A single control system for smooth and saccade-like pursuit in blowflies
Bielefeld University, Department of Neurobiology, PO Box 10 01 31, 33501 Bielefeld, Germany
* Author for correspondence at present address: Visual Sciences, Research School of Biological Sciences, Australian National University PO Box 475, Canberra, ACT, 2601, Australia (e-mail: norbert.boeddeker{at}anu.edu.au)
Accepted 23 February 2005
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Summary |
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Key words: pursuit, control system, visuomotor control, behaviour, computational neuroethology, computer simulation, insect vision, blowfly
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Introduction |
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In primates, including humans, eye movements from a functionally different
class catch the image of a moving target and hold it steady in the frontal
visual field by smooth pursuit eye movements, even if the observer and thus
the visual world moves. Primates were long believed to be the only animals
that can perform smooth pursuit. Only recently smooth pursuit has also been
revealed in cats (de Brouwer et al.,
2001). If target motion is too rapid and displaced outside the
frontal visual field, smooth pursuit is interrupted by catch-up saccades which
tend to centre the target again (de Brouwer et al.,
2002a
,b
).
Visual pursuit can be found in a number of insect species; dragonflies, for
instance, feed on prey caught in flight
(Olberg et al., 2000) and
males of several fly species chase potential mates or rivals during their
courtship behaviour (Collett and Land,
1975
; Land, 1993b
;
Land and Collett, 1974
;
Wagner, 1986a
; Zeil,
1983
,
1986
). The underlying control
system has to solve two important tasks. On the one hand, the chasing insect
has to control its forward velocity and its distance to the target. On the
other, the insect needs to fixate the target in the frontal visual field. It
has previously been shown for blowflies that forward velocity is controlled by
the retinal size of the target (Boeddeker
et al., 2003
). Moreover, it is generally agreed that the retinal
position of the target serves as an input variable to the control system that
leads to fixation of the target in the frontal visual field. However, how
retinal position errors are transformed into turning responses is still
controversial, and the objective of the present study is to contribute towards
solving this problem.
The housefly Musca domestica pursues other flies in a series of
rapid discrete turns. It was therefore suggested that chasing behaviour in
houseflies is controlled by a discontinuous pursuit system
(Wagner, 1986a). The retinal
position of the target was assumed to be sampled only from time to time and
the output of the chasing system to consist of pre-organised responses, i.e.
saccade-like turns without visually driven feedback
(Wagner, 1986a
). In contrast,
the behavioural analysis of chasing behaviour in the small housefly Fannia
canicularis (Land and Collett,
1974
), the dolichopodid fly Poecilobothrus nobilitatus
(Land, 1993a
), the hoverfly
Syritta pipiens (Collett and Land,
1975
) and blowflies of the genus Lucilia
(Boeddeker et al., 2003
)
suggested that the tracking system is basically continuous. In such a control
system, visual input is transformed to motor output on an instant-by-instant
basis. Nonetheless, saccade-like body rotations occur during pursuit in the
above-mentioned flies. Because saccade-like and smooth pursuit strategies
differ so much in their performance, they might be mediated by separate
control systems (Land, 1992
,
1993b
).
The present study starts from a different perspective. Rather than assuming
a priori different mechanisms underlying smooth and saccade-like
tracking in blowflies, we test to what extent the `virtual blowfly', which was
customised for chasing a smoothly moving artificial target in a model analysis
(Boeddeker and Egelhaaf, 2003),
can also account for pursuit of natural targets, i.e. other flies.
The behaviour of the virtual fly can be manipulated by variation of eight
model parameters. Six of these parameters were taken from our preceding study
(Boeddeker and Egelhaaf, 2003).
The performance of the virtual fly was therefore not specifically tweaked for
the pursuit of realistically moving targets as used here. The smooth pursuit
system implemented in the virtual fly consists of two subsystems. One module
controls the virtual fly's forward velocity using the retinal size of the
target as the input signal. A second subsystem controls the angular velocity
of the animal, depending on the retinal target position. In the present study
the original version of the smooth pursuit controller is compared to an
elaborated version where fixation of moving targets is aided by an accessory
controller that uses retinal slip velocity. The use of retinal target
velocity, in addition to position error information, was previously found to
stabilise the performance of smooth pursuit systems
(Land, 1992
). A second reason
for including an additional controller is that the smooth pursuit system of
primates uses target velocity as an important input variable
(Rashbass, 1961
) and current
models of pursuit eye movements include a velocity servo
(Churchland and Lisberger,
2001
; Krauzlis and Lisberger,
1994
; Robinson et al.,
1986
).
We present evidence that, at least in blowflies, both types of following responses, i.e. smooth pursuit and saccade-like turns that capture the target in the frontal visual field, can be explained by a single control system. We show by numerical simulations of fly behaviour that saccade-like changes of body orientation occur without the need for an extra saccade-generating mechanism. This is mainly a consequence of time constants that mimic neuronal processing times, muscular dynamics and inertia. Thus, saccade-like tracking in blowflies can be seen as an emergent property of a smooth pursuit system under circumstances where the target is displaced rapidly on the pursuing fly's retina.
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Materials and methods |
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Design of the virtual fly
Overview
As in our previous study (Boeddeker and
Egelhaaf, 2003), the mobility of the virtual blowfly is restricted
to yaw rotation and to translation in the horizontal plane. These three
degrees of freedom are sufficient to enable the virtual fly to generate
steering behaviours like real flies chasing a dummy target on a circular track
(Boeddeker and Egelhaaf, 2003
).
Gaze direction is assumed to be equivalent to body orientation. We have
refrained, so far, from simulating chases after targets that alter their
flight altitude. We implemented two visual pathways in the virtual fly: one
for target fixation (Fig. 1A)
and one for speed control (Fig.
1B). The retinal size of the target controls the forward speed of
the virtual fly. The position and angular velocity of the retinal image of the
target determine the fly's intended flight direction. To mimic lag effects of
neuronal processing times, muscle reaction times, and body locomotion dynamics
resulting from inertia and viscous air damping we use two low-pass filters as
lumped models in combination with a simple locomotion model
(Fig. 1C).
|
To simulate the fly's rotations about its yaw axis, we boil down the
system's dynamics to one time constant (see Discussion). In accordance with
our experimental results (Boeddeker et al.,
2003), the time constant in the target fixation pathway was set to
15 ms.
For translational locomotion the fly's momentum has an even stronger
influence, represented by a time-constant of 80 ms in the pathway for speed
control. To simulate locomotion in blowflies realistically it is also
important to realise that they generate sideward translation and yaw turns
relative to the outside world by first rolling their thorax and then pitching
the rolled thorax. Hence, the time course of sideward movements and yaw turns
in an external coordinate system do not, in all situations, coincide with the
time course of sideward thrust forces and yaw torque as generated by the fly
in a body-centred coordinate system
(Schilstra and van Hateren,
1999). Rotational and translational motion components are thus
coupled in a non-trivial way. Since the spatial resolution in our movies does
not allow reliable resolution of fly roll and pitching rotations, we refrain
from simulating translational body dynamics in detail. Instead, we use a
kinematic locomotion model (Equation 3) to determine the actual trajectory of
the virtual fly (Fig. 1C). From
our behavioural study we are able to estimate the maximal possible velocities:
rotation was found not to exceed 5000 deg. s1 and maximal
translation was below 3 m s1.
Speed control
The relationship between the retinal size of the target () and the
output of the speed controller (s) is given by Equation 1, with free
model parameters Sv and
* that reflect the gain and
the location of the maximum of the speed controller's characteristic curve.
Since targets at a large distance are too small to be seen by a fly, the
controller output should then not be affected by target size, but adjust
`spontaneous' speed (Sg):
![]() | ((1) |
Target fixation
The angle subtended by the fly's longitudinal body axis and the line
connecting the fly with the target represents the deviation of the target
position from the frontal midline of the pursuer's head (`error angle'). The
error angle is defined in a fly-centred polar coordinate system with 0°
pointing directly ahead. A fixation controller, converting in each simulation
step error angle () and velocity
(
) into a rotational step of the
pursuing virtual fly in the horizontal plane (
), can be
formalised as follows:
![]() | ((2) |
Virtual fly kinematics
The kinematics of fly body yaw rotation is lumped into the filter in the
target fixation pathway (see above), accounting for the recent finding that
inertia is relevant in shaping the dynamics of yaw rotations
(Fry et al., 2003). A third
module determines the virtual fly's position in the next simulation step,
accounting for the fact that translational inertial momentum of the fly has a
non-negligible impact the fly's trajectory. The outputs of the above-mentioned
pathways form the `intended' vector of locomotion of the virtual fly
(
). The direction of this vector is
determined by the fixation controller, its length by the speed controller. In
the physical world, inertia and viscous air damping affect the fly's
locomotion. As for rotation, we have strong evidence that inertial forces play
a major role in the locomotion dynamics of blowflies (see Discussion). As a
result of translational inertia, there is a difference angle (slip angle)
between the direction of heading and the direction of the fly's flight
trajectory, especially during and immediately after sharp turns. Consequently,
the intended vector of locomotion does not exclusively determine the actual
trajectory of the virtual fly. To model this effect of frictional and inertial
forces, we follow an approach similar to methods that have been used to
animate autonomous agents in computer graphics
(Reynolds, 1999
). Our
locomotion model does not consider the details of biomechanical properties or
body dynamics but reduces translational locomotion parameters to a
single variable (M) which, in combination with the time constants of
the two low-pass filters, was fitted to provide flight trajectories of similar
shape to those of real flies in our behavioural experiments
(Boeddeker and Egelhaaf, 2003
).
For each simulation step the new velocity vector
is given by Equation 3. The extent to
which the intended velocity determines the virtual fly's trajectory and how
far the trajectory is determined by the velocity vector in the preceding
flight path simulation step can be adjusted by the parameter M. The
interplay of time constants in the two visual pathways described above, using
the mechanism described by Equation 3, simulates the impact of translational
forces generated by the flight motor relative to the effects of translational
momentum and viscous air damping on the moving fly:
![]() | (3) |
Data are updated 1000 times per simulated second. The behaviour of the
virtual fly can be manipulated by variation of eight parameters. Six of these
parameters were from our preceding study
(Boeddeker and Egelhaaf, 2003):
the two first-order low-pass filter time constants acting on fixation
(
f=15 ms) and speed control (
v=80 ms), the
movement coefficient (M=0.0455), and three parameters characterising
the transfer function of the speed controller (Sg=0.8 m
s1, Sv=67, and
*=0.0865). The gain
factor for yaw rotation, depending on retinal target position
(Gp), was set to 0.1 and the gain factor for yaw rotation,
depending on retinal target velocity (Gv), was varied
between 0 and 0.005. With this set of parameters the rotational speed did not
exceed 5000 deg. s1 and the maximal translational speed was
always lower than 3 m s1. The virtual fly thus locomotes
within the constraints set by the behaviour of real flies
(Boeddeker et al., 2003
).
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Results |
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Pursuit of a realistically moving target by the virtual fly
A virtual fly, which for locomotion control only uses information on the
retinal size and the position error of the target (`position-only servo',
Gp=0.1, see Materials and methods) can not only pursue
smoothly moving targets (Boeddeker and
Egelhaaf, 2003), but also a target that moves like a real fly
(Fig. 3A). The trajectory of
the virtual fly is similar to that of a real fly chasing another fly on an
almost triangular flight path (cf. Fig.
1B). While chasing the target, the virtual fly only manages to
fixate the target in the frontal part of its visual field for part of the
time; the target is displaced several times towards lateral retinal positions
(Fig. 3B). Although a smooth
pursuit system is implemented, rapid saccade-like turns occur
(Fig. 3C) that re-centre the
target in the frontal visual field. These saccade-like turns are characterised
by brief rotational velocity peaks (Fig.
3D).
|
This characteristic behaviour of the virtual fly remains qualitatively the same when the position-only servo is augmented by a velocity input, as long as the gain of the velocity input is relatively small (Gv=0.0015). The additional velocity input smoothes the flight trajectory, reduces the retinal displacements of the target and, concomitantly, somewhat reduces the angular velocities during saccade-like turns (Fig. 3EH). Increasing the gain of the velocity signal will impair the performance of the virtual fly compared to a real fly (Gv=0.025; Fig. 3I). Fixation of the target in the centre of the visual field is poor and saccade-like turns are absent (Fig. 3JL). We conclude that fixation control relies strongly on position error and might be improved by taking into account the image velocity of the target, as long as the velocity input is not too strong.
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Discussion |
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By using a computer-simulated virtual blowfly we show that catch-up saccade-like body turns can be explained as an emergent property of the fly's smooth pursuit system under certain circumstances. We show that sophisticated visually guided behaviour can emerge from much simpler mechanisms than intuitively expected. The complexity of computational mechanisms needed to explain chasing behaviour can be reduced if one considers not only internal control mechanisms, but also the fact that natural behaviour operates under closed-loop conditions, i.e. under conditions where the system output affects the sensory input.
It is important to stress that the scope of this study is to account parsimoniously for control mechanisms underlying chasing behaviour. Although the smooth pursuit system can produce saccade-like movements under certain circumstances, we cannot exclude that the control system in the blowfly is more complex.
Smooth versus saccade-like tracking systems
To transform retinal position errors into angular velocity of the animal, a
continuous control system, analogous to the human smooth pursuit system of eye
movements, has been proposed for several fly species
(Land, 1993b;
Land and Collett, 1974
;
Wehrhahn et al., 1982
).
According to this scheme, correctional body movements continue until the
retinal error is reduced to almost zero. The finding that in tethered flying
male houseflies pursuit responses last as long as the target is visible adds
further support to the idea of a continuous control system underlying pursuit
(Srinivasan and Bernard,
1977
). Such a feedback control system is, from an engineer's point
of view, a good solution if there are not many time-consuming operations
inherent in the system. In technical systems the measuring sensors, the
controller and the actuating element also need time to work, which can impair
the performance and stability of the feedback control system. This is also
true for biological tracking systems, such as those of the fly or of humans.
For the chasing system of the blowfly the situation is even more complicated,
given that chasing is one of the most rapid and acrobatic behaviours found in
the animal kingdom. Targets being pursued by flies can change their direction
of motion by more than one order of magnitude more rapidly than targets can be
followed by smooth eye movements of primates. The pursuit system may become
unstable when the retinal input is very transient, depending on the gain of
rotational control and the time it takes to transform the retinal position of
a target into rotational body movements. Because blowflies are small and
lightweight it might be possible to achieve a shift in flight direction in a
relatively short time. Moreover, the neural system in blowflies seems to be
able to transform visual input to motor output in a very short time to prevent
the control system from getting unstable.
Another solution to improve stability might be to reduce the gain for
rotation, but this results in poor tracking performance if the target moves
very fast. To catch up with the target, switching to a saccadic tracking
strategy can improve performance. Large retinal errors may then be reduced by
fast movements, the saccades. Various pursuit strategies and their performance
were modelled and compared in a review by Land
(1992). Land concludes that
mixed pursuit strategies in which position is dealt with by a saccadic system
and velocity by a smooth system give the best overall results.
In primates, visual fixation and tracking of targets are suggested to be
accomplished by such a mixed strategy: saccades are programmed primarily to
correct errors between target and eye position (for reviews, see
Moschovakis and Highstein,
1994; Sparks and Mays,
1990
). The neural circuit that generates saccadic motor behaviour
in primates is concluded to be a central pattern generator distributed within
the brainstem and adapted to produce high velocity movements with high
precision (e.g. Sparks and Mays,
1990
). In contrast, smooth pursuit eye movements are designed to
minimise the difference between target and eye velocity
(Keller and Heinen, 1991
;
Lisberger et al., 1987
;
Rashbass, 1961
). The neuronal
circuits involved in visual motion analysis for pursuit eye movements have
been viewed as largely independent from those for saccade programming, but
recent research has revealed functional and anatomical linkage between the two
systems (de Brouwer et al.,
2001
,
2002a
;
Gardner and Lisberger, 2002
;
Krauzlis and Stone, 1999
).
The origin of rapid turns
Saccade-generation mechanisms are available to flies: Fannia makes
abrupt turns at more than 1000 deg. s1 when patrolling under
landmarks (Zeil, 1986). During
cruising flight, houseflies and blowflies change their flight direction mainly
by body saccades (Schilstra and van
Hateren, 1999
; Wagner,
1986b
). During target pursuit, the hoverfly Syritta makes
saccadic turns to new targets, and tracks discontinuously at high target
velocities (Collett and Land,
1975
).
Currently there are no computational models that can account for the origin of these saccades in flies. Here we show that a continuous smooth pursuit system can generate body turns that are saccadic in character, similar to those seen in real flies during chasing behaviour. It should be noted that this model is not meant to account for saccade-like body rotations in other behavioural contexts, such as during spontaneous cruising flight.
How are saccade-like body rotations during pursuit behaviour generated without an extra saccade-generating mechanism? Saccade-like body movements can be related to specific spatial relations between the target and its pursuer during a chase. Unless the chasing fly is directly heading toward its target, the target may get displaced very rapidly on the retina. Since translational movement leads to large image displacements when the chasing fly is close to the target, it is not surprising that in this situation particularly, saccade-like turns can frequently be found. To understand why saccade-like turns are generated in these situations, one has to consider the geometry of chasing behaviour: the error angle will, in most cases, increase more rapidly for a given translational velocity of the fly the closer it is to the target. Since the error angle is the signal that drives rotational velocity, large and rapid turns are likely to occur if the fly is close to the target. These rapid turns can be regarded, at least phenomenologically, as saccades.
When we analysed the pursuit of real and virtual flies of a target moving
smoothly on a circular track, we found fast body rotations, especially during
phases of the chase when the blowfly approached the target very closely but
missed it, or just before target capture
(Boeddeker et al., 2003;
Boeddeker and Egelhaaf, 2003
).
A fixation controller with a combined positional and velocity error input
tends to result in an improved performance of the tracking system
(Land, 1992
). However, to
simulate trajectories of the virtual blowfly looking similar to trajectories
of real flies, the gain of the velocity servo must be lower than the gain of
the positional error signal. This is not surprising, since a pure velocity
controller will never manage to centre a target located in the peripheral
visual field. However, a moderate velocity input helps to prevent the fixation
controller from overshooting and damps oscillations.
Fly head and body dynamics
The virtual fly as used here, though it can account for many features of
the chasing behaviour of real blowflies, represents only a crude approximation
to reality. Our assumption that gaze direction coincides with body orientation
is not exactly valid, since during a saccade the head starts turning only
somewhat later than the body and stops moving slightly earlier
(Schilstra and van Hateren,
1998; van Hateren and
Schilstra, 1999
). We have refrained, so far, from simulating this
characteristic, because no experimental data on head movements during chasing
flights are available. A more realistic treatment of head movements might have
implications on the design and, in particular, the time constants in the
target fixation controller. The head weighs much less than the fly's body and,
as a consequence, has a smaller inertia than the rest of the body. Hence, head
movements can be faster than thorax movements and target fixation is likely to
be even more precise (van Hateren and
Schilstra, 1999
) than has been estimated in our studies on chasing
behaviour by taking only the orientation of the body axis into account.
To move the fly, its wings have to generate forces to overcome gravity, the
inertia of the fly's body and the viscous friction of the air. There is a
great deal of knowledge about basic principles of insect flight aerodynamics
(e.g. Ellington, 1995;
Fry et al., 2003
;
Lehmann, 2001
;
Sane, 2003
). From measurements
of body and wing kinematics during flight it has even been possible to
determine the forces that act on the body when turning and to show that, even
for small fruit flies, inertia rather than friction is dominating the flight
dynamics of these insects (Fry et al.,
2003
). Hence the earlier assumption that the angular momentum can
be neglected (Boeddeker and Egelhaaf,
2003
; Land and Collett,
1974
; Reichardt and Poggio,
1976
) is not valid. Despite the fact that we originally designed
the virtual fly according to this assumption, the inertial nature of yaw
rotations is indirectly taken into account by the time constant of the
fixation controller (see Materials and methods). The time constant and gain
for yaw rotation that we used in the present study were estimated from our
kinematic studies on chasing behaviour in Lucilia. Our assumptions on
the maximal yaw acceleration that can be generated by the virtual fly are in
accordance with the maximal acceleration measured in free-flying
Calliphora, a blowfly species related to Lucilia, though
slightly larger (
105 deg. s2;
Schilstra and van Hateren,
1999
). A consideration of the forces, masses and inertial momenta
involved during turns, reveals that the thorax angular accelerations realized
in blowflies appear to be close to the maximum possible
(Schilstra and van Hateren,
1999
).
The situation is more complicated for non-linear translational locomotion.
Blowflies often change flight direction by banked turns
(Schilstra and van Hateren,
1999). During turns the fly's translational momentum keeps the fly
in its `old' flight direction even though the flight motor is already
producing force that points, in an external coordinate system, sideways. This
behaviour is captured by our virtual fly generated trajectories that look very
similar to real flies' trajectories.
In conclusion, since the visual mechanisms underlying flight control are
the main topic of our study, we refrained from simulating flight dynamics and
used a computationally cheap kinematic locomotion model. Nonetheless, the
current version of the virtual fly is sufficient not only to account for most
features of visually guided chasing behaviour as characterised experimentally,
but also to explain the generation of yaw, forward and sideward velocities and
the corresponding accelerations, as seen in an external coordination system
that closely matches those measured in real blowflies (e.g.
Schilstra and van Hateren,
1999; Boeddeker et al.,
2003
).
The neuronal control of chasing behaviour
The fixation controller of the virtual fly that we propose on the basis of
our experimental and modelling analysis
(Boeddeker and Egelhaaf, 2003;
present study) is somewhat similar to a `neuronal' model scheme of the control
system underlying target tracking as proposed by Land and Collett
(1974
). Moreover, the
properties of male-specific neurons
(Gilbert and Strausfeld, 1991
;
Gronenberg and Strausfeld,
1991
; Hausen and Strausfeld,
1980
; Strausfeld,
1991
) have been suggested to fit well into the scheme proposed by
Land and Collett (1974
).
According to this circuitry two distinct visual pathways act in parallel in
the fly's brain when fixating a target in the frontal visual field, in a
similar way as proposed here for the fixation controller of the virtual fly
(see Fig. 1): one pathway for
the processing of target motion and one for the retinal position of the
target.
The division of the fixation controller into one pathway exclusively
signalling position and another pathway sensitive exclusively to velocity
information is convenient for analytical reasons, but is not imperative for
implementation at the neuronal level. There are even arguments against such a
subdivision: visual neurons signalling the retinal position of an object show
different response amplitudes for targets moving with different speeds, and
thus, will be ambiguous with respect to these stimulus parameters. This
feature may be attributed, for instance, to the spatial and temporal transfer
properties of neurons in the early stages of the fly's visual system
(Juusola and French, 1997). In
addition, any motion-sensitive neuron also provides information about the
retinal position of a target, since its sensitivity to visual motion stimuli
is not constant over the entire visual field, but has a sensitivity maximum at
some retinal location with a decreasing sensitivity at increasing distances
from this sensitivity maximum. Again, the responses of motion-sensitive
neurons are ambiguous with respect to stimulus parameters (for reviews on
motion-sensitive neurons of flies, see
Borst and Haag, 2002
;
Egelhaaf et al., 2002
). In any
case, the division of the fixation controller into a pure position and a pure
velocity servo gets blurred at the neuronal level. Although our knowledge on
the functional properties of male-specific neurons of flies is still
fragmentary, their known responses appear to be in accordance with this view
(Gilbert and Strausfeld, 1991
;
Gronenberg and Strausfeld,
1991
). In particular, none of the described neurons represents
either unambiguous position or velocity information. This feature is similar
to the receptive-field characteristics of target-selective descending neurons
(TSDNs) of dragonflies. TSDNs are good candidates for the chief guiding
neurons linking vision to target pursuit behaviour
(Frye and Olberg, 1995
;
Olberg, 1986
). The responses
of these neurons are jointly determined by the position of the target in the
receptive field and by its velocity
(Adelman et al., 2003
).
Land (1993b) pointed out
that a smooth and continuous control system will produce saccades under
certain circumstances related to discontinuities in the sensory input. These
discontinuities might be due to temporary occlusion of the target, poor
contrast or speed-induced motion blur, which is the loss of spatial resolution
for moving images. Although moving objects can be resolved even at high
retinal velocities, especially by the male retina
(Burton and Laughlin, 2003
), it
is likely that time constants intrinsic to the cells in the peripheral visual
system of blowflies may lead to some motion blur that attenuates neural
signals to small objects at very high retinal velocities
(Burton and Laughlin, 2003
;
van Hateren, 1992
;
Juusola and French, 1997
;
Srinivasan and Bernard, 1975
).
In a future version of our virtual fly it would be desirable to include a
realistic model of the peripheral visual system of male blowflies. It may well
be that the behavioural performance of the virtual fly may then become even
more realistic. However, it is a distinctive feature of the peripheral visual
system of male blowflies that motion blur is kept as small as possible. In
male blowflies both optics and phototransduction are specialised to enhance
and deblur the neural images of moving targets, enabling male flies also to
register target position during fast flight manoeuvres
(Burton and Laughlin,
2003
).
So far, when implementing our virtual fly, we have not taken into account the specific neuronal hardware of the male-specific part of the fly visual system. This is because the current experimental data are not yet sufficient to constrain the large number of parameters that need to be specified for a realistic neuronal network model. For this reason, we tried to keep the mechanisms implemented in the virtual blowfly as simple as possible while being sufficient to account for the relevant aspects of blowfly chasing behaviour.
From our behavioural experiments and the simulation of chasing behaviour, we now have adequate knowledge of the relevant visual stimulus parameters used by male Luciliae to guide pursuit of real flies or artificial targets. Whether or not these input variables are represented in the fly's nervous system and how they might be translated into behavioural responses has yet to be assessed in electrophysiological studies such as are currently conducted in our laboratory with retinal input as is seen by a male blowfly during chasing behaviour. On this basis it may be possible to replace the phenomenological model of the control system for chasing behaviour as implemented in the virtual fly by biologically more plausible networks. Only then will it be possible to understand how the neuronal mechanisms underlying chasing behaviour are adapted to the natural operating conditions of the system.
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Acknowledgments |
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