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First published online August 31, 2007
Journal of Experimental Biology 210, 3218-3227 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.007807
The spatial, temporal and contrast properties of expansion and rotation flight optomotor responses in Drosophila
Department of Physiological Science, University of California, Los Angeles, CA 90095-1606, USA
* Author for correspondence (e-mail: frye{at}physci.ucla.edu)
Accepted 13 July 2007
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Summary |
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Key words: vision, optic flow, insect flight, motor control, wing kinematics
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Introduction |
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).
Gaze stabilization results in a moving visual panorama being actively fixed on
the retina by compensatory head or eye movements. The input to gaze
stabilization, the retinal movement patterns termed optic flow, is highly
complex and contains cues generated by forward translation as well as
rotation. There is some evidence that the brain processes these two flow
fields independently (Sandini et al.,
2001; Dahmen et al.,
2001). Indeed, the optic flow fields generated by translation and
rotation are very different, with the first resembling diverging and
converging lines of longitude on a globe, and the latter resembling parallel
lines of latitude. We still have a relatively poor understanding of how these
flow fields are processed and transformed into appropriate gaze stabilizing
motor commands.
For flying insects, experimental rotation of the visual panorama results in
the animal actively turning in the direction of motion. This robust behavioral
attempt to minimize retinal slip comprises a classical `optomotor' response
thought to help maintain stability in the face of external perturbations such
as a gust of wind, or internal perturbations such as bilaterally asymmetric
motor output (Collett, 1980;
Götz and Wandel, 1984;
Götz, 1964;
Heisenberg and Wolf, 1984).
Behavioral optomotor responses, as well as their electrophysiological
correlates within motion processing interneurons in the brain, show distinct
tuning curves for the spatial, temporal and contrast structure of moving
images (Buchner, 1984;
Götz, 1975;
O'Carroll et al., 1996).
A recent study of optomotor responses in fruit flies explicitly compared
optomotor responses to rotation and translation stimuli and reported that
panoramic patterns of image expansion/contraction centered laterally
(approximating a visual stimulus generated during a side-slip maneuver)
triggered optomotor responses that were three times stronger than responses to
a rotating panorama of identical spatial and temporal structure
(Tammero et al., 2004). This
increase in gain emerges because the directional optomotor response to motion
restricted to the rear hemisphere is reversed compared to frontal hemisphere
motion. Thus, counterclockwise motion across the front, coupled with clockwise
motion across the rear, produces a strong counterclockwise steering response
oriented away from the focus of expansion (centered at the animal's right side
in this example). The time course and magnitude of full-field rotational
optomotor responses are nearly identical to the arithmetic sum of half-field
expansion responses. Are rotation and expansion optomotor responses controlled
by a single expansion-sensitive circuit that is sub-optimally stimulated by
full-field rotation cues? Alternatively, are optomotor responses mediated by
separate optomotor control systems tuned specifically for rotation and
expansion cues, respectively?
In the present study we tested the hypothesis that the spatial, temporal and contrast sensitivity of optomotor responses vary for rotational versus translational flow fields, which would support the idea that these motor responses are indeed distinct and thus mediated by separate and parallel pre-motor visual processing pathways. Using a tethered flight simulator, we measured the time course and amplitude of yaw torque wing kinematics in response to systematic variation of spatial frequency, temporal frequency, vertical pattern size and contrast for optic flow fields that differ only in gross spatial organization. Our results show that spatial and temporal frequency sensitivity is similar for rotation and expansion optomotor responses, suggesting common elementary motion detection. However, impulse–response dynamics, spatial integration properties and contrast sensitivity vary markedly for the two stimulus types, suggesting that after the earliest stages of motion coding, the two patterns of optic flow are processed by separate and distinct control systems, most likely comprising separate neuronal circuitry.
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WBA) for the left and right wings,
as well as total wing stroke frequency for each individual stroke. The
difference in amplitude between the left and right wings (
WBA) is
directly proportional to yaw torque
(Götz, 1987;
Tammero et al., 2004), and the
cubed product of total stroke amplitude and frequency is roughly proportional
to the aerodynamic power output during flight
(Dickinson et al., 1998). These
values are digitized by a standard PC data acquisition system, and also
relayed to control the velocity of the LED display under closed-loop
conditions.
Visual motion stimuli, acquisition and data analysis
Apparent motion was generated by panoramic patterns of vertical stripes
moving horizontally. We examined responses to two motion patterns. The first
was a classical optomotor stimulus consisting of a rotating striped `drum'.
For this stimulus, the pattern simply rotated at constant velocity around the
fly in a clockwise direction (viewed from above), therefore the velocity
profile was constant along 360° of azimuth. The second stimulus was
identical to the rotating drum except that the direction of motion in the rear
visual hemisphere was reversed, forming a pseudo-translation flow field. This
pattern of image motion produced a focus of expansion centered 90° to the
left of the animal, and a focus of contraction 90° to the right. In other
words, the pattern expanded from the left and contracted to the right of the
fly (Fig. 1B). The azimuthal
velocity profile follows a square-wave trajectory rather than following a
smooth sinusoidal trajectory. This stimulus produces strong motion cues near
the poles of expansion and contraction, and we therefore refer to this
stimulus as an `expansion' cue. This large-field visual expansion stimulus
elicits robust steering responses in Drosophila
(Tammero et al., 2004). During
flight, expansion generated on the animal's left as well as clockwise rotation
resulted in increased WBA, which is tightly correlated with rightward
yaw torque. In related experiments, we tested for any influence of side-bias
by periodically inverting the direction of visual expansion and rotation.
Consistent with many studies of optomotor behavior, we found no significant
difference between responses to leftward or rightward motion.
The patterns used here vary as a square-wave of intensity along the
azimuth, not sinusoidally. As such, there is significant frequency content
above the fundamental spatial frequency defined by the grating period.
However, in fruit flies the square-wave does not interfere with the perception
of motion responses to the fundamental wavelength. Experiments related to
these using similar gratings have revealed that neither the time course nor
the magnitude of optomotor flight responses vary between a square-wave pattern
and a smooth sinusoidal pattern of the same wavelength
(Duistermars et al., 2007). The
relative insensitivity to high frequency components of a square-wave pattern
likely reflects the well-known spatial low-pass characteristics of
Drosophila's photoreceptor optics as well as the temporal low-pass
characteristics of motion processing pathway through the brain, pre-motor
pathways and musculoskeletal system.
We first described the spatial and temporal sensitivity of expansion and
rotation responses by systematically varying the spatial period of the
projected visual pattern and its velocity. The stimulus regime was composed of
periods of open-loop large-field expansion or rotation test stimuli
interspersed with periods during which the fly had active closed-loop control
of a 30° vertical stripe. This stimulus regime insured that flies were
actively engaged in optomotor behavior when the test patterns were presented.
This was done to insure that each fly was tested under similar optomotor
control conditions – in this case fixating a vertical stripe. For all
experiments, closed-loop periods lasted 5 s and test periods lasted 3 s each.
Test stimuli consisted of a sequence of four increasing velocities repeated
for five consecutive spatial period patterns, both for expansion and rotation
stimuli. Thus each fly was stimulated with a set of 4x5x2=40
different stimulus conditions – expansion and rotation of five spatial
period patterns at four velocities. The spatial patterns were composed of (i)
*=15° spatial period with a 75–25%, light:dark
duty cycle, (ii) =15°, 50:50 duty cycle, (iii) =30°,
50:50 duty cycle, (iv) =60°, 50:50 duty cycle, (v)
=90°, 50:50 duty cycle. At 50:50 duty cycle, the 15° grating
was represented by two ON pixels and two OFF pixels. Thus, for each image
displacement, the pattern moved 1/4 wavelength. We therefore included the
* grating to control for spatial aliasing. Velocity test
values were 10, 169, 232 and 431° s–1. These spatial
period and velocity combinations correspond to temporal frequencies ranging
from 0.11 Hz to 35.4 Hz. We systematically increased velocity, instead of
randomizing them, in order to explicitly test for any significant temporal
hysteresis. We found no such effects, probably because the flies had a
significant duration of closed-loop motion control (roughly 1000 individual
wingbeats, roughly the equivalent of 25 free-flight saccades) in between open
loop tests to eradicate any influence of time-history.
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=30° at 232° s–1). We then constructed
expansion and rotation patterns that varied systematically in the intensity of
the `light' and `dark' parts of the pattern. Contrast, estimated by the
Michelson definition as the difference between `on' and `off' LED intensity
values (Imax, Imin, respectively)
divided by the sum of `on' and `off' values
[(Imax–Imin)/(Imax+Imin)].
We tested optomotor responses to 27 unique contrast values, which were
shuffled randomly, and presented in expansion and rotation. We repeated these
contrast response experiments under two conditions: contrast unadapted and
same-contrast adapted. For the unadapted condition, flies were presented with
the stripe under closed-loop control prior to the open-loop test stimulus. As
such, there was no prior exposure to the test contrast. To examine the
influence of exposure to the test contrast prior to the test, the usual
vertical stripe was replaced with the wide-field test pattern at the selected
contrast during the closed-loop period, followed by the open-loop test. Thus,
for the same-contrast adapted treatment, there was 7 s of exposure to the test
contrast level prior to the test itself.
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),
which are therefore omitted for clarity.
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Time series data including the instantaneous azimuthal position of the
visual pattern and stimulus waveform, raw left and right wing stroke amplitude
and wingbeat frequency were digitized at 500 Hz (Axon Instruments DigiData
1320, Sunnyvale, CA, USA) and stored on a PC workstation. All analyses were
performed using custom software routines written in Matlab (Natick, MA, USA).
The raw wingbeat amplitude signals were low-pass filtered at 200 Hz with a 5th
order zero-phase digital Butterworth filter. Optomotor responses reach steady
state within roughly 1 s, thus for each 3 s test stimulus cycle, we measured
the maximum WBA value within the first 1.5 s of the test (roughly 300
wing beats) to quantify response amplitude (R). Data were then
normalized to the highest value within each trial
(R/Rmax).
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The product of spatial frequency (cycles deg.–1,
reciprocal of spatial period) and velocity defines the frequency that moving
stripes pass over the eye (temporal frequency in cycles s–1).
Mean WBA shows a characteristic tuning profile with respect to temporal
frequency. For both expansion and rotation stimuli, responses were fairly
consistent between 0.1 and 0.6 Hz, then rose steeply to a plateau between 3
and 10 Hz before rolling off at 30 Hz (two-way repeated measures ANOVA
P<0.001, Table 1).
We tested the statistical significance for both the specific treatment (e.g.
vertical extent, contrast) and also for the comparison between expansion and
rotation. The rotation and expansion stimuli produced similar temporal
frequency tuning curves, but rotation responses were attenuated by 20% across
the entire range of temporal frequencies
(Fig. 3, P<0.001
Table 1). To highlight the
similarity of temporal tuning between the two visual treatments, we fitted a
Gaussian curve (Srinivasan et al.,
1999) to the log-transformed expansion data using a least-squares
optimization method. We then used the amplitude and position coefficients from
the expansion fit and allowed only an offset parameter to be scaled for the
rotation data set. R2 values were similar for both fits
(0.82 and 0.89, respectively) with the rotation curve being offset downward on
the y-axis by 20% compared to the expansion curve. These results
suggest that whereas the gain of optomotor responses is higher for an
expansion flow field, the temporal frequency optimum is the same for both
expansion and rotation. We pooled the data across spatial wavelengths under
the assumption that the optima are similar, but without further experiments we
cannot be certain that this is the case. Nevertheless, even if the optima do
vary across spatial wavelength, the variation is qualitatively similar for
expansion and rotation (Figs 3,
4), which would support our
central claim that spatio-temporal tuning is similar for the two flow fields,
whereas response magnitude is generally shifted downward.
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We next explored how optomotor response magnitude is influenced by the
vertical extent of pattern motion. This was done by first horizontally
`scanning' the visual field with a 1-pixel row of moving stripes to identify
the most sensitive vertical region (Fig.
5A), at which location the pattern was extended vertically in
random increments between 1 pixel (3.75°) and 32 pixels (120°). On
average, for the 1-pixel row, optomotor steering responses were strongest near
the visual equator (Fig. 5B).
The mean response trajectories for each of 16 different vertical pattern sizes
are color-coded and overlaid in Fig.
5C. These data indicate three things. First, as has been reported
previously (Tammero et al.,
2004), and indicated in Fig.
2, response trajectory varies strongly between expansion and
rotation stimuli. At the onset of constant-velocity motion, expansion
responses rise quickly, plateau, then slowly decay, whereas rotation responses
rise slowly and instead of decaying continue to rise to the termination of the
stimulus. Second, consistent with prior results
(Tammero et al., 2004), the
passband characteristics for stimuli on the frontal and rear visual
hemispheres varies markedly. Whereas the frontal stimulus results in a
low-pass response trajectory characterized by a rapid onset, syndirectional
plateau, and phase-locked decay in the steering reaction during stimulus
presentation, motion restricted to the rear hemisphere elicits a response with
a lower frequency cut-off (Fig.
5C inset). Third, mean response maxima for the expansion stimulus
continue to increase with increasing vertical pattern size (color-coded
waveforms in Fig. 5C), but
rotation properties, and by implication the underlying neural mechanisms, of
expansion-mediated and rotation-mediated optomotor responses are different
(Fig. 5D, P<0.001
Table 1).
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To further examine the time course of optomotor responses, we stepped the visual stimulus in 3.75° increments at approximately 2 increments s–1. The individual image steps were clearly apparent to a human observer. Each 3.75° image expansion displacement resulted in a rapid turning response phase-locked to the motion cue (Fig. 6). By contrast, the identical stepwise motion of the full-field rotating pattern did not elicit phase-locked torque responses, but rather elicited only a steadily increasing response that is more consistent with smooth pattern motion. These results indicate that rotation responses show low-pass characteristics that are largely absent in expansion responses.
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=30° at a velocity of 232° s–1 for 3 s.
Pattern contrast ratio varied between 0.008 and 0.93. As discussed above, we
ran two experiments: the first was for flies that were tested without any
prior exposure to the tested pattern contrast (contrast unadapted) and the
second for flies that were presented with the test contrast prior to the test
(same-contrast adapted). The results indicate that for both unadapted and
adapted conditions, response magnitude varies according to both the contrast
and the expansion versus rotation structure of visual motion,
(two-way repeated measures ANOVA P<0.001,
Table 1). Expansion
consistently elicited larger optomotor responses. For the unadapted flies, low
contrast rotation and expansion elicited similar response magnitude
(Fig. 7), and as contrast
increased expansion responses saturated at 0.3, whereas rotation responses
were lower in magnitude, increased monotonically with contrast, and saturated
at near 0.93 contrast, 20% lower magnitude than for expansion
(Fig. 7, left). For the
contrast-adapted flies, the rotation responses were similar to the unadapted
condition, but showed slight but insignificant elevated responses across
contrasts. Strikingly, the expansion responses showed significantly stronger
responses at low contrast (Fig.
7, right).
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), the steepness
(ß) and the rightward shift (
) of the functions, respectively. For
the unadapted rotation function =0.83, ß=–5.6, and
=–0.1, R2=0.77. For the unadapted expansion
function =0.95, ß=–26 and =0.52,
R2=0.85. For adapted rotation =0.85,
ß=–5.7 and =–0.15, R2=0.6. For
adapted expansion =0.94, ß=–71 and =–0.04,
R2=0.13 (note that the low R2 results
from the wing steering responses being independent of contrast due to full
saturation). Thus, the rotation response functions saturate at roughly 80% of
the expansion response levels, rise roughly 4 times slower with increasing
contrast, and are shifted to the right on the contrast axis. Contrast
adaptation results in near-immediate saturation of the expansion response
function, whereas there is very little change in the rotation function,
indicating a strong separation of the two responses at low contrast
levels. |
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30°
(Fig. 5), suggesting distinct
spatial integration properties. Second, steering `spike' responses to
intermittent rapid image displacements are conspicuous for an expanding flow
field, but not for a rotating one (Fig.
6), suggesting distinct low-pass characteristics. Third, expansion
responses persist and are near maximal even under very low contrast
conditions, particularly after pre-exposure to the test contrast level
(Fig. 7), implying separate
contrast sensitivity to expansion and rotation.
Relating optomotor behavior to motion detection circuits
Within the earliest stages of motion processing, the apparent direction and
strength of image motion is thought to be determined by the spatial separation
of visual sampling units such as neighboring ommatidia as well as the delay
time constant imposed between two units prior to temporal correlation
(Hassenstein and Reichardt,
1956). As such, the magnitude of electrophysiological responses in
motion processing neurons as well as yaw torque reactions are bounded by
separable spatial and temporal frequency-sensitivity functions reflecting the
properties of the elementary motion detectors (EMDs)
(Borst and Egelhaaf, 1993;
Egelhaaf and Borst, 1993;
Srinivasan et al., 1999). As
an abstract model, each EMD is characterized by a unique spatio-temporal
frequency function surface. Therefore, optomotor behaviors maximally sensitive
to a single spatial wavelength and single temporal frequency very likely draw
from a common pool of EMDs. Our results show that the temporal frequency
optima for both expansion and rotation optomotor responses lie between 3 and
12 Hz (Fig. 3), and the spatial
wavelength sensitivity saturates at =30°. These values are
consistent with findings for yaw torque flight optomotor responses in house
flies (Borst and Bahde, 1987;
Reichardt, 1966) and blow
flies (Wehrhahn, 1985), as
well as for walking fruit flies (Buchner,
1984), and support the parsimonious hypothesis that the same
system of EMDs underlies optomotor responses to both flow fields during flight
in fruit flies.
In larger flies, it is thought that input from the retinotopic array of
local EMDs and their neuronal correlates is spatially pooled by neurons of the
third optic ganglion to construct neural responses to global patterns of optic
flow (Higgins et al., 2004;
Single and Borst, 1998).
Tangential cells of the lobula plate (LPTCs) show spatial integration
properties tuned to the orientation and direction of wide-field movement
across the retina and play a crucial role in the guidance of optomotor
responses (Hausen, 1982;
Krapp et al., 1998). Thus,
examining how optomotor behavioral responses vary with the extent of motion
projected across the retina can be used to estimate the extent of EMD
integration and, by extension, the underlying system of LPTCs
(Borst and Bahde, 1987). We
found that varying the vertical size of the moving pattern had different
effects on the expansion and rotation optomotor responses during flight.
Whereas rotation responses saturated at roughly 30° pattern size,
expansion response amplitude continually increased with increasing pattern
size (Fig. 5). These results
support the conjecture that the two flow fields are processed by separate
ensembles of LPTCs with different, but perhaps overlapping, vertical receptive
fields.
The temporal properties of optomotor steering responses to dynamic stimuli
have been used to correlate behavior with specific LPTC circuits. House flies
(genus Calliphora) show sluggish yaw torque and steering muscle spike
modulations in response to panoramic image rotation by comparison to the rapid
responses elicited by small object motion
(Egelhaaf, 1987;
Egelhaaf, 1989). These
behavioral responses correlate tightly with the membrane responses of
wide-field horizontal system cells (HS) and small-field feature detection
cells (FD) of the lobular plate (Egelhaaf
et al., 1988), implying that HS participates in relatively slow
wide-field optomotor reflexes, whereas FD participates in object tracking or
body saccades with a much shorter time constant. Here, we show that
intermittent displacement steps of a laterally expanding image results in
rapid high-amplitude torque responses, whereas a rotating image produces only
gradual syndirectional shifts in the steering signal, but without rapid
fluctuations (Fig. 6). We take
this as evidence that the expansion optomotor response is low-pass filtered
with a shorter time constant than the rotation response, owing to separate
pre-motor motion processing pathways with different temporal dynamic
properties. One important caveat: we know little of motion processing in
Drosophila, but the horizontal and vertical system cells show similar
anatomical architecture, with perhaps fewer wide-field cells
(Scott et al., 2002).
In addition to the spatial and temporal frequency characteristics, the
detection of visual motion depends upon the periodic contrast of moving
images. The sensitivity to image contrast of either simulated EMDs, LPTC
recordings or behaving flies is non-linear such that at a given spatial
wavelength, response amplitude shows a quadratic dependence at low contrast
and saturates at higher contrast levels
(Buchner, 1984;
Dvorak et al., 1980;
Harris et al., 2000). Flight
optomotor responses also show a saturated-quadratic functional dependence of
image contrast (Dvorak et al.,
1980), but the shape of the response functions differ in that
expansion sensitivity saturates at a higher value, shows a steeper rise, and
is shifted rightward on the contrast axis by comparison to rotation
(Fig. 7). Do these different
parameters suggest separate underlying pathways? Individual LPTC neurons (HS)
display motion adaptation that effectively shifts the sensitivity curve
rightward on the contrast axis, but does not alter the shape of the response
function (Harris et al.,
2000). Thus, it is certainly possible that the rotation saturation
level is `clipped' by a rightward shift in contrast sensitivity within a
single neuronal circuit responding with higher gain to expansion than
rotation, but that does not account for the further change in the shape of the
two curves. Nevertheless, a stronger separation between the expansion and
rotation responses to image contrast emerges after flies are exposed to the
stimulus contrast pattern for some time prior to the actual open-loop test. We
refer to this treatment as `same-contrast adaptation', and it results in a
strong increase in contrast sensitivity (particularly to low contrast) within
expansion responses but no significant change in rotation responses
(Fig. 7). Contrast dependence
of visual motion detection has been attributed to the saturation
nonlinearities prior to or within the EMDs
(Egelhaaf and Borst, 1989),
and may minimize the dependency on specific contrast by LPTCs
(Dror et al., 2001). Yet, we
are suggesting here that the two control systems have common local movement
detectors, so where does the variation in contrast sensitivity for rotation
and expansion optomotor behaviors come from? The simplest hypothesis is that
the behavioral contrast sensitivity is layered upon the control systems by
circuits postsynaptic to the LPTCs. For example, pre-motor descending
interneurons for the two optomotor responses may pool from LPTCs with varying
EMD input and explicit contrast sensitivity. Nevertheless, at the very least,
these results suggest that the sensitivity to image contrast is qualitatively
different for responses to large-field patterns of rotation and expansion,
which can be parsimoniously explained by the presence of two parallel
pre-motor large-field expansion and rotation circuits with different intrinsic
sensitivity to image contrast.
What are the possible parallel visual circuits for expansion and rotation
selectivity? Whereas the reconstructed receptive fields of some LPTCs appear
to be matched for spatially complex panoramic optic flow patterns generated by
flight maneuvers such as pitch and roll
(Krapp, 2000;
Krapp and Hengstenberg, 1996),
there are as yet no reports of LPTCs that specifically encode either patterns
of image expansion centered laterally. The equatorial horizontal system cell
(HSE), once thought to encode panoramic rotation, fails to do sounder
naturalistic optic flow conditions (Kern
et al., 2001). Since LPTCs show extensive heterolateral
connections (Haag and Borst,
2002), it seems reasonable that complex optic flow patterns are
encoded by groups of LPTCs with distinct but overlapping receptive fields.
Postsynaptic to the LPTCs, premotor descending visual interneurons may
assemble spatially sensitivity for expansion and rotation, and convey this
information to relevant steering and power muscle motor networks. Whereas it
is certainly the most studied visual neuropile in the fly, the lobula plate is
by no means the only place where motion circuits reside. Expansion-sensitive
interneurons have been reported within the optic lobes and midbrain in locusts
(Gabbiani et al., 1999;
Judge and Rind, 1997), mantids
(Kral and Prete, 2004), and
hawkmoths (Wicklein and Strausfeld,
2000). Finally, it is at least conceivable that the two optomotor
behaviors reflect two systems of EMDs having different directional
sensitivities converging onto a common pre-motor descending pathway. This
scenario would suggest that the differences in response dynamics must emerge
entirely from the EMDs, which is not likely. Whatever the underlying visual
circuit – as yet undisclosed – it would appear that large-field
rotation and expansion mediated optomotor responses are indeed separate and
distinct behaviors, serving different roles for flight control, and
coordinated by separate parallel pathways.
Advantages of separate rotation and expansion control systems
Maintaining dynamic optomotor equilibrium, avoiding approaching obstacles
or tracking visual objects, require encoding these environmental features
correctly. Separate neural circuits dedicated to expansion and rotation
stimuli ensure response specificity under different sensory conditions. For
example, the high-gain expansion circuit may mediate rapid collision avoidance
and escape from approaching predators, whereas the rotation circuit may
mediate stability during slow flight or hovering. Consistent with the
steady-state optomotor responses described here, under dynamic stimulus
conditions large-field expansion generates larger steering responses than
rotation at a wide range of motion frequencies, suggesting that the expansion
response generally operates at higher gain than rotation responses
(Duistermars et al., 2007).
There is some corroborating free-flight behavioral evidence for the
operation of two distinct control systems. During free flight, fruit flies
execute segments of straight flight interspersed with rapid 90° turns
called saccades (Tammero and Dickinson,
2002a). Saccadic motor patterns are used by animals as diverse as
humans (Land, 1992) and house
flies (Schilstra and van Hateren,
1998) to minimize the corrupting influence of motion blur and
maintain stable gaze. During free flight, Drosophila saccades are
threshold-triggered by a monotonic increase in the magnitude of contralateral
large-field retinal expansion, but not rotation
(Tammero and Dickinson,
2002a). However, between ballistic expansion-elicited saccades,
the flight path is not exactly straight but rather is slightly curved
depending on the proximity of the nearest wall (although the direction of the
curved turn is away from the closest wall, not toward it, so the turn cannot
be explained by a purely syndirectional rotational optomotor reflex). Curved
flight paths are readily observed within visual environments composed of high
contrast horizontal stripe, which generate strong vertical motion cues without
strong horizontal motion cues (Frye and
Dickinson, 2007). Recent evidence from blow flies suggests that it
is during these inter-saccade flight segments that the visual system encodes
the spatial layout of the visual environment
(Kern et al., 2005).
Expansion responses themselves mediate several important behaviors during
flight. Results obtained with tethered Drosophila show that visual
processing is likely further segregated into parallel collision avoidance and
landing behaviors. A laterally expanding object elicits rapid and robust
steering responses oriented away from the focus of expansion. By contrast, the
same stimulus presented within the frontal field of view elicits a leg kick,
without a steering response, thought to comprise an attempt to land. Both the
spatial and temporal properties of these two reflexes strongly implicate the
operation of parallel underlying circuits
(Tammero and Dickinson,
2002b). Theoretical analyses have suggested that visual expansion
selectivity can provide an unambiguous cue to dynamically maintain an upwind
flight posture (Reiser et al.,
2004). Electrophysiological recordings from the cervical
connective in blowflies have revealed a group of expansion-sensitive
descending pre-motor interneurons that likely mediate landing responses
(Borst, 1990). There are scant
physiological analyses of identified descending neurons in flies [but see
Gronenberg and Strausfeld (Gronenberg and
Strausfeld, 1992) for small object selective cells]. The evidence
presented here motivates the hypothesis that descending pathways may draw from
lobula plate cell systems with distinct by overlapping receptive fields in
order to multiplex expansion and rotation sensitive signals for the flight
motor circuits of the thorax. For simplicity, we have discussed LPTC
architecture as if it were common to fruit flies and their larger cousins. Do
fruit flies and house flies or blow flies have similar optomotor control
circuits? As yet, we simply do not know; whereas structural and developmental
evidence supports the notion of functional analogy
(Scott et al., 2002;
Scott et al., 2003), there is
as yet no direct physiological evidence to suggest similar LPTC function.
Furthermore, by contrast to well-studied blow flies and house flies, fruit
flies hover, which may place unique demands on the visual system in these
animals. It will be interesting to reveal the neural specializations for
expansion and rotation optomotor responses, as well as to match these
behavioral control circuits to the comparative sensory ecology flight control
in different insect species.
List of abbreviations
WBA
WBA
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Acknowledgments |
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References |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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Borst, A. (1990). How do flies land? From behavior to neuronal circuits. Bioscience 40,292 -299.[CrossRef]
Borst, A. and Bahde, S. (1987). Comparison between the movement detection systems underlying the optomotor and the landing response in the housefly. Biol. Cybern. 56,217 -224.[CrossRef]
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