Eight pairs of descending visual neurons in thedragonfly give wing motor centers accuratepopulation vector of prey directionPaloma T. Gonzalez-Bellidoa,b,1, Hanchuan Penga,2, Jinzhu Yanga,3, Apostolos P. Georgopoulosc,4,and Robert M. Olberga,d

aJanelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147; bProgram in Sensory Physiology and Behavior, Marine BiologicalLaboratory, Woods Hole, MA 02543; cDepartment of Neuroscience, University of Minnesota, Minneapolis, MN 55455; and dDepartment of Biology, UnionCollege, Schenectady, NY 12308

Edited by Richard A. Andersen, California Institute of Technology, Pasadena, CA, and approved October 26, 2012 (received for review June 20, 2012)

Intercepting a moving object requires prediction of its futurelocation. This complex task has been solved by dragonflies, whointercept their prey in midair with a 95% success rate. In this study,we show that a group of 16 neurons, called target-selective descend-ing neurons (TSDNs), code a population vector that reflects thedirection of the target with high accuracy and reliability across360°. The TSDN spatial (receptive field) and temporal (latency)propertiesmatched the area of the retinawhere the prey is focusedand the reaction time, respectively, during predatory flights. Thedirectional tuning curves and morphological traits (3D tracings) foreach TSDN type were consistent among animals, but spike rateswere not. Our results emphasize that a successful neural circuitfor target tracking and interception can be achieved with few neu-rons and that in dragonflies this information is relayed from thebrain to the wing motor centers in population vector form.

vision | invertebrate | predatory behavior | electrophysiology |confocal microscopy

Dragonflies continuously track their prey (1) during shortpredatory flights (∼200–500 ms) (1–3) by keeping the image

of the moving prey on the specialized dorsal area of their eyes (1).If the prey image drifts on the retina, compensatory motor signalssent to the wings adjust the dragonfly body position to bring theprey image back. This process allows a dragonfly to visually trackthe prey by locking on to it, a process also named fixation (4).The ability to fixate on a moving target is a common feature

among most predatory animals. Once a pursuer’s eyes are fixatedon the prey, it can aim toward the current or the future prey lo-cation. The first choice results in classical pursuit, whereas thesecond one yields interception. Dragonflies are thought to in-tercept their prey by keeping a constant bearing to their target.This strategy is also used by other species, e.g., a human catchinga ball (5, 6), but the nervous system of dragonflies presents a fa-vorable substrate for studying the neural basis of this behavior.This ancient prey-targeting system is tractable and tuned for ex-treme performance, as evidenced by the accuracy (around 95%)(1, 2) and speed at which it functions.We aimed to understand the information sent to the wings

when a target moves across the dragonfly visual field. In partic-ular, we have tested whether the population vector algorithm cansuccessfully decode the directional component of the descendinginformation. The population vector is the weighted sum activityof an ensemble of neurons with directional tuning. It was firstshown to predict the direction of an upcoming arm movement inmonkeys (7, 8). Since then, the population vector algorithm hasdescribed successfully the directional responses to mechanicalstimulation in several invertebrate species (9–12). The dragonflypredatory flight provides a challenge for a reliable vector code.Not only are these animals highly maneuverable, with inde-pendent control of the fore and hind wings (13) and the ability tohover and even fly backward (14), but prey interception alsodemands a highly accurate representation of the prey’s directionvector (15). Nonetheless, computer modeling by Vaidyanathan

et al. (15) suggested that this algorithm could drive an airbornetarget interception system successfully.In the dragonfly brain, a group of neurons called small target

movement detectors (STMDs) respond when a small target movesin a broad range of directions (16). Although there is no evidenceshowing that STMDs code a population vector, they are believed tobe upstream of the target-selective descending neurons (TSDNs)(16, 17). TSDNs are thought to command the dragonfly trajectoryduring fast aerial attacks (5) for the following reasons: (i) eachTSDNtype, identifiable by its profile in the brain, behaves as a distinctfeature detector, with a particular preference for target location,direction, and size (18, 19); (ii) TSDNs receive the information aboutthe position and direction of a small target and relay it to all three(prothoracic, mesothoracic, and metathoracic) ganglia (18, 19); (iii)the axon diameters of the TSDNs are among the largest found in theventral nerve cord (VNC) (18), affording a high conduction speednecessary for the behavior; and (iv) electrical stimulation of in-dividual TSDNs alters the position/angle of the wings (20). There-fore, it is important to examine whether TSDNs use a populationvector to accurately code prey direction and control wingmovement.In the CNS, there are only eight pairs of TSDNs with receptive

fields facing dorsofrontally. We hypothesized that this small num-ber of TSDNs is sufficient to transmit a finely tuned populationvector that codes the direction of prey movement. To test this, wecarried out intracellular recordings and confocal imaging of theTSDNs in the dragonfly Libellula luctuosa. Our results show thatthe population vector of the TSDNs codes with extreme accuracythe prey direction in all of the 360° tested.

ResultsElectrophysiology. We recorded from a total of 51 TSDNs from38 different animals as follows: MDT1, 7 recordings; MDT2, 4recordings; MDT3, 13 recordings; MDT4, 6 recordings; MDT5,2 recordings; DIT1, 7 recordings; DIT2, 5 recordings; DIT3, 7recordings. TSDNs respond only to small moving targets withinthe dorsofrontal visual field (Fig. 1 A–C) and not at all to wide-field movement such as grating patterns (21). The identity of eachrecorded TSDN was ascertained from its target-direction pref-erence and receptive field area. To sample TSDN responses in anunbiased manner and with high precision, we projected upon

Author contributions: P.T.G.-B. and R.M.O. designed research; P.T.G.-B., H.P., and J.Y.performed research; P.T.G.-B., H.P., A.P.G., and R.M.O. analyzed data; and P.T.G.-B.,H.P., A.P.G., and R.M.O. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

See Commentary on page 389.1To whom correspondence may be addressed. E-mail: paloma@mbl.edu.2Present address: Allen Institute for Brain Science, Seattle, WA 98103.3Present address: Northeastern University, Shenyang 110819, China.4To whom correspondence should be addressed. E-mail: omega@umn.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1210489109/-/DCSupplemental.

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a white screen a sequence of 3,497 trajectories, each composedof a moving dot that appeared in a random location and movedin a random direction, but always with a straight trajectory (Fig.S1A). To allow long recordings and prevent TSDN inhibition,the target size was small, subtending 1.48° (diameter) on thedragonfly’s eye, its speed fast (134°/s) and the trajectory timeshort (100 ms).The latency between stimulus onset (first target movement)

and the earliest TSDN response (Fig. S1B) was not significantlydifferent among TSDN types (Fig. 1D). Thus, the timing of in-formation arrival was similar among all TSDNs at the tempera-ture tested (23–25 °C). This was also the case for the off latency(timing of last response – timing target movement cessation).After pooling, the TSDN onset latency (29.94 ± 5.75 ms, mean ±1SD) and offset latency (28.82 ± 9.11 ms) were not significantlydifferent (paired Student t test, P = 0.848, n = 51) (Fig. 1D).These results indicate that, within the 100 ms time window ofeach target trajectory, the latency between stimulus onset and cellresponse did not vary significantly.The responses from 51 cells were grouped into eight TSDN

types, whose electrophysiological properties (Fig. 2) and braintraces (Fig. S2) show homology to those described in the drag-onfly Anax junius (18, 19). For example, DIT1, DIT3, and MDT1all have receptive fields contralateral to their descending axons,whereas the rest (MDT5, MDT3, DIT2, MDT2, and MDT4)have receptive fields ipsilateral to their descending axons (Fig. 2and Fig. S2).Although all TSDNs were excited by a wide range of target

directions and the total number of spikes for a TSDN type variedamong animals (Fig. S3), the peak direction preferences wereTSDN-type specific and consistent (Fig. 2 A and B, polar plots).The tuning width of the directional tuning curves varied between32 and 60° (Fig. S4A). The widest direction-tuning curve is that ofDIT3, which may result from its sensitivity to an expandingstimulus (Fig. S4B). A similar range of tuning widths has beenreported in the cells coding for direction in the monkey motorcortex (22). The basis for such cell-to-cell tuning-width differ-ences remains to be clarified in both cases.In addition to the consistency in the location and size of the

receptive field, the zones of increased sensitivity (where a highernumber of spikes were recorded in response to a moving target)also remained similar among replicas of each TSDN type (Fig.S5). To assess whether the preferred target direction is a func-tion of a specific area within the receptive field, for each screenpixel the mean target direction was calculated from all of thetargets that triggered a spike. For most cells, the preferred di-rection was homogeneous across the receptive field. However,DIT3 and MDT3 also displayed responses that were not direc-tionally sensitive [Fig. 2 A and B, direction receptive field (DRF)maps, area beyond the midline with highly variable coloration].In the case of MDT3, such responses were tightly localized, andthus correlated with the location of the target stimulus. Hence,these responses are not likely to reflect intrinsic cellular noise.Instead, it is probable that these two cells receive additional,nondirectional, inputs from both eyes. This is important becauseonly DIT3 responded significantly to an expanding target,a stimulus type that included moving edges in all directions (Fig.

S4B). Moreover, preliminary data from the Libellulid dragonflyLibellula pulchella indicates that, although MDT3 does not respondto a linearly expanding stimulus, it does so when an acceleratedexpansion, quasi-looming stimulus is presented in the middle of the

Fig. 1. Small moving targets rapidly activate TSDNs.View of a L. luctuosa head from behind (A) and side(B) where the gray cone symbolizes the visual fieldof the dorsal area. The eyes are false colored in red(dorsal eye) and green (rest of the eye). A target(flying insect) moving above the dragonfly fromfront to back crossing the left visual field (yellowarea) in the direction indicated by the arrow, excitesthe TSDN MDT1. (C) Polar plot with the “God’s eyeview” reference used to display target directions,i.e., the target movement shown in A and B is here shown as a vector pointing down to the blue zone. (D) Summary of the latencies. The latencies obtained foreach TSDN type were not significantly different from each other (one-way ANOVA).

Fig. 2. Each TSDN type shows a unique direction tuning curve and receptivefield consistent across animals. (A) Contralateral and (B) ipsilateral TSDN re-ceptivefieldswhose axonswere in the right connectiveof the ventral nerve cord(VNC). The polar plots show the directional preference of each recorded TSDN(red dots) and their mean direction tuning distribution (black bars). The redarrow indicates mean preferred direction. The color-coded direction receptivefield (DRF) maps show the mean direction preference at each pixel, which wascalculated by averaging the direction peak, at each pixel, for all recordings ofeach TSDN type. In addition, spike-triggered average (STA) maps are shown foreach TSDN type. Note that, because number of spikes was normalized beforeand after computing the average, the same scale applies to all STA maps.

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visual field (MDT3 responses per accelerated expansion stimulus,4 spikes; n = 3). We note here that receptive field laterality wasinadvertently switched for two TSDNs, MDT1 and MDT3, in theprevious description of TSDN receptive fields (19). The correctedreceptive field maps for Aeshnids agree with those reported herefor Libellulids.In all TSDNs, the “hot spots” of increased sensitivity are lo-

cated near the visual midline. Although the size of our screen didnot cover the receptive fields in the frontal-caudal axis entirely,the hot spots seemed to be placed dorsofrontal. This trendbecomes more apparent when all of the spike-triggered average(STA) maps for each TSDN type are added and mirrored. Thisoperation yields a sensitivity map for the space sampled by botheyes (Fig. 3A; see Fig. S6 for dragonfly head angles during therecordings). Our results show that dragonflies boast a matcheddetection system, with increased sensitivity to moving targets inthe area of the retina where the prey image is focused duringaerial hunts (1). In addition, plotting the directional tuning curvefor each TSDN demonstrates that the directions pointing towardthe back of the animal are sampled more intensely (Fig. 3B).

Morphology. The overlapping direction tuning curves and re-ceptive fields of the different TSDNs suggest that the populationvector from all TSDN responses could code a change in the prey’sbearing. However, to calculate a plausible population vector foreach wing, knowledge of the TSDN arborization in wing motorcenters is necessary. Moreover, to make morphological compar-isons among the different TSDNs, the level of anatomical con-sistency of each TSDN type across different animals within thesame species must be assessed first.Earlier reports were studies of the TSDNs of the large

“hawking” dragonflies of the family Aeshnidae, but the speciesused in this study belongs to a family of smaller “perching”dragonflies, the Libellulidae. We have confirmed, by electro-physiology (Fig. 2) and brain morphology (Fig. S2), homologybetween TSDNs from Aeshnids and Libellulids, but with onestriking difference. In the prothoracic ganglia of L. luctuosa,MDT2,MDT3, andMDT4 travel through the dorsal intermediatetract (DIT) (Fig. S7).In all but one preparation, only one TSDN was injected in each

animal. Three-dimensionally imaged volumes of the complexformed by the mesothoracic and metathoracic ganglia and thefirst abdominal ganglion were registered and warped for com-parison (Fig. 4 A and B). The traces for each TSDN of the sametype are remarkably consistent across all injected samples (Fig.4C). All of the TSDNs targeted the dorsal part of mesothoracicand the metathoracic ganglia (Fig. 4C) dedicated to motor/ef-ferent fibers in insects. Thus, if the TSDN branching pattern doesnot filter the electrical activity in a ganglion-specific manner, theinformation provided by each TSDN should be available nearlysimultaneously to both the fore and hind wings of the targetedside. Once the TSDN morphology was confirmed across severalanimals, we chose the cell with the most extensive fill for com-parisons among TSDN types. According to the complexity of

their branching, we divided TSDNs into two categories: simpleor complex.Unilateral simple TSDNs, DIT1, DIT2, and MDT2, consis-

tently target four ipsilateral locations in the lateral region of themesothoracic and the metathoracic ganglia (Fig. 4C). DIT1morphology is closely matched by DIT2 and MDT2 (Fig. 4D),although our best MDT2 fill is incomplete. MDT1 and MDT4show bilateral asymmetry in their outputs (Fig. 4C), but a pair-wise comparison with unilateral DIT1 reveals that the branchesof MDT1 and MDT4 target the four stereotypical lateral loca-tions in both sides of the ganglia (Fig. 4D). Indeed, when weinjected the DIT2 cell on the left connective and the MDT1 onthe right connective of the same animal, the contralateralbranches of MDT1 extended to the same location as those of theunilateral DIT2 (Fig. 4B).We have categorized DIT3, MDT5, and MDT3 as complex

cells due to the additional number of short and highly variablebranches found in the medial region of the ganglia (Fig. 4C).However, within the side of the connective in which they descendfrom the brain, the morphology of these complex cells also tar-gets the four lateral locations mentioned previously (Fig. 4E).

TSDN Population Vector Coding. Because all of the TSDNs termi-nals arborize in similar areas, it is plausible that their outputscode a population vector that is converted into wing motormovements. However, a plausible population vector calculationmust take into account the receptive fields and unilateral/bi-lateral TSDN branching morphology. Simmons (23) reportedthat the major motor neurons involved in flight control haveextensive branching in the ipsilateral side, but rarely cross themidline. Therefore, we chose a parsimonious approach in whichthe branching side of the TSDNs indicated the input to thecorresponding wing. To understand which TSDNs would formpart of a population vector, we focused on the TSDN activityelicited by targets moving on the left side of the dragonfly’s visualfield. We looked at the TSDNs with left receptive fields (Fig. 2plus the corresponding mirror plots that would account for theTSDNs present in the opposite connective) and noted whethertheir outputs feed the ipsilateral (left) and/or contralateral(right) wing (Fig. 4 plus the corresponding mirror traces thatwould account for the TSDNs present in the opposite connec-tive). Hence, we obtained the population of TSDNs contributingto the vectors for the wings ipsilateral (left) and contralateral(right) to the target movement (Fig. 5A). We then binned thetarget directions (and corresponding cell activity) every 18° andcalculated the population vector (Fig. 5B) (Materials and Meth-ods and SI Text). The results show that both population vectors,for the left and the right wing, are strongly correlated with targetmovement in all 360 directions tested (Fig. 5 C, i and D, i).Moreover, the accuracy is such that the population vectors andtarget movements are not significantly different in any of thetested directions (Fig. 5 C, ii and D, ii). Thus, the impressiveability of the system to code the target motion direction accu-rately is unaffected by the fact that the TSDN tuning curves donot sample all possible directions equally.

Fig. 3. TSDN sensitivity is matched to the predatory behavior. (A) The visual receptive fields of the TSDNs combine to create an area of increased sensitivity totarget movement near midline. For more details on eye coordinates, see Fig. S6. (B) DIT1, DIT3, and MDT1 transmit motion information in the visual fieldcontralateral to their axons in the VNC. MDT5, MDT3, DIT2, MDT2, and MDT4 transmit motion information in the visual field ipsilateral to their axons in theVNC. Together, all eight axons travel through the ventral connective and thoracic ganglia.

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In the absence of a moving target, the basal activity of TSDNsis zero. Therefore, a target moving in the antipreferred directiondoes not reduce the spiking rate. For such a system to code targetdirection equally well in all 360°, a minimum of four cells withcosine directional tuning are needed (24). We investigatedwhether all eight TSDN types are necessary for an accurate codeof the target direction. We removed various numbers of TSDNtypes, from one (i.e., seven TSDN types left) to seven (i.e., oneTSDN type left), from the population vector calculation andassessed the resulting performance. We found that, on average, ifthe population vector is calculated with six or more TSDN types,

the vector is not significantly different from the target direction(t test, mean difference left wing, 1.23 ± 3.72° SD, P = 0.089; rightwing, 1.19469 ± 3.49° SD, P = 0.07624; Fig. 5C, iii and D, iii), andthus its accuracy is not affected. The two exceptions arise whenDIT2+MDT3 are removed from the ipsilateral wing calculationand MDT3+MDT4 are removed from the contralateral wingcalculation; these changes result in large coding errors (pop-ulation vector direction – target direction = 20.06 and 19.28°,respectively). When only two TSDN types were used in the cal-culations, the constant bias in the population vector outcome,also called constant error, was not far from the target direction(mean population vector direction − target direction, left wing,9.67°, and right wing, 14.50°). However, the variable error, whichcan be regarded as a plus-or-minus confidence interval on theconstant error, was large (mean error, left wing, 29.52°, and rightwing, 40.12°), making the code unreliable. On average, at leastthree types of TSDNs are needed to keep the coding error witha constant bias below the 10° of the presented target direction. Todescribe the accuracy of this system in more detail, the activity ofall TSDNs will have to be recorded with extracellular probes inthe same animal. This is because although the preferred directionfor a type of TSDN is highly constant between animals, the mag-nitude (the spike rate) of the cell response is not (Fig. S3). Theproperties acquired in this study would allow appropriate TSDNtype identification on extracellular traces. This information is nec-essary to investigate simultaneous responses from several TSDNswithin one animal.

DiscussionWe have shown that TSDN responses are suited to act as matchedfilters for the retinal slip of the prey during the predatory be-havior. TSDNs are most sensitive to targets moving in the frontal-dorsal region of the eye, where dragonflies fixate the prey imageduring interception flights (1). It would be informative to study ifthe other insects known to visually track and fixate on targets,such as mantis (25) and hover flies (26), also have a group ofneurons most sensitive to target motion away from the retinalfovea. Moreover, although a descending looming neuron hasbeen recorded extracellularly in a mantis species (27), TSDNsin this taxa are yet to be reported.Our results show that the TSDN properties make these cells

excellent information channels for a prey-direction populationvector that steers wing movements during predatory flights. Theseproperties include the following: (i) overlapping receptive fields,(ii) wide and overlapping direction tuning curves, (iii) latencies ofsimilar duration, and (iv) shared branching organization and lo-cation at the wing motor centers.We have confirmed homology between the TSDNs in repre-

sentatives of two dragonfly families with markedly different for-aging behavior. The subject of this study was L. luctuosa (familyLibellulidae, most of whose members forage from a perch).TSDNs were originally characterized in dragonflies of familyAeshnidae, which detect and pursue prey during continuousflight. We showed that, in L. luctuosa, three of the TSDNs travelin a different tract of the thoracic ganglia. We have also foundthat MDT3 travels through the DIT in another libellulid species(Libellula lydia; Fig. S8). Given that such tracts are laid downearly in development and are highly conserved among insectgroups (28), it is possible that this variation in pathway reflectsa change in the neural “wiring” of the wing control circuitry. Forexample, a homologous neuron involved in swimming patterngeneration has been found to carry out different functions in twoclosely related sea slugs (nudibranchs) that exhibit similar swim-ming modalities (29). To address such evolutionary questionsabout the dichotomy between the TSDNs in hawker and percherdragonflies, the mesothoracic and metathoracic branching pat-terns of the Aeshnid TSDNs also need to be investigated.It is interesting that all TSDNs target the mesothoracic and

metathoracic ganglia with four identifiable locations in the mostlateral region of each ganglion. These results raise two ques-tions. First, as the dragonfly fore and hind wings are controlled

Fig. 4. In the wing motor centers, all TSDNs share morphological features. (A)The mesothoracic, metathoracic, and first abdominal ganglia were imaged andwarped to allow morphological comparison. (B) Unilateral DIT2 on the left andbilateral MDT1 on the right, injected in the same animal, target the samelocations. (C) Traces within each TSDN type (grouped according to the electro-physiological results) are consistent, so the most complete fill from each TSDNtype was used for comparison. TSDNs were categorized into “simple” or“complex” cells. A pairwise comparison (dorsal views) shows that unilateralsimple cells, DIT1 (green), DIT2 (red), andMDT2 (magenta), are indistinguishablefrom each other (D, Upper), but the bilateral simple cells MDT1 (white) andMDT4 (yellow) display specific branching patterns (D, Lower). However, allsimple TSDNs target the same location. In contrast, pairwise comparisons be-tween the complex cells, DIT3 (red), MDT3 (green), and MDT5 (cyan) (E, allpanels) are less informative because their additional intricate branching exhibitshigher variability, particularly in the medial region of the ganglia (traces in C).

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independently (30), what additional information alters the TSDNmotor inputs to yield a specific movement for the rear and thefore wings? Further studies on the central pattern generatorsunderlying the dragonfly flight motor are necessary to answer thisquestion. Second, because the projection from leg sensory neu-rons into the thoracic ganglia of locusts follows a 3D somatotopicmap (31), do the four locations of TSDN branching reveala pattern of signal integration with other localized sensoryinputs? Each dragonfly wing has four sensory structures calledcrevice organs. Crevice organs resemble the campaniform sen-silla of insects and are believed to act as mechanoreceptors forthe degree of wing twist (32). In the locust, campaniform sensillameasure lift and twisting amplitude of the hind wings (33). Be-cause TSDN activation alters the attack angle of the wing (20), itseems likely that information from the TSDNs and input fromthe crevice organs is combined in some manner, but a follow-upinvestigation on the information flow from the TSDNs to thewing motor neurons is needed.Previous reports have shown that the underlying principles

used by dragonflies to calculate motion (17) and orientation (16)are similar to those found in vertebrate systems. Here, we haveshown that population vector code could successfully directtarget interception in these ancient animals. The populationvector algorithm has been coined the “geometrical universalactuator” because sensory information coded in vector form canbe used directly for appropriate motor actuation, without a needfor a time-consuming transformation (12). Our data furthersupport this view. Even though the visual information flowsthrough the brain, the TSDN population vector carries the in-formation about prey direction, and the appropriate compensa-tory forces are implemented in the wing motor centers. Thus, thepopulation vector could allow this steering system to act as an“autopilot” (see ref. 34 for a discussion on the feasibility of au-tomated steering in insects), providing the fast reaction speed

displayed by dragonflies during predatory behavior [∼30 ms fromstimulus onset to compensatory wing strokes (1)].The dragonfly system shows a significant level of robustness;

even when only six TSDN types are used for the calculation, thepopulation vector is still not significantly different from the targetdirection. Unlike the population vector coding in the leech (9),TSDNs do not sample the visual space and target directionshomogenously. Bias in direction sampling has also been reportedin the cells of the monkey motor cortex, which code populationvectors for arm movements (35). Moreover, in insects, the visualperformance of motion detection is matched to the species-spe-cific flight movements (36), a topic recently reviewed by Taylorand Krapp (37). Regardless of such bias, for the 2D plane testedin this study, TSDN population vector coding performs equallywell across all directions. It is possible that the bias in the pre-ferred direction of the different TSDNs becomes significant oncethe animal is presented with a more realistic target showingfreedom of movement in all three dimensions (i.e., includingcontraction or expansion of the prey image).Although we have focused on prey pursuit, dragonflies also

pursue larger targets, such as conspecifics in male/male territo-rial chases. In such nonpredatory flight trajectories, collision maynot be desirable, and an interception strategy may not be ben-eficial. It remains to be elucidated whether the TSDNs are usedin an exclusively predatory context, or whether they play a part inflights with different strategies.In summary, the fast and highly precise prey interception flight

system of dragonflies is particularly amenable to investigation atmany levels: electrophysiological, morphological, and behavioral.The ultimate goal is to elucidate the neural underpinnings frommolecular basis to behavior, something already achieved for theswimming movement of the lamprey (38). The electrophysio-logical and morphological evidence presented here support thenotion that TSDNs provide population vector information abouttarget movement that can be used to direct wing movement

Fig. 5. The TSDN population vector codes direction of the prey with high accuracy. (A) A target moving in the left side of the visual field activates the subset ofTSDNs shown in the diagram. The population of TSDNs providing inputs to the left (nine cells) and right (seven cells) wings differ. (B) Graphical representation ofa dragonfly TSDN population vector. Contributing TSDN vectors (green), stimulus direction (yellow), and population vector (red) are shown. (C and D) Pop-ulation vectors for the left (C) and right (D) wings were calculated for all presented trajectories. (C, i and D, i) The population vectors, binned in 18°, are stronglycorrelated with the target direction. Left wing circular R = 0.9986 (P < 0.001) and right wing R = 0.9984 (P < 0.001). (C, ii andD, ii) The direction of the target andthe direction of the population vector are not significantly different in any direction because the difference between these two parameters is close to zero(dotted values) and because zero is within the 95% confidence interval (white bars). Colors refer to the direction of the presented target. The data concerntargets moving in the left side of the visual field, so targets that traveled toward the medial part of the animal correspond to the red section. (C, iii and D, iii) Onaverage, removing two types of TSDN from the population (six cell types used) does not impact the accuracy of the population vector significantly. For the leftwing, three TSDN types are, on average, sufficient to provide a population vector whose bias is within 10° of the presented target direction.

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during a predatory flight. This system relies on a low number ofcells performing with high accuracy. The functional significanceof this result is that, even in an insect that displays a behavioras complex as interception, the integration of signals and cal-culation of motor output seem to occur at the thoracic ganglia,and not in the brain. A process as seemingly complicated asinterception appears automated to yield the high speed andperformance necessary for insect predation. This study is ofparticular importance because dragonflies are the only insectgroup that exclusively sport direct wing muscles, allowing in-dependent control of both sets of wings. With the view to de-veloping bio-inspired robots, recent studies have investigateddragonfly vision (39) and wing dynamics (40). Thus, understandingthe visually driven neural control of the dragonfly wings mayprove invaluable for future technological applications. The 3Datlas presented here forms a basis for further understandingof this circuit.

Materials and MethodsThe ventral nerve cord of adult L. luctuosa was impaled with a sharpelectrode while the animal viewed a visual display (360 Hz). TSDNs wererecognized initially by robust responses to a vertical or horizontal small

moving target of fixed size, increased response for certain target direc-tions, and lack of responses to a wide-field stimulus. A stimulus composedof 3,497 consecutive small moving targets was presented, and the resultantcell activity was used to calculate its latency, directional tuning (see refs. 41and 42) and receptive field. Looming sensitivity was tested with a linearlyexpanding stimulus. Using negative current (−2 nA), we injected 32 of theTSDNs with Lucifer yellow. The preparations were processed and imagedwith a confocal microscope (Zeiss 710; see ref. 43). To visualize the cellmorphology, we used Vaa3D (www.vaa3d.org) (44, 45) and BrainAligner(46). The population vector was calculated with a bootstrap method, withtuning curves calculated by binning target directions every 18° and aver-aging cell activity for those bins. The population vector calculation wasbased on weight 12 in appendix 1 of ref. 47. See Fig. S9 and SI Materials andMethods for more information.

ACKNOWLEDGMENTS. We thank Anthony Leonardo and the HowardHughes Medical Institute for providing resources necessary for this study;Elliot Imler and Paul Herold for animal husbandry; Hang Xiao, Yinan Wan,and Yan Zhuang for helping with our analysis of neuron morphology;Trevor Wardill for suggestions for the manuscript; and Ian Stevenson, whoprovided help with application of the MATLAB analytical toolbox. We alsothank the Air Force Office of Scientific Research for support (Grant FA9550-10-0472 to R.M.O.).

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