
reduces tuning bandwidth.
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was on average about half as broad after adaptation as before (Fig. 2e;
mean bandwidth ratio of 0.54).
Flank adaptation
Our finding that the smallest response decrement occurs for stimuli that
are similar to the adapted direction suggested that adaptation in a direction displaced from the preferred direction (on the flank of the tuning
curve) would result in a shift of the preferred direction toward the
adapted direction. We evaluated the effect of flank adaptation on MT
neurons using stimuli 20–75° degrees away from the preferred direction
of the cells, choosing an adaptation direction for each cell that evoked a
response that was roughly one-half of the peak value (average 42%).
An example of the effect of flank adaptation is shown in Figure 3a:
adaptation had a negligible effect on the response to stimuli on the
adapted flank of the tuning curve, but there was a substantial reduction in responsivity on the opposite flank, causing the preferred direction to shift toward the adapted direction by 10.9°. After a recovery
period in which the tuning curve recovered to its original form, we
adapted the cell on the opposite flank and observed a shift in the
opposite direction of 9.5° (Fig. 3b). The effect of flank adaptation for
a second, exceptional cell is shown in Figure 3c: the response near the
adapted direction (−75°) increased strongly in this cell, causing an
attractive shift in the preferred direction of 18.3°.
The distribution of shifts in the preferred direction for our population of cells (n = 55; Fig. 3d) showed an average shift toward the
adapted direction of 9.3 ± 1.4° (P < 0.001). For 16 cells that were
adapted twice, once on each flank (Figs. 3a,b), the average shifts were
attractive for both conditions (12.1 ± 3.4°, P < 0.001; 6.7 ± 2.1°,
P = 0.003). We observed similar shifts when the direction preference
was calculated directly from the measured responses by using a vector
sum metric30 rather than the von Mises fits. Attractive shifts in the
preferred direction were produced by a well-maintained, or in some
cells enhanced (18 of 55 cells), response in the adapted direction and a
strongly reduced response for equally potent stimuli from the opposite flank (see below).
The reduction in peak responsivity was intermediate between the
effect on the two flanks (Fig. 3e; geometric mean ratio of 0.72;
P < 0.001). Finally, the direction-tuning bandwidth was reduced after
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flank adaptation, although the effect was substantially weaker than it
was for preferred adaptation (Fig. 3f; geometric mean ratio of 0.80; P <
0.001). We conclude that, as with preferred adaptation, the response is
reduced less strongly in the adapted direction than in other nearby
directions, thereby causing attractive shifts in direction tuning.
We evaluated the effect of adaptation by using a continuous stimulus sequence without temporal gaps between stimuli. We considered that our finding that responsivity was reduced least for
well-matched test and adapt stimuli might be due in some way to
stimulus transients at the transition from adapting stimuli to test
stimuli of different directions. To confirm that these transients were
not affecting our results, we verified in several control cells that similar effects occurred when we added a stimulus blank of 250 ms
before each test stimulus. We also found that removing the initial
300 ms of neuronal response to each stimulus made no systematic
difference to our results. Finally, in many cells the adapted direction
lay between two test directions so that each test stimulus involved a
similar transient (Fig. 3c).
a
Normalized response
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Proportion of cells
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Figure 3 Effect of flank adaptation on the
direction tuning of MT cells. (a) Direction
tuning of an MT cell before () and after ()
flank adaptation (direction indicated by arrow).
Tuning is shifted toward the adapted direction,
and responsivity and bandwidth are reduced.
(b) Adapting the cell shown in a on the
opposite flank of the tuning curve results in an
opposite shift in preferred direction.
(c) Adaptation in a second MT cell causes an
increase in responsivity on the adapted flank
and a strong attractive shift in the preferred
direction. (d) Distribution of shifts in the
preferred direction after flank adaptation for a
population of MT cells (n = 55). Arrowhead
indicates the mean shift, which is significantly
different from 0. Negative values indicate a
shift toward the adapted direction (see text).
(e,f) Flank adaptation also slightly reduces
responsivity (e) and tuning bandwidth (f).
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Figure 4 Effects of flank adaptation on responses to targets of different
contrast. (a) Population contrast response function before () and after ()
flank adaptation. Contrast sensitivity is reduced, but the response at full
contrast is maintained. (b) Population contrast response function for
stimuli on the unadapted flank shows that responsivity is strongly reduced
at all test contrasts, owing to a change in both contrast and response gain.
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Figure 5 Flank adaptation of V1 complex cells does not cause shifts in
tuning. (a) A V1 cell whose preferred direction is not altered by flank
adaptation (direction indicated by arrow). , before adaptation; , after
adaptation. (b) Population histograms for shifts in the preferred direction of
direction-selective (DS; n = 22) and orientation of non-direction-selective
(non-DS; n = 18) complex V1 cells after flank adaptation. There is, on average,
no shift in the tuning of V1 cells.
Effect of flank adaptation on contrast sensitivity
We have previously found that preferred adaptation reduces the contrast sensitivity or gain of MT cells, manifested as a change in the
range of contrasts that evoke a response, but that it has little effect on
responsivity (or response gain)29. To determine how flank adaptation
affects the contrast and response gain of MT neurons to targets moving in the adapted direction and in a direction on the opposite,
unadapted flank, we measured the response to stimuli of varying contrast (Methods) moving in either of these two directions before and
after flank adaptation.
The population contrast response function for targets moving in
the adapted flank direction, obtained by averaging the response of
11 cells after normalizing each to its preadaptation response to the
full-contrast stimulus, showed that the sensitivity to low-contrast
targets was reduced, whereas the response to full-contrast stimuli
was unchanged (Fig. 4a). Thus, the maintained response observed
in the adapted direction in our direction-tuning experiments does
not indicate that flank adaptation has no effect on the response to
stimuli moving in that direction. In the unadapted direction, contrast sensitivity was similarly reduced after adaptation: the minimal
contrast capable of raising the firing rate above the spontaneous
level increased from roughly 0.03 to more than 0.10 (Fig. 4b).
Unlike the effect observed on the adapted flank, however, the maximal firing rate evoked by high-contrast stimuli moving in the
unadapted direction was strongly reduced.
To compare the responsivity on the unadapted and adapted flanks,
we calculated response ratios (the postadaptation response on the
unadapted flank relative to that on the adapted flank) at each contrast
level that evoked a measurable response. These ratios were 0.31, 0.29,
0.32 and 0.55 for contrasts of 1, 0.5. 0.25 and 0.125, respectively. Thus,
the response on the adapted flank was two to three times stronger than
that on the unadapted flank after adaptation, indicating that attractive
shifts in direction preference occur at each of these test contrasts.
We conclude that flank adaptation reduces the contrast sensitivity
for all test stimuli, and that attractive shifts in direction tuning are due
to an additional reduction in response gain that occurs only for test
stimuli that are different from the adapter.
Effect of adaptation on tuning in V1
Because most previous studies have reported that adaptation causes
repulsive, not attractive, shifts of tuning in V1 (refs. 9–16), we recorded
4
from V1 cells to determine whether our adaptation protocol induced
shifts in V1 orientation tuning. We studied both direction-selective
(n = 22) and non-direction-selective (n = 18) complex cells in nine
macaque monkeys. We characterized direction-selective cells by the
same methods used for MT. For non-direction-selective cells, we fitted
von Mises functions to only the adapted lobe of the tuning curve.
An example of the effect of flank adaptation on a V1 directionselective complex cell is shown in Figure 5a. Responsivity on both the
adapted flank and opposite flank was reduced slightly, causing no
shift in the preferred direction. The effect of flank adaptation on the
preferred direction of our V1 population is shown in Figure 5b: nondirection-selective cells showed a small repulsive shift (2.4 ± 2.0°;
P = 0.12), whereas direction-selective cells showed a small attractive
shift (1.2 ± 2.4°; P = 0.31). There was a small difference between the
two populations, but it was not statistically significant (P = 0.14).
Although neither cell type showed significant changes in preferred
direction on average, some individual cells did show reliable repulsive
or attractive shifts.
The difference between the shift induced by flank adaptation in MT
(attraction of 9.3°) and in V1 (repulsion of 0.4° ± 1.6°) was statistically significant (P < 0.001), as was the difference when the comparison was limited to direction-selective V1 neurons (P = 0.003). We
conclude that our adaptation protocol shows a difference in the direction specificity of adaptation effects between MT and the V1 cells
from which the MT is likely to receive input.
End-of-flank and null adaptation
To evaluate the range of adapted directions that induce attractive shifts
in tuning, we adapted MT neurons with stimuli just outside the range
capable of driving the cell (‘end-of-flank’ adaptation). On average, the
adaptation direction in these experiments was 95° from the preferred
direction and evoked a negligible mean response of 2.2 ± 2.0 ips
(n = 17). For the cell shown in Figure 6a, adaptation at −93° had little
effect on direction tuning. The population histograms confirm that endof-flank adaptation had little effect on the preferred direction (Fig. 6b;
mean shift: attraction of 0.7 ± 1.8°; P = 0.69), tuning bandwidth (Fig. 6c;
mean ratio 1.07; P = 0.20) or responsivity of MT cells (Fig. 6d; mean
ratio 0.98; P = 0.35). These results suggest that attractive shifts in tuning
occur only for stimuli that lie within the tuning bandwidth of the cell.
MT cells are inhibited by motion in their null direction29,31,32. We
and others have previously shown that null adaptation reduces the
efficacy of this inhibition29,33, and we therefore evaluated whether
this reduction in inhibition affects the direction tuning of MT cells.
For the cell shown in Figure 6e, null adaptation had little effect on
direction tuning. On average, there was no change in the preferred
direction (Fig. 6f; average attractive shift of 0.2 ± 1.2°; P = 0.84;
n = 22; adaptation 135–225° from preferred) or responsivity (Fig. 6g;
response ratio 0.97; P = 0.28) after null adaptation.
The direction-tuning bandwidth of some MT cells increased after
null adaptation. This effect was not significant for the whole population (Fig. 6h; ratio of 1.04; P = 0.16; mean bandwidth increased from
81° to 86°), but when we considered only those cells that were suppressed below spontaneous firing by null motion, we found a significant broadening (ratio of 1.11; P = 0.001; n = 16). Finally, we
evaluated whether the reduced efficacy of inhibitory input after null
adaptation affected the response to a null stimulus and found that it
was unchanged: the mean response to null motion was 1 ± 2 ips
before adaptation and –2 ± 2 ips after (P = 0.30). We conclude that,
although null adaptation reduces the efficacy of null or opponent
inhibition29, this has only modest effects on the direction tuning of
most MT neurons.
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For convenient exposition, we divided the above data into categories according to the adapted direction (preferred, flank, end-offlank and null). In reality, the adaptation direction differed from the
preferred direction in a continuous manner. In Figure 7, we summarize the shifts in preferred direction and changes in tuning bandwidth
as a function of the adapted direction for all cells. Attractive shifts in
preferred direction are first apparent when the adapting direction
deviates slightly from the preferred direction (10–20°), reach maximal strength for adaptation roughly 45° from the preferred direction,
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Figure 6 End-of-flank and null adaptation in
MT cells. (a) Direction tuning before () and
after () end-of-flank adaptation (direction
indicated by arrow) for an MT cell. (b–d)
Distributions (n = 17) show no change in
preferred direction (b), tuning bandwidth (c) or
responsivity (d) after end-of-flank adaptation.
(e) Null adaptation has little effect on the
direction tuning of a second cell. (f–h)
Distributions (n = 22) of shifts in the preferred
direction (f) and changes in responsivity (g) and
tuning bandwidth (h) after null adaptation.
Tuning bandwidth increases slightly after null
adaptation, but preferred direction and
responsivity are unaffected.
Bandwidth ratio
and are absent for adaptation greater than 90° from the preferred
direction (Fig. 7a).
Considered individually, the shifts in direction preference were
significant in roughly half of the cells (28 of 55); all but one of these
shifts was attractive. In the remaining cells and across the population
as a whole, there was a clear bias toward attraction when the adapting
direction was between 15° and 75° from the preferred direction.
Bandwidth was reduced most strongly when cells were adapted
within 45° of the preferred direction (Fig. 7b; 17 of 22 preferredadapted cells were individually significant), whereas adaptation at
directions between orthogonal and null resulted in a slight increase
in tuning bandwidth.
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DISCUSSION
Most previous studies of neuronal adaptation to prolonged stimulation have been done in V1. In this structure, adaptation usually
reduces neuronal sensitivity and responsiveness most for stimuli that
are similar to the adapter, resulting in a postadaptation shift of tuning
curves away from the adapter9–16. Our results in area MT show
exactly the opposite effect: that is, prolonged adaptation to drifting
sine-wave gratings reduces the responsivity of MT neurons least when
the direction of the test stimulus is similar to the adapted direction.
As a result, preferred adaptation causes a narrowing of direction-tuning bandwidth, whereas flank adaptation causes both a narrowing
and a shift in tuning toward the adapted direction. The only other
similar result of which we are aware comes from a preliminary report
on the effect of adaptation on speed tuning in area MT of the awake
monkey, where responsivity is reduced least when the speed of the test
stimulus is matched to the adapter (B. Krekelberg, personal communication). Clearly, adaptation in MT produces effects unlike those
that have been previously documented in V1.
We thought that it was important to compare directly our MT
effects with effects in V1. In particular, we wanted to ensure that
our MT results were not due to our experimental protocol and to
compare our MT data with a sample of V1 cells enriched with
Narrower
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Adapting direction relative to preferred (deg)
Figure 7 Changes in MT direction tuning as a function of adapting direction.
(a) Shifts in preferred direction are shown for each cell. Preferred, flank,
end-of-flank and null adaptation are colored black, red, blue and green,
respectively. The black line shows a running average of the shift, calculated
by averaging the neighboring ±10 data points. Filled symbols show cells for
which the tuning shifts are significant (P < 0.05). (b) Bandwidth ratios as a
function of the adaptation direction, represented as in a.
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a
b
are likely to be inherited from V1 neurons29
because they have small receptive fields, are
Adapt
Adapt
known to undergo changes in contrast sensi1.0
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tivity35,36 and provide substantial input to
MT37. The spatial specificity of adaptation in
0.5
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MT is not consistent with a substantial
hyperpolarization of neurons after adapta0
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tion, as has been observed in cat V1 (refs.
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38,39), and it seems to rule out other mechaStimulus direction (deg)
nisms that globally alter the responsivity of
c
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an adapted MT cell.
Test
Test
One explanation for the effect of adaptation
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on MT direction tuning is that it involves a
change in the relative strength of excitation
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and inhibition received by adapted MT cells.
Another study has evaluated the consequences
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of altering the strength of recurrent excitation
Preferred direction of cell (deg)
and inhibition in a model of V1 orientation
selectivity40. Recurrent connections in that
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model have a ‘Mexican-hat’ configuration,
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such that each cell receives excitation from cells
with similar preferences and inhibition from
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cells with remote preferences. When adaptation is implemented as a reduction in the
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most strongly by the adapter, the responsivity
Stimulus direction (deg)
of these preferred-adapted cells decreases
slightly and their tuning bandwidth decreases
Figure 8 Changes in MT direction tuning after adaptation are consistent with perceptual effects.
substantially, owing to maintained inhibition
(a,b) Examples of tuning of model cells before (gray) and after (black) adaptation for two
from remote cells. For flank-adapted cells, the
adaptation schemes: ‘adaptation repels tuning’ (repulsion, a) and ‘adaptation attracts tuning’
loss of recurrent excitation causes a reduction
(attraction, b). Arrow indicates the adapted direction. (c,d) Population activity evoked by a test
stimulus at 45° (indicated by arrow) for each adaptation scheme. Repulsion causes a reduction in
in the response to all test stimuli, but the loss of
activity but no shift in its distribution. Attraction results in both a reduced response and a shift in
lateral inhibition from neighboring (preferredthe distribution of activity ‘away’ from the adapting direction. (e,f) Shifts in population response
adapted) cells offsets this effect on the adapted
for a range of test directions. Attraction, but not repulsion, gives rise to repulsive shifts in
flank. The net result is a slight decrease in tunperceived direction similar to those observed psychophysically.
ing bandwidth and a shift in preference toward
the adapted orientation, as we observed here in
MT. In essence, the model involves competidirection-selective complex cells—the cells that provide input to tion among cells via lateral inhibition. Cells that lose excitation are disMT from V1 (ref. 34). We confirmed that the effects of adaptation advantaged in this competition, enabling neurons with offset
on direction tuning in MT cells differ from those in complex V1 preferences to shift toward the region of reduced excitation.
Although this model mimics the effect of adaptation on MT tuncells. The primary effect of adaptation on our V1 cells was to
reduce neuronal responsivity with little change in preferred direc- ing, it relies on adaptation-induced alterations in recurrent circuitry.
tion. We are therefore confident that the effects that we observed in This seems inconsistent with our previous findings that adaptation in
MT cells do not arise from previously undetected properties of the MT is due to weakened feedforward input29, but a simple explanation
subset of V1 cells that relay directional motion signals.
could be that there is a stronger reduction in the feedforward input to
Although our V1 data do not show the significant repulsive tun- cells contributing to recurrent excitation than to those providing
ing shifts previously reported in some studies12–15, our cell sample recurrent inhibition. This could occur if the weakened feedforward
was relatively small and we could easily have failed to detect a small input involved synaptic depression that is stronger for excitatory than
shift in population tuning. Our V1 results are also in good agree- for inhibitory cells, a suggestion that is supported by in vitro studies41.
ment with those of a study16 using a similar experimental design. The basic behavior of the model can be also achieved by maintaining
That study found repulsive shifts in V1 orientation tuning only for the strength of recurrent connections and by reducing most the
cells in orientation ‘pinwheels’, and elsewhere in V1 adaptation strength of feedforward excitation to the cells tuned to the adapted
decreased responsivity without changing tuning. Because most V1 direction.
Notably, with this implementation of the model, we would predict
neurons are outside pinwheels, the main effect of adaptation in V1
may be to reduce responses, with only a fraction of cells undergoing that stimuli that fail to adapt V1 cells strongly will also fail to give rise
to attractive shifts in MT tuning. Preliminary experiments using prorepulsive shifts in preferred orientation.
longed adaptation with coherent dots support this prediction: adaptation with dots reduces the responsivity of MT cells but has little
Potential mechanisms
Our previous finding that changes in MT neuronal contrast sensitiv- effect on direction-tuning bandwidth or preferred direction (A.K.
ity are spatially specific led us to suggest that adaptation effects in MT and J.A.M., unpublished data). We have implemented these simple
Adaptation attracts tuning
Population response
before and after
adaptation
Shift in
perceived direction
after adaptation
© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience
Neuronal response
before and after
adaptation
Adaptation repels tuning
6
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© 2004 Nature Publishing Group http://www.nature.com/natureneuroscience
variants of the model and found that they can produce tuning
changes that are in qualitative agreement with our data. It is important to note, however, that the model relies on the assumption that the
feedforward input to MT is broadly tuned and then sharpened by
recurrent connections in MT. If the pool of neurons providing feedforward input to MT is selective, as suggested by antidromic stimulation experiments34, then a different explanation will be needed.
Implications for perception
Adaptation causes the perceived direction of subsequently viewed
stimuli to shift away from the adapted direction, the ‘direction aftereffect’ (DAE)2,6–8. A similar repulsive shift in perceived orientation, the
‘tilt aftereffect’ (TAE), has been studied extensively 1,2. To test whether
the adaptation effects that we observed in MT are consistent with
repulsive shifts in perceived direction, we made a labeled-line model
that computes perceived direction as the vector sum of the responses
of the model cells, where the ‘label’ of each cell is its preferred direction before adaptation and the weight of each cell is given by the magnitude of its response. We chose this simple framework because it is
intuitive and has been used previously to explore the perceptual consequence of changes in neuronal tuning and responsivity2,17,19.
Consider two schemes: first, adaptation reduces responsivity for
each cell most strongly for stimuli that are similar to the adaptation
stimulus, as is often reported for cells in V1 (‘repulsion’); and second,
adaptation alters tuning in the manner that we observe in MT
(‘attraction’). The tuning of model neurons before and after adaptation at 0° is shown for each scheme, along with the distributions
of activity in the population evoked by a test stimulus at +45°
(Fig. 8a–d). The activity histograms are constructed by plotting the
response of each cell to the test stimulus before and after adaptation.
In our particular simulation of repulsive shifts in tuning (Fig. 8c),
adaptation reduces the population response but it does not alter its
distribution. This is because repulsion is achieved by scaling the
response to each test direction of each cell by a common value
(Methods). By contrast, attractive shifts in tuning (Fig. 8d) cause the
distribution of activity to shift away from the adapted direction by
more than 20°. The main reason for this is that cells with preadaptation preferred directions in the range 15–45° shift their tuning toward
the adapted direction and ‘away’ from the test direction, thereby
reducing their response to the test stimulus; cells with preferred directions in the range 45–75° also shift their tuning toward the adapted
direction, which in this case is also toward the test direction, thereby
preserving their responses to the test stimulus. The effect is enhanced
by the narrowing of the tuning curves of cells with preferred directions
near 0°, which sharply reduces their response to the test stimulus.
The shift in the population vector, the model’s measure of perceived
direction, is shown for a range of test directions (Fig. 8e,f). Our particular simulation of repulsive shifts in tuning (Fig. 8a) predicts that
there will be no change in the perceived direction of any test stimulus
(Fig. 8e). By contrast, attractive tuning shifts (Fig. 8f) result in robust
repulsive perceptual shifts, as has been found psychophysically8.
The simulation shown in Figure 8 reflects one possible implementation of repulsive shifts in tuning, but its main features are
evident in a wide range of simulations that vary the degree to which
the preferred direction is repelled or attracted, the tuning bandwidth is altered, and the responsivity is scaled after adaptation. The
perceptual distortions predicted by simulations in which these factors were varied parametrically are shown in Supplementary
Figure 1 online. The results show that attractive shifts in tuning are
not required for repulsive perceptual effects: if adaptation reduces
responsivity enough, this alone can produce perceptual repulsion.
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The effect of reduced responsivity is enhanced when it is accompanied by attractive shifts and bandwidth narrowing, and it is weakened when tuning is repelled.
A similar conclusion has been reached in a model of the TAE:
strong adaptation-induced losses in V1 responsivity can overcome
the perceptual attraction predicted from repulsive tuning shifts
alone (D.J. Jin et al., Soc. Neurosci. Abstr. 29, 266.11, 2003; see also
ref. 19). In MT, however, the postadaptation reduction in responsivity is relatively modest, and the attractive shifts in tuning and the
reduction in tuning bandwidth are of the form and magnitude
needed to account for the DAE (which is roughly an order of magnitude larger than the TAE).
One might ask whether our experiments on anesthetized monkeys bear directly on perceptual aftereffects measured in alert
humans. Opiate anesthesia of the type that we use seems to have little effect on the response or adaptation properties of MT neurons:
both visual responses and adaptation are very similar in recordings
from alert and anesthetized animals42–44. In addition, in human
subjects, neither attention nor even visual awareness is required for
the creation of visual aftereffects45–47, although attention can modulate the magnitude of measured effects. We conclude that our
results are likely to be accurate replicas of the neuronal effects in
both alert monkeys and humans.
Finally, it is worth noting that the effects of adaptation in MT predict changes in both the perceived direction of a drifting stimulus (the
DAE) and the apparent motion of a static or motion-balanced stimulus (the ‘motion aftereffect’; MAE). Previously, we found that specific
types of MAE could be explained by a postadaptation imbalance in the
sensitivity of MT neurons tuned to opposite directions of motion29, a
suggestion that is consistent with our finding that preferred adaptation
reduces the responsivity of MT neurons, whereas null adaptation has
little effect on response. Null adaptation does, however, reduce the
effectiveness of inhibition elicited by null-direction stimuli and
thereby contributes to the MAE observed with compound stimuli
composed of preferred and null drifting gratings. The DAE does not
involve a direct comparison between preferred and null-adapted cells
or a change in the strength of null inhibition. Rather, it arises primarily
from shifts in preference and changes in the tuning bandwidth of cells
with preferred directions at and near the adapted direction.
Conclusion
In summary, we have identified an effect of adaptation in MT that is
different from any previously documented effect in V1. Rather than
repelling the direction tuning of MT cells, adaptation attracts their
tuning by a preferential reduction in responsivity for stimuli that are
different from the adapter. A simple population coding model shows
that the way in which adaptation alters MT direction tuning can readily predict the large repulsive shifts in perceived direction that are seen
psychophysically. Tuning changes of the type previously observed in
V1, by contrast, would produce little perceptual distortion, suggesting
that the perceptual DAE is likely to arise from changes in MT or other
areas downstream of V1. In addition to resolving discrepancies
between neurophysiological and psychophysical effects, our data
show that current theories20–23 and mechanistic explanations38,39,48
for adaptation observed in V1 convey only a partial picture of the way
in which cortex adjusts to variations in sensory input.
METHODS
Recordings. We recorded from ten cynomolgus (Macaca fascicularis), one
bonnet (M. radiatum) and one pig-tailed (M. nemestrina) adult male monkeys. The procedures used in our laboratory for single-unit recording in anes-
7
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ARTICLES
thetized (sufentanil citrate, 4–8 µg per kilogram (body weight) per hour),
paralyzed (vecuronium bromide, 0.1 mg per kilogram per hour) macaque
monkeys have been described in detail49. All experimental procedures were
approved by the New York University Animal Welfare Committee.
MT recordings were made with platinum-tungsten or tungsten-in-glass
microelectrodes through a craniotomy centered 16 mm lateral to the midline
and 2 mm posterior to the lunate sulcus; electrode penetrations were made at
an angle of 20° from horizontal. V1 recordings were made in the operculum
(where the receptive fields were within 5° of the fovea) and calcarine sulcus
(within 12–18° of the fovea), with vertical penetrations roughly 10 mm lateral
to the midline and 4–10 mm posterior to the lunate sulcus. Signals from the
microelectrode were amplified, bandpass-filtered (300 Hz to 10 kHz) and fed
into a hardware discriminator (Bak Electronics). Spike times were saved with a
temporal resolution of 0.25 ms. We made electrolytic lesions (2 µA for 5 s) at
the end of each recording tract and used published methods29 for histological
confirmation of the recording sites.
Visual stimuli. Stimuli were luminance-modulated, drifting sine-wave gratings presented at a frame rate of 100 Hz by using a 10-bit Silicon Graphics
board operating at a resolution of 1,024 × 731 pixels. The monitor (Eizo
T550) subtended about 22° of visual angle and had a mean luminance of
roughly 33 cd/m2. For each cell, we determined, in this order, the optimal
direction, spatial and temporal frequency, position and size of a drifting
sine-wave grating. Full-contrast gratings were presented to the dominant
eye in a circular aperture, surrounded by a gray field of average luminance.
Stimuli were presented in the classical receptive field, which was defined as
the smallest stimulus size that evoked a response no less than 95% of the
maximal response49, and did not extend into the receptive field surround.
We used two different protocols in our adaptation experiments. In the first
adaptation protocol, a trial consisted of a single randomized sequence of 1-s test
stimuli (16 directions spanning 360° in 22.5° steps), followed by a 40-s adapting
stimulus and a second sequence of test stimuli, each preceded by a 5-s top-up
stimulus. Each ‘test–adapt–test/top-up’ trial was followed by at least 3 min of
recovery (Fig. 1). We recorded three to five trials for each adaptation direction,
block-randomizing the presentation of each direction. In the second adaptation
protocol, we ran several sequences of test stimuli before and after adaptation,
with a single recovery period (∼15 min) between adaptation conditions. We
obtained similar results with the two protocols and pooled the results. In experiments evaluating the effect of adaptation on contrast sensitivity, we used the
same experimental design and recorded responses to seven test stimuli spaced in
equal logarithmic contrast steps between 0.016 and 1.0, presented in a randomized sequence. Spontaneous activity in all experiments was measured either by
using a blank stimulus interleaved with the test stimuli or during a brief epoch
immediately preceding and following the stimulus sequence.
Data analysis. We characterized direction tuning, both before and after
adaptation, by fitting the von Mises function (a circular approximation to
the gaussian function) to the mean response using the χ2 minimization
algorithm STEPIT50. The von Mises function is
ae b cos(θ – xc) + m
where a scales the height of the tuning curve, b determines the tuning bandwidth, xc is the location of the tuning curve peak, θ is the direction and m is the
spontaneous firing rate of the cell. Negative responses in the model were set to
zero. Because changes in the parameter b also affect the height of the curve, we
report alternative bandwidth and responsivity metrics extracted from the fits.
We removed 5 cells (of 71) from the data set that were poorly tuned and thus
not well fitted by the von Mises function. In the remaining data set, the fits
accounted for 92% of the variance in the data. We used bootstrap analysis to evaluate the significance of changes in tuning in individual cells. Specifically, for each
cell we combined all trials of pre- and postadaptation data. We then created 500
‘preadapt’ and ‘postadapt’ data sets by choosing random subsets of the data with
replacement. We fitted each of these 500 data sets in the same way that we fitted
the measured responses. Statistical significance was determined by the rank of the
measured values in the set of bootstrap fits.
All indications of variation in the graphs and text are standard errors of the
mean (s.e.m.). The statistical significance of all results was evaluated with t-tests.
8
Simulations. To simulate the perceptual effects of adaptation, we used a
labeled-line model consisting of 720 cells, spanning 360° in 0.5° increments,
with the maximum response of each cell set to 1 and with a tuning bandwidth
matching the average for our MT population.
In the first scheme (‘repulsion’), adaptation reduced the response of each
cell most strongly in the adapted direction. We implemented this effect by
multiplying the tuning of each cell by an inverted gaussian function (standard
deviation, s.d., 83°) whose minimum (0.5) lay in the adapted direction and
whose value was ∼1 for directions located 2 s.d. away from the adapted direction. We chose the shape and minimum value of this function so that the
changes in tuning observed after flank adaptation would approximate those
previously reported in V1 (refs. 12–15). Specifically, peak responsivity was
reduced most strongly in cells that were best matched to the adapter, and the
preferred direction of flank adapted cells shifted away from the adapter.
In the second scheme (‘attraction’), we modeled the effect of the adaptation
in MT by using the measured shifts in preferred direction and changes in tuning bandwidth and responsivity. We obtained similar predicted perceptual
effects in the model when the population response was read out by a ‘winnertakes-all’ rule or by maximum likelihood estimation (by fitting a von Mises
template to the response).
Note: Supplementary information is available on the Nature Neuroscience website.
ACKNOWLEDGMENTS
We thank W. Bair, S. Solomon, E. Simoncelli and N. Rust for comments on the
manuscript; M. Smith and N. Majaj for assistance with data collection; and M. Hou
and N. Doron for histology. This work was supported by a grant from the National
Institutes of Health (EY02017) and by an Howard Hughes Medical Institute
Investigatorship to J.A.M.
COMPETING INTERESTS STATEMENT
The authors declare that they have no competing financial interests.
Received 8 March; accepted 29 April 2004
Published online at http://www.nature.com/natureneuroscience/
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