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First published online August 3, 2006
Journal of Experimental Biology 209, 3170-3182 (2006)
Published by The Company of Biologists 2006
doi: 10.1242/jeb.02369
Visual stimulation of saccades in magnetically tethered Drosophila
Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA
* Author for correspondence (e-mail: jbender{at}caltech.edu)
Accepted 5 June 2006
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Summary |
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Key words: Drosophila, saccade, vision
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Introduction |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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; Collett and
Land, 1975; Egelhaaf and Kern,
2002; Schilstra and van
Hateren, 1999). Land (Land,
1999) suggested that these so-called `body' saccades might be
analogous to visual saccades in humans and other primates, and noted that
similar periods of stable gazing interspersed with rapid shifts have been
observed across three phyla. One proposed function of visual saccades in
primates is to quickly move the fovea, with its high spatial resolution but
limited spatial extent, around a large visual field to process key features
(after Yarbus, 1961).
Separate from the functional utility of saccades, their underlying neural
basis is of interest because they represent rapid and robust responses to
environmental stimuli. An analysis of free flight trajectories in different
visual environments suggested that visual expansion can trigger saccades in
free flight (Tammero and Dickinson,
2002a). Tethered flies exhibit saccade-like behaviors
(Heisenberg and Wolf, 1979;
Wolf and Heisenberg, 1980;
Tammero and Dickinson, 2002b),
but because these events are much longer, it is not known whether these
fictive turns are analogous at the neurobiological level to free flight
saccades. In an attempt to clarify this, Mayer and co-workers
(Mayer et al., 1988) tethered
flies to a flexible filament in such a way that the flies could rotate about
their yaw axis but were otherwise fixed in space. Flies in this arrangement
exhibited saccade-like behaviors, but those authors could not identify a
visual stimulus sufficient to evoke them, which obscures a clear connection
between the free flight behavior and its putative tethered flight counterpart.
Further, fictive saccades in rigidly tethered Drosophila last
approximately 500 ms (Heisenberg and Wolf,
1979; Tammero and Dickinson,
2002b), around ten times longer than free flight saccades
(Fry et al., 2003), and the
duration is nearly independent of any visual feedback. The most likely
explanation for the difference in time course between saccades in free flight
and in tethered flight is a role for sensory feedback in terminating the
saccade motor program, but the modalities responsible for this feedback have
not been identified. The halteres, the modified hindwings of flies that act as
gyroscopes (Pringle, 1948;
Nalbach, 1993), are a likely
source, but their involvement has not been explicitly examined. In addition,
whereas the torque produced during fictive saccades is unidirectional, an
analysis of free flight saccades shows that flies must generate countertorque
to terminate each turn (Fry et al.,
2003), again suggesting an important role for sensory feedback
that is not present in tethered flight.
To address the issue of whether free flight saccades and fictive turns in
tethered flight share the same neurobiological foundations, we have developed
a novel behavioral paradigm. We tether a fly to a steel pin placed within a
magnetic field, allowing the fly to rotate freely about its yaw axis. This
arrangement provides naturalistic sensory feedback from yaw rotation, which is
mediated by the visual system and the halteres
(Dickinson, 1999). Using this
preparation, we show that the rapid turns observed under these magnetically
tethered conditions are quite similar to free flight saccades and thus likely
result from the same motor program. We also controlled the visual environment
using an electronic panorama to directly test whether and how certain visual
stimuli elicit saccades. The results show that looming objects evoke saccades
with a probability independent of the object's shape. The timing of saccades
relative to a virtual collision depends on the object's velocity in a manner
similar to the visual threshold model proposed by Gabbiani and co-workers
(Hatsopoulos et al., 1995;
Gabbiani et al., 1999;
Gabbiani et al., 2001) based
on recordings from single neurons in locusts, but is also consistent with
models based on integration of elementary movement detectors
(Borst, 1990).
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Materials and methods |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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; Mayer et al.,
1988). The fly was illuminated from below with a circular array of
940 nm light-emitting diodes (LEDs), and a mirror was fixed underneath the fly
to reflect an orthogonal view of the fly's ventral side to a near-IR-sensitive
digital camera (A602f, Basler, Ahrensburg, Germany) running at 101 frames
s-1.
This arrangement of fly, magnets, light source and mirror resided within a
cylindrical arena composed of 32x64 green LEDs. Each LED subtended
approximately 5.6° in the horizontal plane, and the interior of the arena
had a total diameter of 8 cm and a height of 13 cm. The LED arena and
associated control board were as described by M. B. Reiser and M. H. Dickinson
(manuscript in preparation), except that the visual display instructions
emanated directly from a computer with no bias or correction by the control
board. The pattern was updated at 50 Hz, but was refreshed on the LED array at
800 Hz. The visual arena provided only a coarse spatiotemporal simulation of
expansion, since Drosophila's flicker-fusion rate is probably around
200 Hz (Autrum, 1958;
Laughlin and Weckstrom, 1993)
and the interommatidial angle is 4.6°
(Götz, 1964). This is a
potential area of concern, but two main lines of reasoning suggest the
sufficiency of our stimulating apparatus for the purposes presented here.
First, locust single neurons showed no differences in responses to looming
stimuli presented using varying video refresh rates down to 67 Hz
(Gabbiani et al., 1999).
Second, even if our stimuli are suboptimal, they do evoke behavioral responses
that discriminate between similar stimuli (see Results).
Data collection and calibration
We fine-tuned the digital processing of the images from the camera for each
fly before each experiment to produce a nearly binarized image of a white fly
on a black background. The center of the fly's outline in the camera image and
the fly's orientation in each frame were saved for later analysis. A potential
source of noise in our data lies with our ability to precisely determine the
fly's orientation by this method. We estimated this error by tracking a dead
fly for 1 h. The standard deviation of the orientation values under these
conditions was in the range 1-2°; thus, we expect that the error in our
measurement of the orientation of the flies during our experiments is also of
this order. The moving wings of the live flies should not add significantly to
this uncertainty because the shutter speed of the camera was too slow to
visualize them.
At the beginning of a flight sequence, each fly was first subjected to a
calibration phase to determine the fly's center of rotation in camera
coordinates. This calibration period consisted of 1 min of visual stimulation
by a horizontal square wave pattern with a fundamental spatial frequency of
45°. The motion of the pattern simulated a vertical pole of expansion and
a pole of contraction separated by 180°. Under these conditions, flies
robustly avoid the pole of expansion
(Tammero et al., 2004). We
rotated these poles around the arena at about 120° s-1. Because
the center of the fly in the camera image was offset from the fly's center of
rotation, the centroid from each frame traced a circle as the fly spun on the
tether. The center of this circle defined the fly's center of rotation in
camera coordinates. Determination of this point allowed our online tracking
software to unambiguously calculate the fly's heading with respect to the
camera.
Another caveat related to tracking is that the relative position of the camera and the visual arena were slightly different from animal to animal. This occurred because the camera and arena had to be moved between experiments in order to insert and remove flies from the apparatus. We estimate this error to be on the order of ±5°, constrained by the physical size of the arena and the field of view of the camera. This places a lower bound on our ability to discriminate the orientation of the stimulus relative to that of the fly, but not the motion of the fly itself.
Visual stimulation
After the calibration period, we presented each fly with a
22.5x22.5° (4x4 pixel) dark square centered vertically at a
fixed azimuthal position on a light background. The square was programmed to
simulate an object with a half-size of 10 cm approaching the fly from 1 m away
(Fig. 1C). We varied the
parameters of this virtual object's approach to assess the effects on the
fly's responses. In the first set of experiments (N=11 flies), we
tested three conditions: the virtual object approached with either constant
velocity, constant acceleration, or constant deceleration. Because the object
started at the same virtual position in all cases, we varied its initial
velocity in order to keep the trial duration constant. For the
constant-velocity trials, the simulated speed of the virtual object was 1.5 m
s-1 towards the fly; in the acceleration trials, the initial
velocity was 0 and the object accelerated at 6.2 m s-2 towards the
fly; in the deceleration trials, the object began at 3.4 m s-1 and
accelerated at -5.3 m s-2 (Fig.
1D). In the second set of experiments (N=9), the object
could change shape as it approached: expanding in either the horizontal or
vertical dimension only, or expanding along one diagonal axis
(top-left/bottom-right), but maintaining the same surface area as the
horizontal or vertical expansions. These stimuli allowed us to test whether
the responses were specific to motion along a particular axis. In these
trials, the expansion time course was the same as in the constant velocity
experiments for the first set of trials. In other words, these stimuli were
identical to the full, expanding square with v=1.5 m s-1,
except that they were masked such that only a part of the square showed
through (i.e. the square was viewed through a vertical, horizontal or diagonal
slit) and are quantified in terms of this underlying square. For a third set
of experiments (N=15), the object was programmed to approach at one
of two fixed velocities (1.0 or 2.0 m s-1), different from the
velocity used in the previous experiments. In the final set of experiments,
the object was not solid, but rather consisted of a series of alternately dark
and light concentric squares radiating outward. The spatial frequency of the
pattern across these squares was always 22.5°, and the outermost square
had the same expansion time course as an object approaching at 1.5 m
s-1. This pattern was designed to enhance the stimulation of
putative Reichardt-type motion detectors in the fly's visual system
(Reichardt, 1961) while
maintaining the same approach geometry as in the other experiments.
In each trial, we triggered one of the different expansion paradigms every 10 s. Once the object reached its full size (180° of azimuth and 117° of elevation; 32x32 pixels), it remained at that size for about 5 s. For the first set of experiments, the square immediately changed back to its original size at that time. In the other trials, the object contracted back to its original size with the same time course as that with which it expanded (Fig. 4A). In all cases, the initial and final conditions of each trial were identical - the original 22.5x22.5° dark square on a light background. However, because the fly was free to rotate around its yaw axis, the square might sit at any azimuthal position relative to the fly when stimulation occurred. Within each experiment, the order of the trials was selected randomly ad hoc, with the restriction that two successive trials did not use the same stimulus condition.
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When measuring the probability of saccade occurrence in response to a stimulus, we looked for saccades in a 500 ms window beginning 30 ms after the start of the stimulus. However, in the trials in which the object's velocity was 1.0 m s-1, this brief window did not include the time of the virtual collision. For these trials, we used a 500 ms window beginning 280 ms after the beginning of the stimulus. Visual inspection of the saccade distribution over the course of the stimulus did not suggest a significantly different saccadic frequency during the initial 280 ms than during a similar period in which there was no stimulation (data not shown). If there was more than one saccade in the 500 ms window, only the first was analyzed. By these criteria, we observed a total of 2933 saccades that we assigned as having been triggered by visual expansion. To estimate the spontaneous saccade rate, we calculated the probability of a saccade occurrence in a 500 ms window beginning 3 s after a stimulus presentation.
We used a k-fold cross-validation technique (with k=10) to estimate saccade metrics (amplitude, duration and peak angular velocity) from various stimulation parameters. In this analysis, we divided the data randomly into 10 blocks (i.e. k=10). For each block, which represented 10% of the entire data set, we fit a second-order polynomial to the other 90% of the data and then evaluated the predictive ability of this model on the current, excluded block, using the mean-squared error (MSE) of the prediction to measure its precision. We then compared the mean of these 10 MSE measurements with the naïve MSE (the overall variance for a particular saccade metric) to determine how much of the behavioral variation could be explained by each stimulus parameter. Although a monomial model had slightly less predictive power, increasing the order of the polynomial model beyond second-order did not improve the predictions (data not shown).
Modeling of saccade initiation
Gabbiani and co-workers (Gabbiani et
al., 1999; Gabbiani et al.,
2001), visually simulating objects approaching with a constant
velocity, reported that the locust motion-sensitive neuron DCMD responded at
its peak firing rate with a constant delay after a looming stimulus reached a
fixed critical size. They note that the time course of visual stimulation in
this paradigm depends only on the quantity l/v, the ratio of
the object's half-size (l) and its velocity v, irrespective
of whether the object is a circle or a square. Their data fit tightly to the
linear relationship:
 | (1) |
where tpeak was the time of the DCMD neuron's peak
firing rate and 
was the delay between the time the stimulus reached
critical size (tcrit) and tpeak. The
coefficient 
is related to the critical angle, 
crit,
by:
 | (2) |
The linear relationship between tpeak and
l/v means that crit is constant.
Although we used only three values of l/v with our
constant-velocity stimuli, we utilized the fact that l/v
varied monotonically during the accelerating and decelerating stimuli to
estimate and , using the peak in behavioral response
probability rather than neural firing rate to determine
tpeak. To do this, we first calculated the probability of
saccade initiation as a function of post-stimulus time. We then filtered this
function using a 5 Hz low-pass Butterworth filter and found its peak time,
tpeak (Fig.
8A). Using the values of l/v at
t=tpeak for the accelerating and decelerating
stimuli and the constant values of l/v for the other square
stimuli, we calculated and . However, for the accelerating and
decelerating stimuli, the value of l/v changed between
tcrit and tpeak. Because
represents this delay, we replaced l/v in the regression
matrices with the values of l/v at
t=(tpeak-) and recalculated and
. We performed this transformation iteratively until was
changing by less than 0.01 ms - generally within 5-10 iterations.
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Results |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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), who used
a flexible filament to tether their flies.
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;
Tammero and Dickinson, 2002a),
where these turning events are termed `body saccades'. We set a threshold 4
s.d. away from the mean angular velocity, at ±350° s-1,
to separate saccades from free flight. We calculated the duration and
amplitude of saccadic events in our preparation (see Materials and methods;
Fig. 2B) and compared the
co-distribution of those parameters with previous observations made using a
filament tether (Mayer et al.,
1988) (Fig. 3).
Again, there is a good qualitative match between data sets (see Discussion).
The amplitude, duration and peak angular velocity of these saccades are well
approximated by log-normal distributions. The mean absolute amplitude using
this distribution was 35.2±1.6°, the mean duration was
78.5±1.4 ms, and the mean peak absolute angular velocity was
637.8±1.4° s-1. The relationship between peak velocity
and amplitude is tighter than that between duration and amplitude
(Fig. 3; Pearson coefficient
=0.59 for amplitude/duration, =0.70 for amplitude/peak velocity),
but duration and peak velocity are uncorrelated (=0.06).
Saccade stimulation parameters
Mayer and colleagues did not identify a stimulus that elicited saccades
(Mayer et al., 1988). However,
recent free flight observations (Tammero
and Dickinson, 2002a) and experiments using a rigid tether
(Tammero and Dickinson,
2002b), suggest that visual expansion may trigger saccades.
Therefore, we presented our flies with visual stimuli simulating the approach
of a dark, square object (see Materials and methods;
Fig. 1). Accelerating,
decelerating and constant-velocity (v=1.5 m s-1) stimuli
all evoked saccades at a probability significantly higher than that due to the
spontaneous saccade rate (ANOVA, P<0.01)
(Fig. 4B).
We investigated the directional sensitivity of the visual response in
separate experiments in which the square expanded only along one axis
(vertical, horizontal or diagonal). For comparison, we also presented these
flies with a full expansion stimulus (v=1.5 m s-1).
Because some prior experiments on rigidly tethered animals are ambiguous as to
whether flies avoid expansion or fixate contracting stimuli
(Tammero et al., 2004), the
stimuli in these trials were presented as contractions as well as expansions.
Halfway between expansion trials, the stimulus would contract back to a small,
dark square with the reciprocal time course to that with which it expanded
(Fig. 4A). Under these
conditions, all expansion stimuli reliably evoked saccades
(P<0.01) (Fig. 4C).
In contrast, the saccade rate following stimulus contraction was not different
from baseline.
A third set of flies was presented with either a square approaching at constant velocity (at either 1.0 or 2.0 m s-1) or with a concentrically striped square approaching at 1.5 m s-1. Again, each expanding stimulus elevated the probability of observing a saccade well above baseline (P<0.01) (Fig. 4D). In this case, we observed a significantly lower saccade rate following contraction of the low-velocity square and concentric square stimuli (P<0.05), suggesting that contraction stimuli may weakly inhibit saccades under some conditions. Pairwise comparisons between the expansion-triggered saccade probabilities for all of the stimulus types did not yield any significant differences (ANOVA with Bonferroni correction for multiple comparisons, P>0.05 for each pair).
We further calculated the probability of saccade initiation as a function
of time from the beginning of the stimulus
(Fig. 5). The time course of
stimulus expansion strongly affects the time course of saccade probability,
which is consistent with results reported for rigidly tethered flies
(Tammero and Dickinson,
2002b). The partial (horizontal, vertical and diagonal) stimuli
elicit saccades close to the time of virtual collision
(Fig. 5A-C), whereas the full
square stimulus with the same expansion time course tended to evoke saccades
earlier (Fig. 5D). Compared to
the stimulus with v=1.5 m s-1
(Fig. 5D), the faster-moving
stimuli are correlated with a later peak in saccade probability
(Fig. 5E,H), and the
slower-moving stimuli are associated with earlier saccade activity
(Fig. 5F,G). The peak in
saccade probability resulting from the concentric stimulus occurred earlier
than that elicited by the simple square with the same time course
(Fig. 5D,I). These results are
difficult to reconcile with a time-to-contact avoidance response; however, a
more quantitative relationship between stimulation and response timing will be
discussed later.
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Next, we assessed the probability of saccade initiation according to the
orientation of the stimulus relative to the fly
(Fig. 6Aiv). Considering all
expansion stimuli together, the position of a stimulus on the fly's retina
strongly affects the likelihood that the stimulus will induce a saccade.
Frontal stimuli evoked saccades with higher probability than stimulation from
behind. In addition, the response probability is slightly smaller for a
stimulus directly in front of the fly than for a stimulus located to one side
of center. A similar effect was observed for response latency in rigidly
tethered flies when stimulated by square stimuli with a linear angular
expansion (Tammero and Dickinson,
2002b): latency was shortest to off-center, frontal stimulation,
slightly longer to frontal stimuli, and longest to rearward expansion. That
study also found that the probability of the landing response, as indicated by
foreleg extension, peaks in response to a frontally centered expansion
stimulus. Our data are compatible with their interpretation that off-center
visual expansion evokes a turning response, whereas center-symmetric expansion
independently elicits a landing response and a delay or suppression of the
turning response. The other statistically significant responses we found - the
repression of saccades following low-velocity and concentric contraction
stimuli - did not show any dependence on stimulus position (data not
shown).
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), it is not known how stimulus parameters might affect
saccade dynamics. Specifically, we estimated how stimulus position (azimuthal
orientation relative to the fly), stimulus angular size, stimulus edge angular
velocity, time from the start of expansion, and time to `collision' might
affect the dynamics of visually triggered saccades. Grouping all expansion
stimuli together, Fig. 6i-iii
shows the relationship between these factors and our three saccade metrics of
choice: duration, amplitude and peak velocity.
We used k-fold cross-validation of a second-order polynomial model
to determine the relative contributions of each factor to the final value of
each metric (see Materials and methods). The only factor with any predictive
value was the stimulus position relative to the fly
(Fig. 6A). Saccade amplitude
and peak angular velocity were largest when the stimulus was directly in front
of the fly and smallest when the stimulus was behind the fly, varying smoothly
in between. Knowledge of this parameter explains 
10% of the observed
variance in those saccade metrics, but does not improve a prediction of
saccade duration. None of the other factors tested had more than 1%
predictive value.
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Saccades in tethered flight
Although saccades in both freely flying and rigidly tethered
Drosophila occur as discrete events easily discernible from straight
flight, the intersaccade trajectories in free flight are not always straight
(Tammero and Dickinson, 2002a;
Frye et al., 2003) [for
analyses in other species, see also van Hateren and Schilstra, and Boeddeker
et al. (van Hateren and Schilstra,
1999; Boeddeker et al.,
2003)]. This raises the question of whether the rapid, fictive
turns of tethered animals represent true saccades or gradual turns, and leaves
unclear the extent to which they share similar neurobiological foundations
with the free flight behaviors. It has been proposed that saccades in free
flight can be triggered by visual expansion
(Tammero and Dickinson,
2002a), and presentations of visual expansions to rigidly tethered
flies do indeed elicit turning responses
(Tammero and Dickinson,
2002b). However, these `torque spikes' are of much longer duration
than free flight saccades (Heisenberg and
Wolf, 1979; Tammero and
Dickinson, 2002b; Fry et al.,
2003).
Consideration of body dynamics suggests that free flight saccades require
the generation of torque to begin the turn and countertorque to stop
(Fry et al., 2003). Rigidly
tethered flies, however, never generate countertorque. Using a dynamic model,
we were able to estimate the time course of torque produced by our flies
during magnetically tethered saccades. The model predicts the fly's torque
(
) from the time course of its angular position (
), using its
moment of inertia (I) and frictional damping constant (C),
by the equation
.
Fry and co-workers (Fry et al.,
2003) estimated I=5.2x10-13 Nm
s2 and C=5.2x10-13 Nm s based on body
morphology, but we adjusted I to compensate for the effects of
tethering. The center of rotation for our flies (the tethering point) is
forward of their center of mass, about which they would normally rotate. We
modeled the fly as a cylinder of constant density with a moment of inertia
about its center of mass approximately equal to the body morphology estimate
[cylinder mass=1.25 mg, radius=0.4 mm, and length (L)=2.5 mm].
Rotating this cylinder about an axis corresponding to the tethering point (at
l=L/6) instead of its center of mass
(l=L/2) almost precisely doubles its moment of inertia.
Using this corrected value of I=1.0x10-12, our model
predicts a substantial countertorque phase
(Fig. 7). However, it also
indicates a peak torque about ten times smaller than that estimated for free
flight saccades (Fry et al.,
2003). There are two possible explanations for this discrepancy.
First, our dynamic model may utilize inaccurate values of I and
C. The value of I should be reasonably accurate, given that
the fly's mass and geometry are known. The frictional coefficient is more
suspect, but to explain the low torque seen here, the value would need to be
350 times larger than earlier calculations based on Stokes' Law
(Fry et al., 2003), which
seems unlikely even if the pin/bearing joint in our setup introduces some
additional friction. Direct measurements (J. A. Bender and M. H. Dickinson,
manuscript in preparation) suggest that the value of the time constant
(I/C) for a magnetically tethered fly is no smaller than
about 0.25 s, which bounds C at no larger than 4I
s-1.
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;
Götz, 1987), in which the
wings touch at the peak of the upstroke. These kinematics provide some
additional lift (Lehmann et al.,
2005) and create a large pitch moment
(Fry et al., 2005), but are
not normally seen in free flight (Fry et
al., 2003). This suggests that, like rigidly tethered flies,
magnetically tethered animals may be constantly attempting to pitch down,
which could compromise their ability to create yaw torque during saccades
(Lehmann and Dickinson, 2001).
Furthermore, free flight saccades involve rotation around the pitch, roll and
yaw axes (Schilstra and van Hateren,
1999; Fry et al.,
2003), and such rotation may be important in producing the full
change in heading seen in free flight. Limiting rotation about the functional
pitch and roll axes would reduce the net change in heading generated by the
same motor program, and thus the torque estimated by our model. We do, in
fact, observe a mean saccade amplitude of 35°, instead of the 90°
turns seen in free flight (Tammero and
Dickinson, 2002a).
In Drosophila, free flight saccades have a duration of 50-70 ms
(Fry et al., 2003), but the
fictive behaviors in tethered flight last for 300-500 ms
(Heisenberg and Wolf, 1979;
Tammero and Dickinson, 2002b).
One likely hypothesis that explains this difference is that although both can
be evoked by similar stimuli, the termination of the saccade motor program
depends on input from the halteres or other fast sensory modalities not
engaged on a rigid tether. Indeed, using our magnetic tether, we observed
saccade durations of 60-90 ms, quite comparable to free flight values. Earlier
observations on loosely tethered flies
(Heisenberg and Wolf, 1979;
Mayer et al., 1988) show
results qualitatively similar to ours (Fig.
3). However, for a given turn duration, the mean amplitude of
turns in those experiments was smaller than in our magnetically tethered
preparation. One possible explanation for these differences is that flies
tethered to a filament were encumbered by more rotational friction than in our
experiments - in order to reduce torsional stiffness, Mayer and co-workers
needed to use a 30 cm long, 10 µm string, which was actively spooled as the
fly turned, yielding a torsion constant of 3x10-11 N m per
revolution. In addition, they tethered their flies in a horizontal plane,
while ours were inclined by 30° to better replicate free flight posture
(David, 1978;
Fry et al., 2003). Because
saccades in free flight can involve rotations about all three body axes, the
effects of restraining motion to a single plane will depend on the orientation
of that plane relative to the body.
Finally, although our flight arena was designed to give the fly relatively
normal feedback from the halteres about the functional yaw axis, the halteres
are substantially less sensitive to yaw than to pitch and roll
(Sherman and Dickinson, 2003).
Therefore, it is likely that the quality of the mechanosensory feedback
received by our flies during saccades is less than in free flight, yielding
expected saccade dynamics somewhere in between those seen under rigidly
tethered and free conditions.
In summary, we conclude that the behaviors we report here are closely
related to free flight saccades, as are the fictive saccades observed in
rigidly tethered flies. They are stimulated in the same way, and we find that
the responses lie along a continuum correlated with the physical rotation
undergone by the flies. Because the halteres serve as a gyroscope, encoding
angular velocity (Pringle,
1948; Nalbach,
1993), they are a logical source for the feedback responsible for
the behavioral differences. Indeed, if the neurobiological underpinnings of
free flight and tethered flight saccades are the same, the only way to
reconcile the differences in the time course of torque production is a role
for sensory feedback in the termination of the saccade motor program.
Insights into underlying neural activity
Although previous work has shown that visual expansion can trigger saccades
(Tammero and Dickinson, 2002a;
Tammero and Dickinson, 2002b),
it is unlikely that this is the only sensory stimulus that can do so. We
observed a spontaneous saccade rate of approximately 0.4 Hz, similar to that
observed by Heisenberg and Wolf in rigidly tethered Drosophila
(Heisenberg and Wolf, 1979).
In addition, there is reason to suspect that olfactory stimuli affect the rate
of saccade generation both in free flight
(Frye et al., 2003) and in
tethered flight (Frye and Dickinson,
2004). Therefore, even if saccades are a stereotyped response, it
is likely that they are elicited through multiple sensory pathways. Here, we
attempt to clarify only how a subset of visual stimuli can induce saccades.
Virtual objects approaching with different shapes and velocities have
approximately equal probabilities of evoking saccades
(Fig. 4), but the time course
of stimulation affects the time course of saccade probability
(Fig. 5). What, then, are the
features of the stimulus that determine when a saccade is triggered?
There are two fundamentally different ways in which flies might determine when a collision is imminent. First, they could monitor the perimeter of objects in their environment, and initiate an evasive maneuver when the perimeter exceeds a certain critical value. This scheme presumes that an object has reasonably clear boundaries that can be discriminated from the visual background. Such a process would disregard information about the internal texture of an object, but show a dependency on object shape. Second, the fly could integrate motion over a large patch of visual space and initiate a saccade when the summed motion reaches some threshold. This latter process does not require that the object be distinct, with a clear boundary, but does depend on its contrast, as well as the luminance of the visual field. It further depends on the field size and time over which the local motion is integrated. While we will consider the two models separately for the moment, it is important to note that they are not mutually exclusive. It is conceivable that the fly could estimate an object's perimeter from the motion signals generated during approach. Likewise, a perimeter detection circuit with a time-varying threshold could give rise to properties similar to the motion-integration model. It was not our intention to discriminate experimentally between these two collision-avoidance models, but we will briefly discuss our results in the light of each.
Implementations of both models have been described in birds
(Sun and Frost, 1998) and in
insects. The motion-integration model has received much attention in fly
vision literature. It is known that single cells in the lobula plate exhibit
responses to pure motion (Egelhaaf et al.,
1989; Borst and Egelhaaf,
1989), and specifically to some patterns of motion encountered by
flying insects (Krapp and Hengstenberg,
1996; Franz and Krapp,
2000). The sensitivity of these neurons is thought to arise from
the spatially integrated output of Hassenstein-Reichardt (delay-and-correlate)
elementary motion detectors (EMDs)
(Hassenstein and Reichardt,
1956) somewhere in the visual lobes. One behavior thought to
involve EMD integration is the landing response
(Borst, 1990), which is
sensitive to changes in contrast, as the model predicts.
Supporting the perimeter-threshold model, however, Gabbiani and co-workers
(Gabbiani et al., 1999;
Hatsopoulos et al., 1995)
described the response of a descending motion-sensitive neuron (DCMD) in the
locust to an approaching virtual object. They found that the DCMD neuron's
peak spike rate occurs with a constant delay after the stimulus reaches a
critical angular size. Santer and colleagues
(Santer et al., 2005)
additionally demonstrated that the flying locust initiates a diving response
that is correlated with the output of this neuron. Further, the time of DCMD's
peak firing rate shows no dependency on stimulus texture, contrast or position
(Gabbiani et al., 2001),
consistent with the predictions of a perimeter detector model.
We tested the ability of the perimeter model to describe our data by using
the theoretical framework developed by Gabbiani and colleagues (see Materials
and methods). Briefly, their model describes the constant delay, ,
between the time the stimulus reaches a critical angle,
crit, and time of the peak spike rate in the DCMD neuron. We
used the time course of saccade initiation probability
(Fig. 8A) as a proxy for the
spike rate of a putative DCMD homolog in Drosophila, with data from
our solid square stimuli only (Fig.
8B, filled circles). The model predicts a linear trend, and our
data fit this prediction quite well. None of the measured behavioral values
differ from the model's prediction by more than two sampling bins (of 10 ms
each). We iteratively arrived at values of =49 ms and
crit=62° with an r2 value of 0.91.
These behavioral observations are of the same order as the range of analogous
values seen in single neurons of the locust (=5-40 ms,
crit=15-40°, varying across individuals)
(Gabbiani et al., 1999;
Gabbiani et al., 2001), and
the 20-30 ms delay reported in the chasing response of the house fly
(Land and Collett, 1974;
Collett and Land, 1975).
Because there is evidence of the existence of EMDs in the fly brain but no
reported instance of a neuron directly responding to object perimeter, it is
prudent to examine the predictions one might make if the angular threshold
calculation were made using the spatiotemporal integration of underlying EMDs.
In such a scenario, the Hassenstein-Reichardt model predicts that stimuli with
concentric stripes should activate more EMDs than a uniform object would. We
did, in fact, find that the time of peak saccade probability for the
concentric square stimulus (Fig.
8B, green circles) was slightly earlier than that predicted by the
Gabbiani model, although still within two sampling bins. Thus, our results do
not provide an unambiguous answer, but the direction of the effect is the same
that one would predict if the critical angle computation were, in fact, made
based on EMD output. Gabbiani and co-workers
(Gabbiani et al., 2001)
performed this same experiment on locusts, with approximately the same result.
They found a slight effect in the predicted direction (earlier peak activity),
but the effect was not statistically significant. Our data also corroborate
this earlier study in another way; smaller stimulus shapes with identical
expansion geometry evoke later peak activity. Gabbiani and colleagues
demonstrated an increased value of crit for circular as
opposed to square objects, and our data show much later saccade activity in
response to our partial (horizontal, vertical and diagonal) stimuli (orange
circles). The responses to these stimuli with different shapes but identical
area and perimeter are indistinguishable, based on our data. Again, these
effects are what one would expect if the angular threshold were calculated
from EMD output. Therefore, both models might be useful in explaining our
data, and there is other evidence for the existence of both in the
Drosophila saccade system. While saccades and landing responses seem
to be evoked by similar motion signals
(Borst and Bahde, 1988),
Tammero and Dickinson (Tammero and
Dickinson, 2002b) demonstrated that the two behaviors are
controlled independently. On the other hand, they also demonstrated that
saccades in free flight can be explained by a large-field expansion threshold
(Tammero and Dickinson, 2002a)
and that rigidly tethered flies attempt to turn rapidly in response to
environmental expansion even in the absence of visual `objects'
(Tammero et al., 2004).
If an angular computation is being performed by Drosophila, what
neuron might serve as the homolog to locust DCMD? Many neurons project from
the motion-sensitive visual areas of the brain to the flight control
circuitry. In other dipteran insects (Calliphora erythrocephala and
Sarcophaga bullata), these neurons number at least 50 pairs
(Strausfeld and Gronenberg,
1990), few of which have been physiologically characterized
(Gronenberg and Strausfeld,
1990). In Drosophila, only one pair of descending neurons
- the giant descending neurons - have been studied in any detail
(Levine and Tracey, 1973;
Nachtigall and Wilson, 1967;
Tanouye and Wyman, 1980).
Single spikes in these neurons can initiate flight
(Levine, 1974;
Tanouye and Wyman, 1980;
Trimarchi and Schneiderman,
1995; Lima and Miesenbock,
2005), but are not thought to function during flight.
An intriguing possibility is that the haltere equilibrium system is
co-opted for the purpose of saccade initiation. One function of the halteres
is to sense and initiate corrections to high-frequency angular deviations
(Dickinson, 1999;
Sherman and Dickinson, 2003).
However, since the halteres are evolutionarily modified hindwings, in addition
to stroke muscles, their anatomy includes direct control muscles, similar to
the steering muscles of the forewings
(Bonhag, 1948;
Mickoleit, 1962). Chan and
colleagues (Chan et al., 1998)
found that these haltere control muscles can be activated by visual input in
the blowfly (Calliphora vicina). Therefore, perhaps saccades are
initiated by efferent haltere input, using the intrinsic, reflexive
counterturn response normally mediated by the halteres as an active turning
mechanism. This could eliminate the necessity for a form of efference copy to
be received by the halteres in order to prevent the normal, equilibrium
response from counteracting a saccade command. In addition, it could provide
an explanation for the high degree of stereotypy observed in saccades.
Methodical manipulation of the sensory feedback received by flies during
saccades is one set of experiments which would help further address the
question of whether ongoing sensory feedback plays a role in modulating
saccade dynamics.
crit
crit
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Acknowledgments |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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P<0.05, relative to the spontaneous rate. Total
n=2933 saccades. The error bars represent the s.e.m.; the dotted line
shows the spontaneous probability.
