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First published online August 31, 2007
Journal of Experimental Biology 210, 3277-3284 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.008425
Neural mechanisms underlying target detection in a dragonfly centrifugal neuron
1 Discipline of Physiology, School of Molecular and Biomedical Science, The
University of Adelaide, SA 5005, Australia
2 Department of Zoology, University of Washington, Box 351800, Seattle, WA
98195, USA
Author for correspondence (e-mail:
karin.nordstrom{at}adelaide.edu.au)
Accepted 12 July 2007
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Summary |
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Key words: target detection, velocity tuning, contrast dependence, spatial interactions, insect vision, elementary motion detection (EMD)
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Introduction |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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).
To aid this task, dragonflies have among the largest compound eyes and
smallest interommatidial angles measured in insects, down to as little as
0.24° in the large predator Aeshna (see
Land, 1997) and around
0.3–0.5° in the dorsally directed acute zone of the smaller
corduliid dragonfly Hemicordulia tau
(Horridge, 1978). The dorsal
optical specializations of dragonflies are accompanied by higher-order lobula
visual neurons optimized for the detection of small moving targets
(O'Carroll, 1993), as well as
by descending target neurons providing the information to the flight muscles
(Olberg, 1981), with receptive
fields in the dorsal part of the visual field. While the optics and the visual
pathways for target detection provide a neural basis for the observed
behavior, little is known about the input pathways that underlie the
impressive sensitivity and selectivity of higher-order small target motion
detection (STMD) neurons.
During the past five decades, our understanding of insect vision has
improved dramatically. Pathways subserving motion analysis are among the best
studied of all neural pathways. One of the earliest models for motion
detection, the correlational elementary motion detector (EMD), was described
by Hassenstein and Reichardt (Hassenstein
and Reichardt, 1956). This model is based on non-linear
correlation of the response of one photoreceptor (or other unit) by the
delayed response of a neighbor. By subtracting the response of a mirror-image
correlator, the EMD gives direction-selective responses to moving features,
while ignoring flicker, or static luminance. This model is well supported by
physiological and behavioral evidence.
In insects, correlation-type EMDs are believed to form major input to
higher levels of motion processing, such as detection of optic flow patterns
(see Borst and Haag, 2002).
Lobula plate tangential cells (LPTCs), a group of neurons in dipteran flies
selective for wide-field motion, have been shown to take input from local
motion detectors, consistent with this model
(Egelhaaf et al., 1989;
Haag et al., 2004;
Hassenstein and Reichardt,
1956). The inherent rejection of non-moving features makes
correlation-type EMDs well suited as a preliminary stage in detection and
analysis of very small moving targets. STMDs of the hoverfly lobula are often
direction selective (Nordström et
al., 2006), and some display distinct velocity optima
(Nordström and O'Carroll,
2006). While both properties are consistent with correlation-type
EMDs, we have not yet directly tested other predictions of this model, as has
been used in analysis of LPTCs (Egelhaaf et
al., 1989; Haag et al.,
2004).
Hoverfly STMDs constitute a large, and to some extent, heterogenous group
of small higher-order visual neurons
(Nordström et al., 2006).
We have yet to encounter a model class of STMD neurons in dipteran flies that
provides the robust repeatability of the fly LPTCs for detailed quantitative
experimental investigation. However, in the dragonfly Hemicordulia,
we repeatedly encounter an immediately recognizable STMD neuron with a
contralateral receptive field (as compared to the recording site) and large
axon that permits recordings of long duration, providing the opportunity for
more detailed investigation. In this paper, we describe the basic physiology
of this neuron [which we name centrifugal STMD1 (CSTMD1)] to investigate
whether dragonfly STMDs utilize correlation-type EMDs as input. We analyzed
the response to targets of different contrast and velocity compared with the
output of an EMD model elaborated with biologically inspired input filters
(spatial and temporal pre-filtering). We provide clear evidence that
correlational EMDs (or equivalent processing) are on the input pathway to
STMDs. We finally show that complex spatial interactions permitted by the
complex dendritic tree of CSTMD1 might be involved in the exquisite tuning of
STMDs to small targets.
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Materials and methods |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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. The dragonfly was mounted in front of a 48 cm RGB CRT visual display
with a high refresh rate (200 Hz) and a mean luminance of 150 Cd
m–2. The dragonflies were mounted in front of the display at
a distance of 15–20 cm. They were aligned with the monitor using the
planar back surface of the head as a morphological landmark, and the animal's
equator was assumed to be 90° perpendicular to this. The animal's midline
was used to determine the vertical meridian. This was used in later analyses
to determine receptive field size and location, and stimuli size and
velocity. Visual stimuli were presented using VisionEgg software (http://www.visionegg.org). The display subtended approximately 100x75° of the animal's visual field of view, with a resolution of 640x480 pixels, permitting targets down to 0.15° to be presented. Data were digitized at 5 kHz using a 16-bit A/D converter (National Instruments, Austin, TX, USA) and analyzed both on-line and off-line with Matlab software (The Mathworks Inc., Natick, MA, USA). Data in Fig. 5 show a recording using the Picassso Image Synthesizer on a Tectronix 608 XYZ display.
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Subsequently, targets were presented moving in a single path through the
centre of the receptive field. We defined neurons as STMDs using similar
criteria to our recent studies (Barnett et
al., 2007; Nordström et
al., 2006). Target specificity was determined using a series of
bars of variable height and the width fixed at 0.6° drifting at 26 deg.
s–1. We recorded from over 50 neurons identified as
STMDs.
To estimate velocity tuning we drifted a 0.8x0.8° square and an 8° wide by 0.8° high black target across the centre of the receptive field at velocities ranging between 6 deg. s–1 and 600 deg. s–1. The targets were presented moving horizontally in the preferred direction and included a minimum 3 s rest between presentations.
We altered target contrast by increasing the luminance of the target
(Itarget) against the bright background of the display
(Ibackground), quantified using the Michelson definition:
 | (1) |
Data analysis
Analysis of spiking responses was carried out off-line in Matlab by
band-pass filtering the digitized response and then detecting spikes using an
algorithm that makes use of both edge and relative magnitude (level) cues.
Receptive fields were obtained from the horizontal scans using the methods we
developed previously for fly STMDs
(Nordström et al., 2006).
The peri-stimulus time histogram was divided into 21 bins, corresponding to
5° intervals on the two-dimensional display. We further analyzed the local
preferred direction using a method analogous to that used previously for fly
LPTCs (Krapp and Hengstenberg,
1997) and dragonfly descending neurons
(Frye and Olberg, 1995). The
response to four directions of motion at each point in the receptive field was
fitted in a least-squares manner with a sinusoid of variable phase, amplitude
and offset but a fixed period of 360°. We then used the phase of the
fitted function to infer the local preferred direction, plotted as the
orientation of a local vector, and the relative amplitude (after normalizing
for the overall maximum response of the neuron) as the length of the local
vector.
For all other experiments we measured the spike rate during the time the target traversed the receptive field, as a function of the varied parameter. For each experiment we performed 1–3 repeats in each animal. Where more than one repeat was performed, we calculated the mean of the repeats for further analysis. Given N-values thus represent the number of animals subjected to each particular test.
Morphology
To identify recorded neurons, we backfilled micropipettes with 4% Lucifer
Yellow in 0.1 mol l–1 LiCl. The dye was injected by passing a
hyperpolarizing current (0.2–2 nA, depending on the amount of current
individual electrodes would pass without blockage) for 1–10 min.
Following electrophysiology, the brain was dissected out of the head capsule,
fixed in 4% paraformaldehyde (in 0.1 mol l–1 phosphate
buffer), dehydrated through an ethanol series and cleared in methyl
salicylate. A z-series of photographs from the whole-mount was used
to reconstruct the morphology of the neuron.
EMD modeling
To predict the effect of altering target size (width in the same direction
as travel) on velocity tuning we simulated a one-dimensional array of
elementary motion detectors (EMDs) in a 360° ring configuration using
Matlab software. The inputs to the model were dark targets (luminance 0)
animated against a bright background (luminance 1), as in the biological
experiments. Photoreceptor input was spatially filtered (Gaussian blur) and
sampled to yield an interommatidial angle of 0.56° and an acceptance angle
of 0.78° based on optical data for Hemicordulia
(Horridge, 1978) and yielding
a ring of 640 EMDs. To account for at least some minimal (linear) high-pass
temporal filtering likely in the insect visual system, this signal was then
convolved with a kernel based on that obtained through white-noise analysis of
fly monopolar cells (James,
1990) and fitted with a double log-normal function of the form:
 | (2) |
). The simulation was run at a resolution of 1000 samples per
second and 100 samples per degree of visual space. The ring configuration of
the EMD array (and the fact that the target is thus always within the
`receptive field' of the EMD array) allowed us to conveniently estimate a
response (the linear sum of responses of all EMDs at any instant in time)
equivalent to the steady-state response of the system to a sinusoidal grating
stimulus, even though the response of any single EMD is a complex, transient
phenomenon. This allowed us to explore the response as a function of the speed
and spatial dimensions. |
Results |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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) with a peak response around 1–2° and little
response to bars over 10° high, or to widefield stimuli such as gratings
(Fig. 1A and
Fig. 2).
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). Based on the above criteria, we identified 50 neurons as
STMDs, exhibiting a wide variety of receptive fields and other properties
[ranging from STMDs with very small receptive fields as described in the
hoverfly (Barnett et al., 2007)
to neurons with massive homolateral, or even heterolateral, receptive fields].
We repeatedly encountered a neuron that provided stable recordings (up to 4 h
in some cases) and thus provided a good basis for more detailed study. This
neuron has a moderate-sized (
30° width) contralateral (as compared to
recording site), dorsal receptive field
(Fig. 1B). The neuron gives
very characteristic large (up to 100 mV), fast (duration <3 ms), bi-phasic
action potentials. In the healthiest recordings, the STMD shows little
spontaneous activity, but many penetrations from the same neuron (confirmed by
successful dye injection on seven occasions) yield characteristic regular
spontaneous firing at approximately 20 Hz.
Neuroanatomy of CSTMD1
Dye fills of the STMD (black in Fig.
1C) reveal a large axon traversing the protocerebrum from the
right hemisphere (we recorded intracellularly from the left hemisphere). We
have thus given it the anatomically descriptive name CSTMD1 (centrifugal small
target motion detector 1). CSTMD1 shows a mass of dense arborizations, with a
heavily beaded appearance across the entire distal left lobula (inset I,
Fig. 1C). There are two
additional arborizations in the ventro-lateral protocerebrum on both sides
(insets II and III, Fig. 1C).
The soma is located adjacent to the arborizations in the right midbrain.
Combined with the sparse, unbeaded (spiny) appearance of dendrites in this
region (inset III, Fig. 1C),
these observations suggest that this is the input region. This is further
confirmed by the physiologically recorded receptive field with excitation
confined to the right visual field (Fig.
1B). The midbrain dendrites in the left hemisphere have a beaded
appearance, suggesting output synapses (inset II,
Fig. 1C). Interestingly, if we
overlay a mirror image reconstruction (red in
Fig. 1C), the two midbrain
arborizations are co-located, suggesting the possibility that this neuron
makes synaptic contact with its contralateral counterpart.
We have based our definition of neurons as CSTMD1 on the physiological and anatomical characteristics described in Fig. 1. Even though there might be a small chance that the CSTMD1 `class' could include additional neurons with similar physiological and anatomical characteristics, such redundancy in a small insect brain is unlikely.
Contrast dependency
The EMD model is based on non-linear correlation of the response of one
photoreceptor by the delayed response of a neighbor
(Hassenstein and Reichardt,
1956). This non-linearity (usually modeled as multiplication)
leads to a characteristic dependence on pattern contrast, as observed in
numerous recordings from LPTCs (Egelhaaf et
al., 1989; Haag et al.,
2004; Harris et al.,
2000). To test for a similar effect in CSTMD1, we scanned the
center of the receptive field with 0.6x0.6° targets of varying
contrast moving at 26 deg. s–1
(Fig. 3). Our data show a
strong dependence on the contrast of the target. This does not saturate as
obviously as in LPTCs (Harris et al.,
2000), possibly reflecting the smaller-than-optimal target size
(which may not reach the same effective contrast as is possible with a
wide-field grating), but the contrast response function has an overall similar
form. Our data thus support a similar mechanism for the underlying motion
detectors to those in the LPTCs.
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;
Nordström et al., 2006)
has revealed both directional and non-directional STMD neurons, the latter
could still comprise the summed output of EMDs with different preferred
directions. Fig. 2 shows that
responses of CSTMD1 to all target sizes were stronger for targets moving from
the midline to the back of the eye. By constructing local vectors representing
the local direction selectivity, the receptive field map
(Fig. 1B) suggests that local
optima are for targets moving up and away from the frontal visual view point.
However, as all scans were performed in the same order, with the preferred
direction (right and up) followed by the less preferred (left and down), we
cannot rule out habituation to have an effect on this response.
Effect of target size on velocity tuning
The velocity tuning of an EMD array is influenced by the spatial statistics
of the image (Buchner, 1984).
While this is easy to confirm in dipteran LPTCs using sine-wave gratings,
which have all of their power at a single spatial frequency, STMDs only
respond to discrete targets, for which the spatial frequency is less easily
defined. We used a computer model to predict the effect of target size on the
velocity tuning of STMDs for discrete targets of different width (0.8° and
8° wide). The non-linear interaction between the leading and trailing
edges of the small target leads to a complex tri-phasic response at low
velocities (i.e. transiently inhibited even during preferred direction
motion), becoming biphasic (and briefer) as speed increases
(Fig. 4A). For a wider target,
the response form is different, as the leading and trailing edge responses are
separated further at low velocities (Fig.
4B). Whilst the specific shape of these transient responses
depends on the temporal filters modeled in the system (data not shown), all
such models show shift in the overall velocity optimum (i.e. the peak in the
instantaneous sum of all EMDs) from low to high velocities for the wider
target (Fig. 4C).
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190 deg.
s–1) for wider targets. The maximum response is also lower,
reflecting the specificity of STMDs for small targets
(Fig. 2). Further evidence for the specificity of the effect of target spatial dimensions on velocity tuning comes from an experiment that we have thus far replicated for a single CSTMD1 neuron (Fig. 5). Velocity tuning for three different targets, oriented parallel to the direction of travel, shows a shift to higher velocity as in Fig. 4D. However, if we rotate the orientation 90° (i.e. orthogonal to the direction of motion), the velocity tuning for the three targets all peak at similar values, being simply inhibited as target length is increased (Fig. 5B). Overall, the results of these experiments show a dependence of velocity tuning on spatial structure consistent with the EMD model, despite the evidence for spatial inhibitory mechanisms that tune the system to small targets.
Spatial interactions within and outside the receptive field
While the above experiments suggest strongly that a correlation-type EMD or
equivalent mechanism is operating at early stages in the STMD pathway, there
is nothing in such a model (including our computational implementation of it)
to explain the sharp tuning of these neurons to target length (i.e. orthogonal
to the direction of motion), as shown in earlier work
(Barnett et al., 2007;
Nordström et al., 2006)
or in Fig. 2 for CSTMD1. While
we have previously proposed models to explain such tuning based on spatial
inhibitory interactions within the receptive field
(Barnett et al., 2007;
Nordström et al., 2006),
it is more complex to consider the effects of such inhibitory mechanisms
along the direction of travel, because of the addition of the
interactions in the time domain (as evident from the effect of target width on
velocity tuning in Fig. 4D and
Fig. 5A). To overcome this
limitation, we instead examined the inhibitory effect of a second target of
equal size, moving along the same track but at a varying horizontal distance
(Fig. 6). When the two targets
are separated by large angles, the response to each passing into the
contralateral receptive field is distinct
(Fig. 6A). As the target
separation is decreased, there is clear evidence for an interaction between
the responses to the two targets, with spike rates never reaching that
observed for single targets (see Fig.
2). By a target separation of 5°, the response is largely
abolished in the individual trace (Fig.
6C, left column) and it is also significantly reduced over three
trials in the same neuron (Fig.
6C, histogram in right column). Smaller separations see a partial
recovery of response towards control levels, as the two targets effectively
fuse to become one (Fig.
6D,E).
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With this experimental design it is difficult to preclude the possibility
that the `inhibitory' effect arises from habituation or some other adaptive
process, since the second target passes across the same local motion detectors
as the first, with a short delay (120 ms for a separation of 5°).
However, this is unlikely to explain the depression of the response to the
first target for a separation of 15°
(Fig. 6B), where the second
target is still in the left visual field (see
Fig. 1B) when the first target
crosses the visual midline and into the excitatory receptive field of CSTMD1.
Further work is required, using other controls to preclude habituation effects
to fully explore the inhibitory mechanisms revealed by this stimulus. However,
given interommatidial angles of 0.56°
(Horridge, 1978), it is
interesting to speculate that the strongest inhibitory interactions occur
within the receptive field and at separations equivalent to 10
ommatidia. The suppression of responses by targets in the visual field of the
opposite eye might result from a second mechanism, mediated by the
heterolateral axon in CSTMD1. Given the observation that these neurons bear a
likely output arborization corresponding to the inputs of their contralateral
counterparts (Fig. 1C), it is
plausible to suggest that these neurons form a reciprocal inhibitory pair.
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Discussion |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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; Gilbert and
Strausfeld, 1991), the receptive fields of target-sensitive
neurons of the dragonfly ventral nerve cord coincide with the location of the
dorsal acute zone (Olberg,
1981). It has more recently been shown that dragonflies fix the
location of the prey in this dorsal acute zone during pursuit
(Olberg et al., 2007). These
behavioral investigations showed dragonflies to give a peak preference for
small targets of 0.2–0.5° of the visual field
(Olberg et al., 2005) while
CSTMD1 described here shows a peak response to slightly larger targets
(Fig. 2). Direct or indirect
CSTMD1 output to descending pathways remains to be shown. However, CSTMD1
could still be involved in prey detection and capture by modulating the
response of other STMDs.
The anatomy of hoverfly STMDs (Barnett
et al., 2007; Nordström
et al., 2006) and of blowfly target neurons
(Strausfeld, 1991) shows
relatively constrained arborizations and small axons. Compared to these, the
dragonfly CSTMD1 is huge (Fig.
1C). In dipterans, neurons of equal size are usually involved in
optic flow analysis, necessitating input from a large part of the visual
field. The receptive field of CSTMD1 is also larger than its dipteran
counterparts (Fig. 1B). The
huge output arborizations (Fig.
1C) would furthermore provide opportunities for synaptic feedback
to other lobula neurons, as well as to its contralateral partner. This latter
case might provide an opportunity for focusing on one target in a swarm of
prey – thus optimizing successful catch rates – as indicated by
the intriguing spatial interactions shown in
Fig. 6.
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Acknowledgments |
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Footnotes |
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Present address: Division of Neurobiology, University of Arizona, Tucson,
AZ 85721, USA
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References |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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