Summation of visual and mechanosensory feedback in Drosophila flight control
1 UCB/UCSF Joint Bioengineering Graduate Group, University of California,
Berkeley, CA 94720, USA
2 Bioengineering, California Institute of Technology, Pasadena, CA 91125,
USA
* Author for correspondence (e-mail: flyman{at}caltech.edu)
Accepted 30 September 2003
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Summary |
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Key words: halteres, mechanosensory, Drosophila melanogaster, flight, control systems
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Introduction |
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A prominent feature of dipteran visual systems is a fast flicker fusion
rate, which exceeds 250 Hz in the blowfly Calliphora
(Autrum, 1958). By contrast,
the compound eyes provide relatively crude spatial resolution. For instance,
in Drosophila, the ommatidial spacing is approximately 5°, making
it difficult for the flies to resolve small objects from a distance
(Buchner, 1976
). Each
ommatidium contains a group of eight primary retinal photoreceptors that send
axons into the brain (Hardie,
1985
). The visual centers of the brain are composed of four optic
ganglia: the lamina, the medulla, the lobula and the lobula plate (Strausfeld,
1976
,
1984
). Cells within each of
the ganglia process visual motion in a hierarchy of progressing complexity.
Due in large part to their relative accessibility to electrophysiology, the
large identifiable cells of the lobula plate have been studied most
extensively (Hausen, 1984
).
Physiological studies have revealed that certain lobula plate tangential cells
are sensitive to the optic flow patterns resulting from rotation and
translation of the fly in space (Krapp et
al., 1998
; Krapp and
Hengstenberg, 1996
). Behavioral studies have shown that full-field
visual motion elicits compensatory responses, demonstrating a connection
between the visual system and the flight motor
(Blondeau and Heisenberg, 1982
;
Götz, 1964
;
Hengstenberg, 1991
).
The flight motor also receives sensory feedback from the halteres. These
tiny hindwings have been modified through evolution into sensory organs. The
halteres, which beat anti-phase to the functional wings through a stroke plane
of 180°, are positioned beneath the wings at a 30° angle from the
transverse body axis (Fig. 1A; Drosophila: Dickinson,
1999; Calliphora:
Nalbach, 1993
). The base of
the halteres is populated by hundreds of mechanoreceptors, consisting of
campaniform sensilla and chordotonal organs, a subset of which are thought to
encode Coriolis forces (Nalbach,
1993
; Nalbach and
Hengstenberg, 1994
; Pringle,
1948
). Coriolis forces, generated as the fly rotates in space,
deflect the haltere from its beating plane. Because Coriolis forces are the
cross product of the linear velocity of the haltere and the angular velocity
of the body, the resultant strains measured by haltere mechanoreceptors should
rise with rotational velocity. Haltere-mediated wingbeat responses have been
correlated to rotational velocity, providing additional confirmation of their
gyroscopic function (Dickinson,
1999
).
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Further evidence of the significance of visual and haltere feedback in
flight control can be found by examining the interconnections between these
modalities and the fly's motor systems. In Calliphora, motor neurons
of neck muscles receive input from both visual and haltere afferents
(Strausfeld and Seyan, 1985).
The haltere-to-neck motor neuron connection is very fast with latencies from
stimulation to motor neuron spiking of 2.5-3.0 ms (Calliphora:
Sandeman and Markl, 1980
).
Haltere afferents also connect to the motor neuron of steering muscle B1
(mnb1) (Calliphora: Fayyazuddin
and Dickinson, 1996
; Drosophila:
Trimarchi and Murphey, 1997
),
a muscle whose firing activity has been correlated with changes in wingbeat
amplitude (Drosophila: Heide and
Götz, 1996
; Calliphora:
Tu and Dickinson, 1996
;
Balint and Dickinson, 2001
).
Although a direct connection between visual afferents and the steering muscles
has not yet been documented with intracellular recordings, in the flesh fly
Neobellieria (=Sarcophaga) bullata, visual
interneurons are dye coupled to the motor neurons of B1 and B2, providing
anatomical support for such a connection
(Gronenberg and Strausfeld,
1991
). Visual afferents do, however, provide excitatory input to
the muscles controlling the motion of the halteres, suggesting visual input
can influence steering muscle activity indirectly (Calliphora:
Chan et al., 1998
).
Behavioral experiments conducted in a wide range of animals provide further
insight into the mechanisms of sensory fusion. One aspect of multimodal
integration, which has been the focus of previous work, is the role that each
sensory modality plays in different phases of complex behaviors. In
cockroaches, for example, visual and mechanosensory cues are involved in
different stages of the escape reflex (Ye
et al., 2003). Similarly, predatory fish rely on sensory feedback
from the visual system, lateral line and, in some cases, electrosensory organs
during feeding behavior. Although some or all of these components contribute
to the overall success in prey capture, the relative importance of each
feedback channel varies through different stages of feeding
(Nelson et al., 2002
;
New, 2002
;
New and Kang, 2000
). Locusts
use feedback from many sensory modalities, including compound eyes, ocelli and
wind-sensitive hairs, to orient themselves during flight (for a review, see
Reichert, 1993
). The ocellar
system can inhibit the strong excitatory input from the compound eyes and wind
hairs on descending interneurons if these modalities are providing feedback
that is in conflict with the ocelli. This suggests that, in the presence of
inconsistent measurements, the control system relies on the ocellar feedback
(Reichert, 1993
). Although
these studies document interaction among certain sensory inputs during complex
behaviors, they do not provide a quantitative measure of the relative
contribution of each component when all sensory systems are intact. Studies in
humans on the integration of visual and haptic cues during perception tasks
have demonstrated that behaviors resulting from cue combinations correlate
well with the output predicted by a maximum-likelihood estimator
(Ernst and Banks, 2002
;
Hillis et al., 2002
). In this
paradigm, the relative contribution of each modality to the overall sensory
estimate is directly related to the variance of its measurement, such that
signals with lower variance are given more influence.
The goal of the present study is to characterize the integration of feedback from the halteres and visual system during compensatory flight maneuvers in fruit flies. Using a specialized flight simulator, we activate different sensory modalities both individually and concurrently while monitoring the animal's behavioral response. We systematically vary the relative phase, amplitude and rotational axis position of concurrent visual and mechanical oscillations to determine the contribution of each sensory modality. Our results show that the flight control system uses both sensory channels when available, such that the response to complementary concurrent stimuli is larger than the response elicited by exciting just one modality. The flight control system integrates these inputs in a manner that can be modeled by a weighted sum, in which haltere feedback is given preference over visual information.
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Materials and methods |
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Flight simulator
A flight simulator composed of a cylindrical light-emitting diode (LED)
display mounted within a 3 degrees-of-freedom rotational gimbal (for details,
see Sherman and Dickinson,
2003) enabled us to present separate and concurrent visual and
mechanosensory stimuli during tethered flight
(Fig. 1B). The high spatial
resolution (
min=3.5°) wrap-around visual display was
refreshed at approximately 1 kHz, well above the temporal resolution of the
Drosophila visual system. Each tethered fly was mounted in the center
of the display beneath an infrared LED and above two photocells that comprise
part of a real time wingbeat analyzer
(Heide and Götz, 1996
;
Lehmann and Dickinson, 1997
).
For each stroke cycle, the wingbeat analyzer measures the stroke amplitude for
each wing, as well as the instantaneous wingbeat frequency.
Rotation experiments
The objective of this study was to test the flies' response to concurrent
visual and mechanical stimuli. The two stimuli were presented as sinusoidal
oscillations that varied by phase offset, amplitude or axis of rotation. In
each case, our experimental protocol consisted of presenting each fly with a
repeated series of three open-loop stimuli separated by 5 s of rest, an
interlude that allowed their wingbeat amplitude and frequency to return to
pre-stimulus levels. During these recovery periods, the flies were maintained
under an optomotor closed-loop condition in which they controlled the position
of a 14° wide dark stripe by adjusting their left and right wingbeat
amplitudes (Götz, 1987).
By switching to closed-loop conditions during the 'rest' periods, flies tended
to respond more robustly during subsequent stimulus presentations. Each
segment of stimulus presentation began with mechanical oscillation with no
accompanying visual motion. This was followed by a visual oscillation during
which a striped pattern on the display rotated around either the roll or pitch
axis but with no mechanical oscillation
(Fig. 1C). The final stimulus
in each trial was a simultaneous presentation of visual and mechanical
oscillations. Each stimulus presentation consisted of six sinusoidal
oscillations.
The first set of experiments explored the effect of phase offset between
visual and mechanical stimulation. For these experiments, the concurrent
oscillations were always presented at the same frequency but with a phase
offset, , which was systematically varied in different trials by
increments of 45°. For each experiment,
was selected at random from
eight phase increments between 0° and 360°. The amplitude of both
visual and mechanical oscillations was 30°. It should be noted that by
defining both mechanical and visual rotations with the same convention (i.e.
right-hand rule), a fly will experience a naturalistic combination of
mechanical and visual stimuli when the two are presented with a phase offset
of 180°. This is counter-intuitive but results from the fact that if a fly
physically rotates to the left, the visual word will move across its retina to
the left.
The second set of experiments measured how mechanical motion of varying amplitude influenced the contribution of visual feedback during simultaneous stimulation. The mechanical and visual stimuli were presented at the same frequency (1.2 Hz) but with the amplitude of the mechanical oscillations varied randomly between 5°, 10°, 20°, 30° and 40°. For all trials, the amplitude of the visual stimulus was fixed at 30°. Data were collected at each of the following phase offsets: 180°, 270° and 90°. All oscillations were about the pitch axis, since motor responses are generally more robust than for roll or yaw.
Our third experiment focused on the response to simultaneous rotations about two orthogonal axes. In this experiment, flies are presented with concurrent mechanical and visual rotations that have the same frequency and amplitude but differ in their axis of rotation. We measured the response to the following stimulus combinations: visual pitch/mechanical roll and visual roll/mechanical pitch.
Based on our results from previous frequency response experiments
(Sherman and Dickinson, 2003),
we selected one oscillation frequency for each experiment and axis such that
the responses to visual and mechanical motion were of comparable strength. The
selected frequency for the phase and amplitude experiments was 3.0 Hz for roll
stimuli (
peak=565 deg. s-1) and 1.2 Hz for pitch
(
peak=226 deg. s-1). For the orthogonal axes
experiments, the oscillation frequency was 1.2 Hz for visual roll/mechanical
pitch and 2.4 Hz for visual pitch/mechanical roll. Previous results showed
that the haltere-mediated response to yaw was much weaker than the visually
elicited yaw response (Sherman and
Dickinson, 2003
). For this reason, we focused on pitch and roll
and did not investigate the interaction between visual and mechanical stimuli
for rotations about the yaw body axis.
The data, which included left and right wingbeat amplitude, wingbeat
frequency, the position of the visual pattern and the orientation of the
gimbal, were digitized at 200 Hz and stored on computer. Signals were filtered
digitally (zero phase delay) with a low pass cut-off of 20 Hz to remove any
high frequency noise. To account for slight differences in the position of
each fly relative to the wingbeat analyzer, we normalized the wingbeat data
with respect to baseline variability as described previously
(Sherman and Dickinson, 2003;
Tammero and Dickinson, 2002
).
Thus, in these experiments the output of the wingbeat analyzer is used as a
relative measure of behavioral responses, not as a precise measure of stroke
amplitude. The output from the wingbeat analyzer is, however, linearly
proportional to both stroke amplitude
(Lehmann and Dickinson, 1997
)
and flight torque (Tammero et al.,
2003
). We interpret modulation of the sum of the left and right
wingbeat amplitudes to represent the control of pitch, because a bilateral
change in stroke amplitude creates moments about the pitch axis. Similarly,
modulation in the difference between the left and right wingbeat amplitude
represents the fly's attempt to adjust roll or yaw.
For these analyses, we only included data from flies that flew long enough
to complete the multiple set of trials that were required for each experiment.
The number of repetitions varied depending on the experiment: for phase
experiments, flies completed at least five repetitions at each of the eight
phase relationships; for amplitude experiments, flies completed minimally six
repetitions at five amplitudes; and for multi-axis experiments, flies
completed 20 repetitions for the one experimental condition. Multiple
responses at a given experimental condition (phase offset, amplitude, axis)
were time-averaged. A fast Fourier transform (FFT) algorithm was used to find
the sine curve that best fit the averaged responses to mechanical rotation
(Rm), visual rotation (Rv) and
concurrent mechanical and visual rotation (Rm+v). The
amplitude of the calculated sine fits is denoted WBA in figures and
legends. The averaged wingbeat amplitude signals are referred to as WBA. All
data were analyzed using custom software written in MATLAB (Mathworks, Natick,
MA, USA).
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Results |
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We examined the responses in wingbeat amplitude (left minus right for roll,
left plus right for pitch) to simultaneous visual and mechanical oscillations
at eight different phase offsets. The responses averaged across flies are
plotted against the phase offset between the two sensory stimuli in
Fig. 3 (A, pitch axis; B, roll
axis). The response to concurrent stimuli is weakest at phases between
+45° and -45°, conditions at which feedback from the visual system and
halteres is most contradictory. The response to simultaneous 3 Hz visual and
mechanical rotation about the roll axis peaks near a stimulus phase difference
of approximately 150°. This 30° delay from the expected maximum
stimulus phase of 180° might be explained by the intrinsic delay in visual
motion processing, estimated to be approximately 30 ms in flies
(Land and Collett, 1974). At
the stimulus frequency of 3 Hz, this delay would cause a phase shift of
33°, which is comparable with the observed value. The same logic applied
to the roll results, however, predicts a phase delay of 13°, whereas an
advance of
20° was observed (Fig.
3B). Thus, although the intrinsic processing delays within the two
modalities should influence these results, our measurements do not appear to
offer adequate resolution to observe such subtle effects.
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For both roll and pitch, the peak response to concurrent stimuli is approximately double the amplitude of the responses at zero phase and is significantly larger than the mean responses to stimuli presented separately (indicated on the y-axis as R-v and R-m). This suggests that the response of the fly to input from a single modality is not saturated, and experimental treatments presenting either visual or haltere stimulation alone yield only a fraction of the potential response of a freely flying insect encoding the same motion cues with multiple sensory systems.
To examine the relative contribution of each sensory modality on flight
equilibrium reflexes, we fit the amplitude of the response to concurrent
stimuli (Rm+v) with a linear sum of isolated stimuli
(Rm+Rv). Whereas this appears to fit
some phase relationships very well, this simple scheme did not provide a good
fit at all phase offsets. A substantially better match resulted from a
weighted sum:
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To determine how ß varies with u, we measured the
contribution of visual feedback during concurrent oscillations while varying
u. We calculated ß for each mechanical stimulus amplitude by
solving equation 1, with
=1. The mean value of ß for a group of flies is plotted against
u for the three phase offsets tested
(Fig. 4A). There is no
significant difference in the value of ß over a wide range of mechanical
stimulus amplitudes, implying that any haltere stimulation, regardless of
magnitude, results in a fixed decrease in the contribution from the visual
system (Fig. 4A). Thus, ß
appears to resemble a switching function. To test this possibility further, we
calculated the single values of both
and ß that best fit the data
for all three phase relationships and all five amplitudes
(Fig. 4B). The resulting
values,
=1.17 and ß=0.65, provide an excellent fit for the data,
implying that ß is approximately constant for all u.
Furthermore, these weighting values correspond well with values determined
independently in the separate phase offset experiments.
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We next tested whether haltere feedback is still weighted more heavily than
visual feedback if the mechanical and visual oscillations are applied along
orthogonal stimulus axes. When presented with a combined stimulus of
mechanical pitch and visual roll, the flies modulate wing kinematics to create
a simultaneous roll and pitch motion. Averaged traces from a single fly
illustrate that during concurrent oscillations flies exhibit a pitching
response equal to that elicited by a mechanical stimulus presented alone
(Rm; Fig.
5A, top trace). At the same time, the fly modulates left minus
right wingbeat amplitude to create a roll response that is approximately 60%
of the amplitude of a pure visually elicited response (Rv;
Fig. 5A, bottom trace). Flies
achieve both responses simultaneously through a slight shift in the phase
relationship between the amplitude modulation of left and right wings. Because
mechanical pitch oscillations yield a wingbeat response with an insignificant
rolling component, and likewise visual roll will result in very little
modulation of pitch, we can estimate that the pitch response
(Rp) is a function of just Rm, and the
roll response (Rr) is a function of Rv
(Fig. 5B,C). We calculated the
values of and ß that best fit the equations
Rp=
Rm and
Rr=ßRv. For the converse
experiment, mechanical roll/visual pitch, we determined
and ß
that best fit the equations
Rp=ßRv and
Rr=
Rm. The averages of the
resulting
and ß were 1.0 and 0.68, respectively. Thus, regardless
of the axis of rotation, the phase or the amplitude of the stimuli, it appears
that mechanosensory feedback is weighted more heavily than visual feedback
when both signals are simultaneously active.
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Discussion |
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Interconnections between halteres, visual system and flight
motor
A biologically accurate model of the flies' sensorimotor control system
must incorporate our current understanding of the convergence of sensory input
onto the flight motor. Visual feedback projects to each of the three major
muscle groups that mediate flight control: wing, neck and haltere muscles. In
Calliphora, lobula plate tangential neurons that encode visual motion
synapse directly with motor neurons controlling the neck muscles that adjust
gaze during flight (Strausfeld and Seyan,
1985). However, such connections could not be responsible for the
mechanosensory-mediated attenuation of the visual response because the head
was rigidly fixed to the thorax in our experiments. Also, in
Calliphora, Chan et al.
(1998
) found that visual
stimulation evokes directionally specific spiking responses in control muscles
of the haltere within the metathorax. This synaptic interaction would provide
a mechanism by which visual feedback could modulate haltere input but does not
explain the phenomena we have observed in Drosophila, in which
haltere input attenuates visual reflexes.
The most likely anatomical sites for the observed interaction between
mechanosensory and visual pathways are either the dendrites of descending
interneurons within the brain or the flight motor neurons within the thorax.
In Calliphora, haltere afferents project directly to the
subesophageal ganglion (Chan and Dickinson,
1996) and thus might provide direct or poly-synaptic input to
descending interneurons encoding wide-field visual motion information. Haltere
afferents and visual interneurons also converge on pathways within the thorax.
Although there is little electrophysiological evidence for a direct
physiological link between descending visual interneurons and steering
muscles, this absence may simply reflect gating in quiescent preparations as
occurs in locusts (Reichert and Rowell,
1985
). There is anatomical evidence that the motor neurons of at
least two wing steering muscles, B1 and B2, are dye-coupled to descending
visual neurons in male flesh flies (Neobellieria bullata;
Gronenberg and Strausfeld,
1991
). The steering muscle B1 receives electrotonic input from
haltere afferents in both Calliphora vicina
(Fayyazuddin and Dickinson,
1996
) and Drosophila melanogaster
(Trimarchi and Murphey, 1997
).
In both species, B1 is known to control changes in wingbeat amplitude
(Heide and Götz, 1996
;
Tu and Dickinson, 1996
).
Haltere afferents are also known to contact the B2 steering muscle
(Fayyazuddin et al., 1993
),
but this connection has not been studied in detail. Nevertheless, it appears
that haltere afferents and visual interneurons converge directly onto the
steering muscle motor neurons controlling stroke amplitude.
It is important to note that although the haltere is the likely source of
the signal that modifies visual input, it is not the only possibility.
Hengstenberg (1991) presented
evidence for as many as eight reflexes that can provide feedback to the neck
motor system in Calliphora, and many of these could function
similarly to detect mechanical oscillations and control wing motion in
Drosophila. Although a previous ablation study indicated that the
halteres are required for the major component of the wingbeat response to
mechanical oscillation (Dickinson,
1999
), interpretation of ablation experiments is somewhat
ambiguous and we did not repeat such methods in this study. Aside from the
compound eyes, other non-haltere sources of equilibrium feedback include the
ocelli, prosternal hairs on the neck, and wing campaniform sensilla. These
modalities could contribute to both the basic response to mechanical
oscillation and the attenuation of the visual reflex during concurrent
presentation. Given that the head was fixed to the thorax and the fly was
rigidly fixed to the light display when oscillated, it is unlikely that the
ocelli or neck receptors are involved in these effects. However, it is
impossible to rule out the contribution of wing sensilla, which could respond
to changes in loading during mechanical oscillation or Coriolis forces acting
on the wing.
Physiological mechanisms of inhibition
No matter what receptors are involved, or where the convergence takes
place, the afferents sensitive to mechanical oscillations must somehow alter
the membrane properties of cells within the visual pathway. One could propose
a variety of circuits involving pre- or post-synaptic inhibition and layers of
local interneurons to explain these effects. However, one simple and
parsimonious explanation is that the small attenuation of the visual pathway
arises indirectly from the spatial arrangement of convergent afferents on
post-synaptic neurons. Nonlinear spatial summation has been characterized on
the tangential cells of the lobula plate
(Single and Borst, 1998). For
example, if haltere inputs are positioned closer to the spike initiation zone
of a post-synaptic motor neuron than the visual interneurons, the impact of
visual input might be diminished in the presence of an active haltere input,
which would function to shunt input from more distal synapses. In
Calliphora, haltere afferents originating at dF2, the campaniform
field considered most likely to encode gyroscopic forces
(Fayyazuddin and Dickinson,
1995
; Pringle,
1948
), synapse very close to the axon of the B1 motor neuron
(Chan and Dickinson, 1996
),
whereas the terminals of descending interneurons, based on studies in
Neobellieria bullata (Gronenberg
and Strausfeld, 1991
), appear to be more medial. The distal
location of the haltere terminals, which contain a sizable electrotonic
component, has presumably evolved to minimize the latency of equilibrium
reflexes (Fayyazuddin and Dickinson,
1996
). Thus, the attenuation of the visual input may represent a
secondary consequence of a circuit designed to rapidly convey haltere
information to motor neurons. An alternative explanation is that the weighting
of the two sensory inputs may play a specific functional role in the
performance of the flight control system. Obviously, support for this or any
other explanation will require further physiological and anatomical
studies.
Functional explanations of haltere dominance
In flight control, as in most feedback-mediated control systems, quick
reliable feedback is essential for stability and robustness in the presence of
disturbances. The haltere mechanoreceptors and the visual system vary greatly
in their temporal responses and reliability. Haltere feedback is very fast;
the delay from haltere deflection to neck motor neuron firing is approximately
3 ms (Sandeman and Markl,
1980). Visually mediated motor responses are an order of magnitude
slower (30 ms), as estimated for flight chases in free-flying houseflies
(Fannia canicularis; Land and
Collett, 1974
). This disparity is due in large part to the
relatively slow process of phototransduction, which involves a biochemical
cascade. On the other hand, the visual system is much more sensitive than the
halteres to slow changes in rotation
(Hengstenberg, 1991
;
Sherman and Dickinson,
2003
).
Models of multimodal integration have shown that the feedback weights from
multiple sensory channels correlate well with scaling factors generated by a
maximum likelihood estimator (Ernst and
Banks, 2002; Hillis et al.,
2002
). A maximum likelihood estimator determines a quantity by
taking a scaled sum of all the measurements of that quantity. The weight
assigned to each measurement is inversely proportional to the normalized
variance in the measurement. Thus, the greater the variability in a
measurement, the less influence it is given. Although our results do not
indicate that the visual responses have a larger variance than the haltere
responses, on an intuitive level the complexity of the visual world could
produce a signal with more ambiguity than the signal from the halteres. For
example, visual estimation of rotational velocity depends on a wide range of
parameters including image contrast, luminance and spatial structure, all of
which may vary quite widely during flight in natural settings
(Reichardt and Poggio, 1976
).
In addition, coherent visual motion, such as the swaying of tree branches or
grass stems, might inappropriately provide an adequate stimulus for
rotation-sensitive circuits within the visual system. Thus, the flight control
system might compensate for potential visual miscues by limiting the weight
placed on the visual measurement and showing preference to the halteres, which
provide an accurate measure of velocity that is not contaminated by the
spatial composition of the visual world.
Characterizing and making sense of ß
Our analyses have led to a characterization of a rather unusual weighting
function, ß, which functions as a switch
(Fig. 4A). This function has
two distinct and somewhat perplexing features: first, a discontinuity near
zero and, secondly, a constant gain over a range in which the haltere-mediated
wingbeat response rises monotonically. In regards to the first issue, we know
from previous research that there must exist a threshold in angular velocity
below which the haltere sensors fail to respond to body rotation. This feature
of the haltere response was identified previously by Hengstenberg
(1991), who reported that
haltere-mediated head movements only occurred above rotation velocities of 50
deg. s-1. In our experiments, the smallest mechanical oscillation
(amplitude=5°) resulted in a peak angular velocity of 38 deg.
s-1, a stimulus that elicited very weak responses. Because the
signal-to-noise ratio for the haltere-mediated wingbeat response is large for
very low levels of stimulation, we cannot confidently determine ß in this
region, thus we cannot rule out the possibility that the weighting function
rises smoothly to 1 at low stimulus intensities.
An equally confounding feature of the visual gain function is that it
remains constant over a wide range of stimulus intensity in which the
haltere-mediated wingbeat responses were increasing
(Fig. 4A). This latter
observation would suggest that the haltere system (or any other sensory
modalities, such as wing campaniforms, that contribute to the effect)
increases its output monotonically with increasing stimulus amplitude. Studies
of campaniform sensilla on the wing, which are serially homologous to those on
the haltere, would suggest that during flight this dynamic range is achieved
via either recruitment or phase shifts and not by changes in the
firing frequency of individual cells
(Dickinson, 1990). However, a
gradual recruitment of mechanoreceptors is not consistent with the all-or-none
change in ß. On the other hand, a haltere-encoding mechanism that
involves intensity-dependent phase shifts is not inconsistent with our
results. Changes in wingbeat amplitude are tightly correlated with the
advances in the firing phase of mnb1
(Heide and Götz, 1996
;
Tu and Dickinson, 1996
), and
there is some evidence that haltere afferents may be in part responsible for
such shifts (Fayyazuddin and Dickinson,
1999
). If all the individual mechanoreceptors are recruited at
near threshold stimulus levels, and the wingbeat amplitude adjustments are due
to the timing and not the magnitude of the input, then there is no reason to
expect the level of visual suppression to vary with haltere stimulation. A
final possibility is that these effects are mediated by other
mechanoreceptors, such as on the wings, antennae or legs, and these have
already reached a saturated region of operation. Regardless of the
physiological underpinnings, we have shown that this sensory input weighting
is extremely robust over a wide range of experimental conditions.
Control system model
The results presented here suggest a flight control model in which each
sensory channel when concurrently active is given a particular functional
weight. A simple model that incorporates these results with previous findings
is shown in Fig. 6. In this
model, the dynamics of the two sensory channels are represented by transfer
functions. These functions, determined by our previous frequency response
analysis (Sherman and Dickinson,
2003), represent the input-output relationship between angular
velocity and wingbeat amplitude response. Thus, they comprise multiple
elements along the sensorimotor pathway, including signal transduction,
sensory processing and flight muscle dynamics, each of which contributes
temporal characteristics to the net response. The visual system transfer
function can be approximated as a low pass filter, since only slow rotations
elicit large responses. Although the haltere-mediated wingbeat response
increases with increasing velocity, the gain of the system is approximately
constant in the operating region, thus the transfer function can be
approximated as a band pass filter
(Sherman and Dickinson, 2003
).
The results described in the present study have provided the appropriate
weighting functions for each channel; a switch can model the visual system
weighting function, and a unity gain block can model the weight on the haltere
signal. While this model does not provide insight into the physiology behind
these interactions, it does create a framework useful for characterizing the
interaction between multiple sensory feedback channels and the flight
motor.
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In summary, we have determined from a systems perspective how feedback from different sensory modalities is integrated into a flight control algorithm. Our results show that inputs from the visual system and halteres are combined in a weighted sum, which favors information from the halteres. Furthermore, the weights on each sensory channel appear to be independent of the phase, magnitude or axis of rotation, suggesting a hard-wired control mechanism. These results provide insight into the mechanisms of feedback in flight control and contribute to a general understanding of multimodal integration.
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