REVIEW

The insect central complex and the neural basis ofnavigational strategiesAnna Honkanen, Andrea Adden, Josiane da Silva Freitas and Stanley Heinze*

ABSTRACTOriented behaviour is present in almost all animals, indicating that it isan ancient feature that has emerged from animal brains hundreds ofmillions of years ago. Although many complex navigation strategieshave been described, each strategy can be broken down into a seriesof elementary navigational decisions. In each moment in time, ananimal has to compare its current heading with its desired directionand compensate for any mismatch by producing a steering responseeither to the right or to the left. Different from reflex-drivenmovements,target-directed navigation is not only initiated in response to sensoryinput, but also takes into account previous experience andmotivational state. Once a series of elementary decisions arechained together to form one of many coherent navigationstrategies, the animal can pursue a navigational target, e.g. a foodsource, a nest entrance or a constant flight direction duringmigrations. Insects show a great variety of complex navigationbehaviours and, owing to their small brains, the pursuit of the neuralcircuits controlling navigation has made substantial progress over thelast years. A brain region as ancient as insects themselves, calledthe central complex, has emerged as the likely navigation centre ofthe brain. Research across many species has shown that the centralcomplex contains the circuitry that might comprise the neuralsubstrate of elementary navigational decisions. Although this regionis also involved in a wide range of other functions, we hypothesize inthis Review that its role in mediating the animal’s next move duringtarget-directed behaviour is its ancestral function, around which otherfunctions have been layered over the course of evolution.

KEY WORDS: Neuroanatomy, Insect brain, Motor control,Navigation, Sensory integration

IntroductionA defining feature of animals is their ability to move. Movementsrange from reflex-driven escape responses to highly optimizedforaging trips in complex environments. All oriented movementsthat are not solely controlled by reflexes are considered navigationalbehaviours for the purpose of this Review. Irrespective of whetherthese behaviours last for seconds, hours or months, they have incommon that they are directed towards either a transient or a stablenavigational target. This target can be a randomly chosen headingrelative to a landmark or compass direction, an inherited migratorybearing, a food stimulus or the site of an animal’s nest (Heinze,2017). Although diverse strategies can be used to generate acoherent, target-directed navigation behaviour, for each of theman animal has to internally define a desired direction. To be able to

align its body with this direction, it additionally has to establish itsown heading angle. If the two directions do not match, a body turnhas to be initiated to compensate for the difference.

Although this basic idea applies to most animals, insects haveproven to be powerful model species to illuminate the underlyingbehavioural and neural principles. This can be attributed to the richrepertoire of sophisticated navigation behaviours that insectsachieve despite their comparably simple brains. The insect brainis uniquely accessible for detailed functional and anatomicalexamination at the level of single cells, neural circuits and entirebrain regions. Large strides have thus been made recently to uncoverthe neural basis of navigation in a range of species, findings that cannow be related to the results from decades of behavioural work.

A brain region called the central complex (CX) has shifted intothe focus of this work and has gained the status of the navigationcentre of insect brains (Pfeiffer and Homberg, 2014; Turner-Evansand Jayaraman, 2016; Heinze, 2017). Whereas navigational controlis clearly not the only function of the CX, we propose the idea thatthe neural circuits responsible for fundamental navigation decisionsmight represent the ancestral role of this brain region, to whichsecondary functions have been subsequently added in response toincreasingly complex demands of different species’ ecologies.

The central complexThe CX is a unique midline neuropil found in all insects examined todate. It originated more than 400 million years ago and has changedintriguingly little over all this time (Homberg, 2008; Strausfeld,2009). In all species, it consists of the fan-shaped body and ellipsoidbody (FB and EB; for alterative names, see Box 1), the protocerebralbridge (PB) and a pair of noduli (Pfeiffer and Homberg, 2014;Strausfeld, 2012). A characteristic feature of the CX is its highlyregular neuroarchitecture consisting of 16–18 vertical columnsintersected by horizontal layers (Heinze and Homberg, 2008;Homberg, 2008; Williams, 1975; Wolff et al., 2015; Lin et al.,2013). Tangential cells provide input to entire horizontal layers ofeach CX neuropil from a great variety of brain regions (Fig. 1C).Different types of columnar cells innervate single columns and, witheach cell type existing in sets of eight to nine individual neurons perhemisphere, in principle one for each column (e.g. Heinze andHomberg, 2008; Wolff and Rubin, 2018) (Fig. 1D). They link singlePB columns with corresponding columns in either the FB or the EB,thereby producing a stereotypical, interhemispheric projection matrix(Fig. 1D). The main output pathway connecting the CX todownstream brain centres is also composed of large columnar cellsprojecting to the lateral accessory lobes (LAL; Franconville et al.,2018; Heinze and Homberg, 2008; Heinze et al., 2013).

As is required for navigational control, the CX has a variety ofsensory inputs. The coding of celestial compass cues has been mostthoroughly investigated, but neurons of the CX also respond tomechanosensory information from the antennae and wings, visualfeatures of the environment, as well as large-field motion cues. Yet,

Lund Vision Group, Department of Biology, Lund University, 22362 Lund, Sweden.

*Author for correspondence (stanley.heinze@biol.lu.se)

S.H., 0000-0002-8145-3348

1

© 2019. Published by The Company of Biologists Ltd | Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

the CX is not primarily a sensory brain region, but has long beenknown to be involved in locomotor control (Strausfeld, 1999;Strauss and Heisenberg, 1993). Evidence was obtained fromDrosophila mutants with structural CX defects and associatedlocomotor deficiencies (Strauss, 2002; Triphan et al., 2010), as wellas from surgical lesions, electrophysiological recordings andinjection of electrical current in behaving cockroaches (Harleyand Ritzmann, 2010; Ridgel et al., 2007; Bender et al., 2010; Guoand Ritzmann, 2013; Martin et al., 2015). The latter experimentsestablished a causal relationship between CX activity and control ofspecific movements of the animals.In addition to sensory-motor integration, the CX is instrumental

for a range of other phenomena. The Drosophila CX is required forspatial working memory during navigation tasks (Ofstad et al.,2011; Neuser et al., 2008). Changes in properties of CX responsesaccording to the animal’s behavioural and motivational state werefound in Drosophila (Weir and Dickinson, 2015; Weir et al., 2014)and cockroaches (Martin et al., 2015). Additionally, the insect’sarousal level affects whether CX output leads to motor action, asshown by data on sleep control and gating of locomotor behaviourin the Drosophila CX (Donlea et al., 2018). Finally, the CX playsa direct role in sensing the nutritional state of the animal(in Drosophila; Park et al., 2016) and encodes memory ofbehaviourally relevant (aversive) visual shapes (Liu et al., 2006).

Navigational decisions as a common framework for CXfunctionHow can all these data be aligned within a common framework ofCX function? As outlined above, a comparison between the desiredheading and the current body orientation is essential for all planned,targeted behaviours. If these two angles do not align, compensatorysteering decisions have to be generated. These elementarynavigational decisions have to be carried out on a moment-to-moment basis and are independent of the overall navigation strategy.If this hypothesis is true, navigation strategies should only differ inthe way the intended heading is computed and in the sensoryinformation used to compute the current heading. The identical

basic circuit could thus mediate all navigation behaviours, withslight modifications adjusting for subtly different demands ofspecific strategies. Additionally, whether a sensory input is selectedas a potential target for navigation during this elementary decisionprocess can be expected to depend on the animal’s motivationalstate and its previous experience – features that attach relevance tosensory information. In light of this hypothesis, we will reviewdiverse behavioural strategies and the role of the CX circuits inmediating them in the following sections.

Behavioural strategiesStraight-line orientationBall-rolling dung beetles depend on moving along a straight course(Byrne et al., 2003). They shape their dung balls when they arrive ata dung pile and have to escape this area of fierce competition asquickly as possible to permanently secure their ball as a foodreservoir. The most efficient way to get away from the dung pile is toroll along a straight course in a randomly chosen direction (Fig. 2A)(Byrne et al., 2003; el Jundi et al., 2016). Yet, this seemingly simpletask is far from trivial. Without external reference, any animal,including humans, will very quickly walk in circles (Cheung et al.,2007). Indeed, a variety of celestial cues keep these beetles on astraight path. They not only use the Sun (Dacke et al., 2014) and theskylight polarization pattern (el Jundi et al., 2014b), but also theskylight intensity gradient (el Jundi et al., 2014b) and the spectralgradient (el Jundi et al., 2015a). Nocturnal species use the Moon(Dacke et al., 2004), the nocturnal polarization pattern (Dacke et al.,2003) and the Milky Way (Dacke et al., 2013) to keep their ball-rolling trajectories straight. Similarly to dung beetles, Drosophilawas recently shown to follow straight courses in randomly chosendirections relative to a simulated sun (Giraldo et al., 2018) or a stablelandmark (Green et al., 2018 preprint). In both insects, thisbehaviour depends on a functioning CX.

Long-distance migrationLong-distance migrations are seasonal movements of animals fromone region to another to escape unfavourable conditions in theirhabitat. Although this strategy is vastly different from the dungbeetle’s rolling behaviour in terms of both geographical and temporalscale, both involve following a straight course until an animal reachesthe anticipated favourable conditions, without the need to measuredistance. The basic navigation decisions at each moment in time arethus similar between the strategies. A key difference is, however,that the direction of migration is not randomly chosen, butgenetically fixed across the entire population of migrating animalswithin a species. The best-described long-distance migratorsamong insects are the monarch butterfly (Danaus plexippus)(Reppert and de Roode, 2018), the bogong moth (Agrotis infusa)(Warrant et al., 2016) and the desert locust (Schistocerca gregaria)(Homberg, 2015) (Fig. 2C). Indeed, in all three species, the CX hasbeen implicated in processing navigation-relevant sensoryinformation. Although much of the evidence for the involvementof the CX has resulted from locust studies (reviewed in Homberget al., 2011), the migratory patterns of this species are less wellunderstood than those of the monarch butterfly or the bogongmoth, with locust swarms often migrating downwind rather thantowards a specific target (Homberg, 2015).

Migrating over thousands of kilometres to a specific region ischallenging. First, the migratory heading needs to be followedprecisely, as any deviation would mean missing the target. Second,after weeks of following their migratory heading, the insects have tostop close to the target area to find their precise resting site – certain

List of abbreviationsAOTU anterior optic tubercleAOTU-LUC lower unit complex of the anterior optic tubercleAOTU-UU upper unit of the anterior optic tubercleBU bulbCX central complexDRA dorsal rim areaDRME dorsal rim medullaEB ellipsoid bodyFB fan-shaped bodyGA gallGNG gnathal ganglionINP inferior neuropilLAL lateral accessory lobeLLAL lower lateral accessory lobeLU lower unitNO noduliNU nodular unitPB protocerebral bridgePLP posterior lateral protocerebrumPon pontine neuronSNP superior neuropilULAL upper lateral accessory lobeUU upper unitVMNP ventromedial neuropils

2

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

mountain tops in the case of the monarch butterfly (Merlin et al.,2012) and specific alpine caves for the bogong moth (Warrant et al.,2016). It is unknown what triggers the switch between the long-range migratory behaviour and the short-distance searchingbehaviour. Although both species might use olfactory cues to findtheir target sites, concrete evidence supporting this hypothesis isstill lacking and neural work has focused on the long-rangecomponent of migration.

Path integrationDifferent from the previous two behaviours, finding the way back toa place of origin (homing) requires distance tracking in addition todirectional information. Path integration is a computational strategythat enables homing. During path integration, direction and distanceinformation are continuously integrated to update the animal’sinternal estimate of its current position in relation to a fixed origin.Arthropods, especially central-place foraging hymenopterans suchas ants and bees, have long been known to use this strategy for

returning home in a straight line after a tortuous outbound trip tofind food (Fig. 2E) (for a review, see e.g. Heinze, et al., 2018;Wehner and Srinivasan, 2003). Recently, path integration was foundto also occur without the context of homing, when Drosophila wasshown to use it for returning back to a food source after performingshort exploratory excursions (Kim and Dickinson, 2017).

The internal estimate of the animal’s position with respect to anorigin contains information about the shortest path to that point, i.e.the home vector. It is computed by continually integratingmemories of the distances travelled in each direction during theoutbound trip (Fig. 2E) (Wehner and Srinivasan, 2003; Heinzeet al., 2018). The directional component of the home vectorbecomes the desired heading as soon as an insect on a foraging triphas found enough food to warrant carrying it back to the nest. Thepath integrator thus switches from ‘accumulating’ to ‘following’.Once the end of the vector has been reached, a systematic search forthe nest entrance is initiated (Fig. 2E). Search behaviour is key forsuccessful homing, as it compensates for the errors accumulated

Box 1. Nomenclature of CX neuropils and neurons across insect speciesAll neuropils of the central complex clearly correspond betweenDrosophila (iii) and other insects, with the central body upper unit (CBU) being equivalent tothe fan-shaped body (FB) and the central body lower unit (CBL) matching the ellipsoid body (EB). For the associated neuropils of the lateral complex, thecorrespondence is not completely obvious. Whereas the gall (GA) and the bulb (BU) are easily identified in most insects (GA reduced in locusts; BU oftenconsisting of several parts), the lateral accessory lobe (LAL) appears to contain parts of the crepine (CRE) region ofDrosophila in other insects. In locusts (i),all or most of the upper LAL (ULAL) seems to correspond to the fly’s CRE, whereas in moths and butterflies (ii) the parts of the LAL immediately surroundingthe mushroom body medial lobe likely correspond to parts of the CRE. This correspondence on the level of brain regions can be extended to the identity ofsingle neuron types and names of identified homologues across species are given in the table below.

For comprehensiveness, discontinued, former names of neuropils are shown in brackets. Images in (i) and (ii) are from www.insectbraindb.org; data arefrom el Jundi et al. (2009) (locust) and de Vries et al. (2017) (bogong moth). Drosophila data are courtesy of Arnim Jenett (Tefor Core Facility, UniversitéParis-Saclay, France). BU, bulb; CBL, central body lower unit; CBU, central body upper unit; CRE, crepine; EB, ellipsoid body; FB, fan-shaped body; GA,gall; LAL, lateral accessory lobe; LBU, lateral bulb; LLAL, lower LAL; MBU, medial bulb; NO, noduli; PB, protocerebral bridge; ULAL, upper LAL.

Moth/butterfly

CBUCBL

LAL NO

BU

GA

PB

(ii)

a

pm

l

d

v

Locust

CBU

CBLULAL

LLAL

MBU (medial olive)

NO

PB

LBU(lateral triangle)

(i)

a

pm

l

d

v

Drosophila

FB

EB

CRE

LAL (ventral body)

GA

PB

NO

BU(lateral triangle)

(iii)

a

pm

l

d

v

Neuron type names

Drosophila

Other insects Consensus name Alternative name Systematic name (Wolff et al., 2018)

TL2, TL3 R neurons Ring neurons n.d.TN1,TN2 L-N,L-NA LAL.s-NO3P/Mi.b, LAL.s-NO3Ai.bCL1a E-PG Wedge neuron PBG1-8.b-EBw.s-D/V GA.bCL1b P-EG PBG1-9.s-EBt.b-D/V GA.bCL2 P-EN Tile neuron PBG2-9.s-EBt.b-NO1.bTB1 Delta7‡ PB intrinsic neuron PB18.s-GxΔ7Gy.bCPU1a/b-type1 P-F-R* Horizontal fibre system PBG1-8.s-FBℓ3,4,5.s.b-ROB.bCPU1-type2§ PF-LC* Horizontal fibre system PBG1-7.s-FBℓ2.s-LAL.b-CRE.bCPU4 P-FN e.g. PBG2-9.s-FBl1.b-NO3P.bCPU2 n.d. PB-FBc,d,e,f-IL+R-HB-medial

¶ n.d.

*Likely correspondence; difficult as Drosophila targets of these cells are not easily found in other insects.‡In insects with a posterior optic tubercle, these cells have a sidebranch in this neuropil.§So far only described in monarch butterflies (Heinze et al., 2013) and bogong moths (de Vries et al., 2017).¶Identified only in Lin et al. (2013).n.d., not determined.

3

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

during path integration (Cheng et al., 1999; Wehner and Srinivasan,1981; Heinze et al., 2018).Behavioural readouts, e.g. homing flights, search behaviour and

the honeybee waggle dance (Fig. 2E,F), are visible manifestations ofthe internal position estimates acquired via path integration. Throughstudying these behaviours in bees and ants, we have gainedsubstantial insight into the nature of the insect’s path integrator.Directional (compass) information is obtained by observing celestialcues (Fig. 2E,F) (Evangelista et al., 2014;Wehner andMüller, 2006),generating an internal reference frame in global coordinates. By

accumulating optic flow, i.e. the apparent movement of theenvironment across the retina resulting from self-motion, duringflight, bees also derive distance information from visual information(Esch and Burns, 1996; Srinivasan et al., 2000). In contrast, thepedestrian ants primarily use a step integrator for estimating distance(Wittlinger et al., 2006).

Visual route followingOne feature shared between all previously described navigationstrategies is that they do not involve long-term memory. The target

Diptera

Lepidoptera

Coleoptera

Hymenoptera

Blattodea

Schistocerca gregaria

Ryparobia maderae

Megalopta genalis

Scarabaeus lamarcki

Agrotis infusa

OdonataEphemeropteraZorapteraDermapteraPlecopteraOrthopteraMantophasmatodeaGrylloblattodeaEmbiopteraPhasmatodeaMantodea

IsopteraThysanopteraSternorrhynchaHeteropteraColeorrynchaAuchenorrynchaPsocodea

RaphidiopteraMegalopteraNeuropteraStrepsiptera

Trichoptera

ShiphonapteraMecoptera

NO

EB

FB

PB

Drosophila

A B

C D

L8

L7L6 L5 L4 L3 L2 L1

NO LAL

FB

PB

R1 R2 R3 R4 R5 R6R7

R8

LALLAL

BU

FB

EB

NO

PB

SNP

INP

VMNP

GA

PLPCPU1

LAL

N

VMN

GA

P

A

NP

SNP

INP

LAL

VMNP

PLP

L

CL1a

L8

L7L6 L5 L4 L3 L2 L1 R1 R2 R3 R4 R5 R6

R7

R8

EB

PB

GA GA

Fig. 1. Anatomy of the central complex (CX). (A) Location of the CX (colour) in the insect brain (the sweat bee Megalopta genalis, from Stone et al.,2017). Image source: www.insectbraindb.org. (B) The CX is conserved across a wide range of insects. Data are from el Jundi et al. (2009) (locust), Wei et al.(2010) (cockroach), Stone et al. (2017) (sweat bee), Immonen et al. (2017) (dung beetle), de Vries et al. (2017) (bogong moth) and Jenett et al. (2012)(Drosophila). Images are from www.insectbraindb.org for all species except the cockroach and Drosophila. (C) Input pathways to the different CX components.Input to the protocerebral bridge (PB), ellipsoid body (EB) and noduli (NO) is shown on the left side, while fan-shaped body (FB) input is shown on the right.(D) Columnar neurons form highly stereotypical intrinsic connections between the PB and the FB/EB. Note that different cell types form projection patterns thatare shifted with respect to one another, so that identical PB columns are mapped to different FB/EB columns. Based on locusts and the monarch butterfly(Heinze and Homberg, 2008; Heinze et al., 2013). BU, bulb; GA, gall; INP, inferior neuropil; LAL, lateral accessory lobe; PLP, posterior lateral protocerebrum;SNP, superior neuropil; VMNP, ventromedial neuropil.

4

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

Fig. 2. See next page for legend.

5

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

direction is either inherited (long-distance migrants) or it is maintainedfor a short time only, indicating that the information is stored inworking memory. The latter applies to the home vector during pathintegration as well as to the direction of ball rolling that a dung beetlechooses for a particular trip. Yet, there are other navigation behavioursthat require long-term memory (Collett and Collett, 2002). The mostprominent such strategy is route following, using landmarks or visualsnapshots (Collett, 2010; Zeil et al., 2014). Like path integration, routefollowing is used for homing in hymenopterans (ants, bees and wasps)and many species rely on a combination of both strategies to return tothe nest (Collett, 2012). Particularly in landmark-rich environments,these insects learn the arrangement of visual features along theforaging path (Narendra, 2007; Narendra et al., 2013). The establishedroutes are stable over time and override information from pathintegration, especially close to the nest (Wystrach et al., 2015;Wehneret al., 1996; Kohler and Wehner, 2005). The memory of such routeslikely comprises snapshots of the visual panorama around the nest andalong the taken path, which are matched to the currently perceivedvisual input (Fig. 2G,H) (Zeil et al., 2014). By attempting tomaximizethe match between the current view and the memorized view, theinsect can follow a valley of minimal image difference and thus locatehome using a familiar route (Zeil et al., 2003).

Directional information in the central complex – the internalcompassOne piece of information that is required for all of the describedstrategies is directional input that is used to compute the bodyorientation with respect to the environment. Across locusts,butterflies, beetles, flies and bees, this direction coding involveshighly conserved brain regions: the anterior optic tubercle (AOTU),the lateral complex and the CX (Fig. 3A–E). Many neurons in theseregions are tuned to celestial compass cues and indicate the animal’sazimuth by modulating their firing rates (Homberg et al., 2011;Heinze and Reppert, 2011; Stone et al., 2017; el Jundi et al., 2015b).In flies, visual landmarks (Seelig and Jayaraman, 2013), rotationaloptic flow and non-visual angular velocity cues (Green et al., 2017;Turner-Evans et al., 2017) were shown to mediate direction codingin neurons homologous to the sky compass neurons. Althoughcockroach neurons were recorded that responded similarly to thoseof flies, their morphological identity has not yet been identified

(Varga and Ritzmann, 2016). Importantly, allothetic (external) cuesare represented in the CX at the same time as idiothetic (internallygenerated) cues, suggesting that diverse sources of informationare continuously integrated to generate a robust representation ofbody orientation.

Neurons of the compass pathway upstream of the CX (Fig. 3A)already encode heading based on multiple celestial cues in anintegrated way, such as the position of the Sun combined with theskylight polarization pattern (Heinze and Reppert, 2011; el Jundiet al., 2014a,b; Pegel et al., 2018) or polarized light combined withspectral information (Pfeiffer and Homberg, 2007). This use ofseveral, partly redundant visual compass cues likely increases theaccuracy of the heading estimate. Additionally, shown viabehavioural studies, the geomagnetic field is used by migratorybutterflies (Guerra et al., 2014), ants (Fleischmann et al., 2018) andmoths (Dreyer et al., 2018), information that might feed into the samecircuit. In bogongmoths, magnetic fields are used in conjunctionwithvisual landmark cues, suggesting that the integration of multiplesensory modalities indeed provides an advantage when elaboratenavigation abilities are required (Dreyer et al., 2018).

After directional information reaches the CX, it is translated into acoordinated activity pattern across a population of columnar neuronsconnecting the EBwith the PB (E-PG cells), yielding a single activitybump across the width of both structures that tracks the animal’scurrent heading (Seelig and Jayaraman, 2015). Each of the eight PBcolumns in each hemisphere thus represents one azimuth direction,together tiling the entire horizon and conveying meaning to theregular, columnar neuroarchitecture (Fig. 3G). In flies, this headingsignal was found to be tethered to visual features of the environment,i.e. provide directional reference based on local cues (Seelig andJayaraman, 2015). In migratory locusts, a similar mapping ofdirections was found in PB neurons tuned to polarized skylight, thusindicating a heading code rooted in a global, Sun-based referenceframe (Heinze andHomberg, 2007). Different from flies, in which thezero-point of the direction map (phase) shifts arbitrarily betweenindividuals and experiments, the polarized-light-based direction mapin locusts is identical across individuals. Flies therefore appear toremap their compass based on local cues in each new environment,whereas locusts appear to possess a fixed reference frame (Fig. 3G).

Although the heading direction activity bump is linked to externalcues, in Drosophila and cockroaches, corresponding activity wasalso detected in darkness, revealing that idiothetic rotational cuescan also be used to generate and update the position of the observedactivity bump (Green et al., 2017; Turner-Evans et al., 2017).Angular movement of the animal in one direction yields increasedactivity in columnar cells called P-EN neurons in one hemisphere ofthe PB. Between the PB and EB, these cells are recurrentlyconnected with the E-PG cells, and an offset in the columnarprojections between both cell types (Wolff et al., 2015) leads to ashift of the activity-bump position along the heading map, wheneverthe activity of P-EN cells in one hemisphere exceeds that in the otherhemisphere (Fig. 3H). Owing to the excitatory recurrence betweenE-PG and P-EN cells that underlies this bump shifting, inhibition isrequired to prevent the excitation from spreading through all CXcolumns. Indeed, in Drosophila, global inhibition, likely mediatedvia Delta7 cells (Franconville et al., 2018), is combined with localrecurrent excitation to form a ring attractor circuit (Kim et al., 2017).Although this circuit allows for tracking the fly’s angularmovements even in darkness, it does not yet explain how externaldirectional cues are integrated with idiothetic rotation cues.

Integration of external cues is essential to avoid the accumulationof errors resulting from continuous estimate of body rotations

Fig. 2. Insect navigation behaviours. (A) Ball-rolling dung beetles usecelestial cues for choosing a random travel direction. Left: illustration ofbehaviour; right: rolling directions are random with respect to the Sun (datafrom Byrne et al., 2003). (B) During a circular dance on the dung ball, thesebeetles take a snapshot of the celestial cue constellation to decide on a rollingdirection. Beetle images in A and B courtesy of B. el Jundi and M. Dacke.(C) Monarch butterfly and bogong moth migratory routes. Both insects flyover 1000 km from their breeding grounds to the wintering/summering sites.(D) When navigating by celestial cues, migrating insects need to compensatefor the time of day. (E) A honeybee foraging flight with navigation-relevantvisual cues used to compute a vector representation of the hive’s position(path integration). Orange triangle: hive. Looping pattern at the end of inboundflight: systematic search to locate nest entrance. Flight track adapted fromDegen et al. (2016). (F) Skylight polarization is an ambiguous directional cue.Bees communicate their directional knowledge from the outbound flight(top panels) via the waggle dance (bottom panels). Dance distributions arebidirectional when only polarized light cues are available (adapted fromEvangelista et al., 2014). (G) Image matching, as performed by ants in richvisual environments (Collett et al., 2013), is used for following habitual routes.A series of snapshots of the visual panorama are compared with currentviews and both are brought to a best match via body rotations (Zeil, 2012).(H) Landmark-based navigation uses salient visual features independent ofthe surrounding panorama in a similar way to overall image matching. G and Hare adapted from Heinze (2017).

6

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

Fig. 3. See next page for legend.

7

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

(Cheung et al., 2007). Without allothetic calibration, the describedDrosophila circuit by itself cannot be used for tracking body anglesfor path integration over distances relevant to bee homing (Heinzeet al., 2018) or for long-range migration.Even though the systematic mapping of headings in the PB found

in locusts andDrosophila has not yet been extended to other insects,all the major neuron types identified as components of the compasscircuit across the different CX neuropils (TL, CL, TB1 and CPU1neurons) also exist in dung beetles (el Jundi et al., 2015b), monarchbutterflies (Heinze and Reppert, 2011) and bees (Stone et al., 2017).As these cells resemble their locust counterparts in great detail(Fig. 3F), it seems likely that the ordered heading representation inthe PB columns is conserved across insects. If true, this generalencoding of body orientation solves the fundamental problem ofrepresenting the current heading of the animal. How is this directioncode combined with other information and, ultimately, used to driveconcrete navigation behaviours?

Integrating directions and distanceHow the current heading representation is integrated with otherinformation is not known. However, examining path integration inbees with a combination of electrophysiology, anatomy andmodelling has led to a testable hypothesis about neural circuitmechanisms downstream of the described heading encoding. Forpath integration, information about the animal’s forward velocityhas to be combined with heading information (Srinivasan, 2015). Inthe tropical bee Megalopta, optic-flow-based speed signals wereshown to arrive at the CX via two types of noduli neurons (TNneurons; Fig. 4A), revealing that optic flow converges with compassinformation in the CX (Stone et al., 2017). While one type isinhibited by simulated forward flight and excited by simulatedbackward flight, the other behaves the opposite way (Fig. 4B).Together, the detailed response characteristics of these cells(Fig. 4C) allow the faithful encoding of holonomic movements,i.e. bee-typical movements during which the movement direction ismisaligned with body orientation.The converging distance and direction information must be

continuously integrated and stored to generate a path integrationmemory. Anatomical data suggest a columnar cell type of the FB,the CPU4 neurons, as neural substrate for this integration (P-FN inDrosophila) (Stone et al., 2017). These neurons have arborizations

in the PB, the noduli and the FB (Fig. 4A), an anatomy that, intheory, could allow compass information from the PB and opticflow information from the noduli to reach these cells. Directsynaptic contacts between TN ‘speed’ neurons and fibres likelybelonging to CPU4 cells in the noduli (Stone et al., 2017),combined with reported polarized-light responses in locust CPU4cells (Heinze and Homberg, 2009), support this hypothesis.Dendritic arbours overlapping with CPU4 output branchessuggest the main CX output cells (CPU1 cells; PF-LC cells inDrosophila) as principal targets of these cells.

To generate an activity-based working memory by accumulatingoptic flow over time, Stone et al. (2017) postulate recurrentconnectivity between the PB and noduli between all CPU4 cells ofone column. To gain sufficient capacity for encoding andmaintaining accumulated neural activity, a large number ofneurons would be needed. Supporting this idea, each CX columnin the bumblebee (Bombus terrestris) contains at least 18 individualCPU4 neurons (Stone et al., 2017). In the model, TB1 direction cellsdirectly inhibit the proposed memory cells (CPU4), whichadditionally receive speed input in the noduli. The least inhibitedmemory cells can thus accumulate optic-flow-based speedinformation. Across the CPU4 population, such a hypotheticalmemory mechanism would yield an array of direction-lockedodometers (Figs 5B,C and 6A). Throughout any foraging trip, thepopulation of these cells could thus encode distance and direction ofthe animal with respect to the nest (the home vector) as a pattern ofneural activity distributed across the FB columns.

Towards a general concept of encoding desired headingsHow generally applicable is the outlined representation of the homevector? In dung beetles, the suggested similarity of current-headingencoding would allow the navigational control circuitry that uses thisinformation to be laid out in a similar way as well. Different fromhoming, the beetle’s intended heading is a random direction (Byrneet al., 2003; el Jundi et al., 2016). The ball-rolling direction is chosenwhen the beetle performs a short circular dance on top of its freshlyformed dung ball, just before embarking on its trip (Fig. 2B) (el Jundiet al., 2016). During this time, the beetles take a celestial snapshot, i.e.memorize the current configuration of all available celestial cues.Whenever the beetle is disturbed during the subsequent trip, it repeatsthis dance to realign itself with the originally chosen direction (elJundi et al., 2016). These animals thus have to store a single anglewithin the reference frame provided by the available celestialinformation. Although this desired direction does not contain anydistance information, it nevertheless resembles the angularcomponent of the home vector resulting from path integration. Ittherefore seems conceivable that the same neurons proposed toencode the home vector as a population-coded activity bump acrossthe FB columns (CPU4) also have this function in dung beetles. Howcould the activity bump in these neurons be created during thebeetle’s orientation dance? While the answer is unknown, one canspeculate that the activity pattern generated by the heading code in thePB is transferred to CPU4 cells in a randomly chosen moment duringthe dance. Similar to the home vector, this activity bump would bemaintained by the recurrent CPU4 connections between noduli andthe PB (Fig. 6B). To allow the imprinting of the current heading ontothe CPU4 population, the dance behaviour would have to generate ashort sensitive period, e.g. by causing the release of neuromodulatorsthat facilitate synaptic transmission between the head-direction cellsand the CPU4 cells.

In the context of migration, the described path integration modelalso suggests a possibility of how a genetically fixed migratory

Fig. 3. Compass encoding in the insect CX. (A) Brain of the tropical beeMegalopta genalis. Highlighted are neuropils involved in compass encoding(CX, lateral complex, AOTU) as well as the pathway for processing polarizedskylight (POL pathway) (data from Zeller et al., 2015; Pfeiffer and Kinoshita,2012). (B–E) Brains with highlighted navigation-relevant regions for dungbeetle (B), monarch butterfly (C), bogong moth (D) and desert locust (E). Dataare from Immonen et al. (2017), Heinze et al. (2013), de Vries et al. (2017) andel Jundi et al. (2009). Images are from www.insectbraindb.org. (F) Neuronsinvolved in processing of compass cues in the dung beetle (images courtesy ofB. el Jundi). (G) Top: representation of the angles of polarized skylight in thePB columns of the locust (Heinze andHomberg, 2007). Bottom: correspondingmapping of body orientation of Drosophila with respect to surroundinglandmarks (Seelig and Jayaraman, 2015). (H) Tracking of heading directions inDrosophila: asymmetrical activity in the P-EN neurons (CL2 in other insects) ofthe right and left PB hemispheres during turning shifts the E-PG neuron-encoded activity bump in the EB, allowing angular integration of body rotations.Arrowheads indicate directional tunings of E-PG neurons. Adapted fromGreenet al. (2017). AOTU, anterior optic tubercle; AOTU-LUC, lower unit complex ofthe AOTU; AOTU-UU, upper unit of the AOTU; BU, bulb; DRA, dorsal rim area;DRME, dorsal rim medulla; EB, ellipsoid body; FB, fan-shaped body; GA, gall;GNG, gnathal ganglion; INP, inferior neuropil; LAL, lateral accessory lobe;LLAL, lower lateral accessory lobe; LU, lower unit; NO, noduli; PB,protocerebral bridge; ULAL, upper lateral accessory lobe.

8

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

heading might be encoded in the CX. It could involve the sameCPU4 neurons suggested to encode the home vector. In migrants, aCPU4-mediated desired heading would have to be genetically fixedto always point towards the migratory target. In theory, this could berealized by sinusoidally adjusting the weights of the CPU4 outputsynapses. Any cell receiving their population activity would sensean activity bump, even though the ongoing activity of each CPU4cell would be identical because of shared, flight-driven optic-flowinput (Fig. 6C) (Heinze, 2017). To reverse the target direction forthe return migration, the synaptic weight distribution across the FBcolumns could be inverted by external stimuli during the resting

period. Indeed, in monarch butterflies, the reversal of theirmigratory direction is triggered by exposure to cold temperatures,mimicking conditions in their overwintering grounds (Guerra andReppert, 2013).

During long-range migration, using celestial cues as a reference fordetermining headings presents an additional problem that can besolved in one of several ways using the presentedmodel. The positionof celestial cues depends on the Earth’s rotation and thus on daytime.If an animal were to simply follow the Sun, its apparent movementthroughout the day would lead the animal in circles. Therefore, theSun’s azimuth needs to be reinterpreted according to time of day to

Fig. 4. Convergence of sensory information in the CX as a basis for path integration. (A) Convergence of speed and direction input in the CX of thebeeMegalopta (left). CX location (green) in the brain is shown alongside polarized-light input pathway (purple arrows) and the likely route for optic flow information(blue arrows). Inset: proposed cellular substrate for convergence for speed and direction inputs are the CPU4 columnar neurons, one of which is depicted(green). At least 18 CPU4 cells exist for each CX column. (B) Normalized mean activity of three TN2 ‘speed’ neurons in response to different velocities oftranslational optic flow. Coloured circles, mean activity; grey circles, individual data points; solid lines, background activity ±s.d. Figure from Stone et al. (2017),with permission from Elsevier. (C) The four TN neurons in the Megalopta CX possess four preferred expansion points of translational optic flow (as predictedby local motion tunings), providing the basis for holonomic motion encoding; based on Stone et al. (2017).

9

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

maintain a constant bearing (Fig. 2D). This means that either thecompass itself has to shift its zero-point relative to the Sun over thecourse of the day (allowing the representation of the target direction toremain constant), or the representation of the target direction wouldhave to shift with respect to the compass. Alternatively, the output ofthe entire circuit could be modulated according to time of day.Although a recent study models how the sun compass may be time

compensated (Shlizerman et al., 2016), it does not suggest a specificneural substrate and it remains to be shown whether the neuralcircuitry for representing current and desired headings is involved inazimuth compensation.

Route following differs from the three behaviours alreadydescribed in that the navigational target is recorded in long-termmemory. These memories can last for a lifetime and have been

Compass input (CBL/EB)

TB1CL1a

PonPon

PB

CB

U/F

BN

O TN(left)

TN(right)

Optic flow input (speed)

Compass(currentheading)

Steering

Memory(desired heading)

Memory balance

Speed

A

Excitation

Inhibition

Inactive connection

CPU4 (left)

CPU4 (right)

TB1

CPU1

OutboundReturn

100 steps

50 steps

1 m

10 m

B

E

CPU4Goal direction(home vector)

Compassheading

Steering rightSteering leftSpeed input

TB1

CL1a

TN

C Memory acquisition (outbound) Homing (return)

State switch

CPU1

D

F

CPU1(left)

CPU4(right)

CPU1(right)

CPU4(left)

Turn left Turn right

Fig. 5. The CX as neural substrate for path integration. (A) Schematic connections between cell types in the proposed Megalopta path integrator network(Stone et al., 2017). Arrows: excitation; blunt ends: inhibition. Pon, pontine neurons. (B) Topology of the path integration model circuit. Circles represent neurons,size of neurons indicate activity level; colour code for model layers and cell groups is as in A. Arrows in the circles: directional tuning (TB1) and integrateddirection preference (CPU4); R: right turn; L: left turn; compass rose: current (green) and desired heading (orange). (C) Illustration of the two activity bumpsresulting from encoding current heading (green, in PB and EB) and target direction (yellow, in FB). The target direction results from integrating speed andcompass information. (D) Steering is induced by comparing PB activity with FB activity column by column. The resulting imbalance in CPU1 neuron activitybetween the right and left side causes steering. (E) Homing behaviour produced by the path integrator model comparedwith desert ant data (below). Near the nestsite (triangle), the model initiates searching behaviour. (F) Comparison of a desert ant (right) and the model negotiating obstacles during the homeward journey.E and F from Stone et al. (2017); insets in E and F modified from Wehner (2003) (reproduced with permission from Elsevier).

10

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

proposed to reside in the mushroom body (Ardin et al., 2016). As nodirect connection exists between the mushroom bodies and the CX,it is currently unknown whether or how this strategy can be aligned

with the above-described circuitry. In principle, however, duringroute following the insect also has to match current and desiredheadings. Given that the current heading is likely encoded in the PB

Path integration memory Genetically encoded goal direction for migration

PB

RecurrentCPU4 cells

A

E

C

D

CPU4Goal direction(home vector)

Compassheading

Speed input

TB1

TN

PB

NO

FB

PB

NO

CPU4

FB

Trigger from memory match

PB

NO

CPU4

FB

Flight input(optic flow)

SE

N

CPU4

Fixed synaptic weights FB

PB

NO

CPU1steering

FB

B

CPU1steering

CPU1steering

CPU1steering

CPU1steering

W

TN

Ancestral circuit?Short-term maintenance of current heading

to counteract disturbance

NOShort-term memory for sustained

straight movement in random directionShort-term memory for straight

movement towards memorized goalsTrigger from, e.g. dung beetle dance

Fig. 6. A common framework for encoding navigational decisions in the insect CX. (A) Illustration of how compass signals from the PB could be integratedwith a speed signal arriving at the noduli to produce a distributed representation of the home vector across FB columns during path integration, based on themodel by Stone et al. (2017). In the model, the home-vector memory resides in CPU4 neurons, which form recurrent connections between PB columns and theNO. At least 18 individual CPU4 cells per column exist in bumblebees (Stone et al., 2017), suited to form local microcircuits to sustain a lasting, activity-basedworking memory. CPU4 output in the FB could directly signal the desired heading to steering cells. (B) Similarly, a direction code can be achieved for straight-lineorientation in dung beetles (or menotactic orientation in random directions in general) by transferring the current heading signal from the PB to CPU4 neurons.A modulatory signal, e.g. during the dung beetle dance, could trigger the imprinting of the direction code on the CPU4 population. Stability of the memoryover the time of the behaviour would require several CPU4 neurons per column. (C) Adjusting synaptic weights between CPU4 and CPU1 cells in a sinusoidalmanner would allow genetic encoding of migratory directions. Evenly distributed activity across all CPU4 cells (driven by optic flow input) would signalan activity bump to the CPU1 cells according to synaptic weight distribution, guiding the animal towards the migratory heading upon deviation. (D) A similarmechanism could be used for route following based on memorized visual snapshots. If the current view of the animal matches a memorized snapshot, theresulting positive valence output from the memory centres (e.g. mushroom body) could serve as trigger to imprint the current view as a temporary desiredheading (Collett and Collett, 2018). (E) Based on the path integration model by Stone et al. (2017), we propose a simplified circuit, lacking memory, speedinput and the large number of CPU4 cells as possible ancestral circuit, suited to store a copy of the current heading representation in CPU4 neurons for a shortamount of time. In case of disturbance, the original heading can be regained via the CPU1-based steeringmechanism, using the information stored in CPU4 cells.

11

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

and steering likely involves the CX output, it would be mostparsimonious to assume that the desired heading for route followingis also encoded in the FB columns (Fig. 6E). Indeed, tangential FBinput neurons possess dendrites in regions of the brain that receivemushroom body output projections (Heinze et al., 2013; Young andArmstrong, 2010). These output cells of the mushroom bodygenerally encode the valence of a sensory cue, i.e. indicatewhether aperceived cue had been associated with a positive or negativeexperience (Aso et al., 2014). During route following, this wouldmean that whenever the image similarity between the current viewand a remembered view is high, the output would signal a positivevalence, which could be interpreted as a trigger to fix the currentheading, e.g. based on skylight polarization or visual landmarks, asgoal direction (Collett and Collett, 2018). A positive match couldthus trigger an imprinting of the current head-direction activitypattern in the PB onto the population of CPU4 neurons in the FB in away similar to that proposed for the dung beetle (Fig. 6E).

Navigational steeringAcross all strategies, once the navigating animal has determined itsdesired and current headings, it has to steer to compensatemismatches. During homing via path integration, steeringcommands have been proposed to originate in the CX output cells(CPU1). These neurons are anatomically suited to combine thesignals of CPU4 memory neurons with the input from TB1 compassneurons (Figs 5A–C and 7B; Stone et al., 2017), thereby comparing

activity patterns representing the current and desired headings. Asthe projection pattern of CPU1 cells is shifted by at least one columnwith respect to CPU4 neurons (in opposite directions in eachhemisphere) (Heinze and Homberg, 2008; Wolff et al., 2015), thecomparison of TB1 and CPU4 activity patterns will lead to animbalance in the total activation of CPU1 cells between the right andthe left hemispheres, whenever the two activity patterns are notaligned (Stone et al., 2017). This uneven activation will causesteering into the direction that will push the animal back towards itsgoal (Fig. 5).

Although this direct impact of CPU1 cells on steering ishypothetical, two findings support the proposed model. First, datafrom cockroaches have revealed direct effects of CX neurons onturning behaviour (Martin et al., 2015). And second, studies in themoth Bombyx mori revealed that the LAL contains descending ‘flip-flop’ cells that are directly involved in steering (Namiki andKanzaki, 2016; Olberg, 1983). These bi-stable neurons ‘flip’between two activity states, a highly active one and a less active one,in response to a pheromone pulse or a light flash, and thecontralateral flip-flop neuron is usually in the opposite state to theipsilateral one. As the flip-flop neurons project directly onto neckmotor neurons, each flip of activity leads to changed activity of theneck motor neurons, which ultimately leads to a head turn (Mishimaand Kanzaki, 1998, 1999) (Fig. 7C). Flip-flop neurons have so faronly been shown to be involved in short-distance pheromonefollowing, where they underlie the zig-zagging trajectory the male

AL

B

Object information(shapes, relevant features) (?)Output

a

LAL flip-flop neuron

Pheromonepulses

Output

Output

CBU/FB

Object information(shapes, relevant features) (?)

Translationaloptic flow

Output/input?

CPU2 CPU1CPU4 Translational

optic flow

CBU/FB

TB1

Output Output

Gall

Rotationaloptic flow

PBPOTU

C

Angularvelocity

A

GallGall

LAL

PB

NO-small

NO-large

B

Orientation behaviour

POTUPOTUCBL/EB

LAL

Compass inputVisual landmark input

Mechanosensory input wingsMechanosensory input antennae (?) Compass input

Visual landmark inputMechanosensory input wings

Mechanosensory input antennae (?)

Rotationaloptic flow

Angularvelocity

NO-smallCBL/EB

CL2 CL1aCL1b

NO-large

Ipsi-LAL

40 m

V

2 s

Contra-LAL

Pheromone plume

Fig. 7. Sensory-motor transformation in the CX as basis for navigational decisions. (A) Summary of sensory input and possible output pathways ofthe CX. (B) Detailed information flow within the CX. Several sensory pathways convey different information to different levels of the two main CX circuits, theFB circuit (orange) and the EB circuit (blue). Question marks: proposed input pathways inferred from physiological data lacking anatomical confirmation.Grey arrows: connections verified in Drosophila (Franconville et al., 2018). Dashed grey arrows: proposed connections. Coloured arrows: information flow.(C) Left: a flip-flop LAL neuron in the moth Bombyx mori responds to pheromone pulses with inversion of activity. Adapted from Mishima and Kanzaki (1998).Right: illustration of the zig-zagging plume tracking behaviour initiated by the flip-flop neurons. POTU, posterior optic tubercle.

12

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

moth uses to follow a female’s pheromone trail. However, they arealso ideally suited to transform the activity imbalance between theright and left CPU1 populations (CX output) into a steering decisionduring homing, straight-line orientation and for long-distancemigration (Namiki and Kanzaki, 2016). Moreover, the possibleconvergence of path integration memory and the desired heading forroute following on the same CPU1 neurons would also allowdynamic weighting of the target directions signalled by bothstrategies during homing.

Elementary navigation decisions as ancestral functionof the CX?As outlined above, navigation can be broken down into elementarydecisions. At eachmoment in time, the animal has to decidewhethera right turn or a left turn will bring its current heading in betteralignment with its target direction. This applies equally to straight-line orientation, long-range navigation, homing by path integrationor homing by route following. The main difference betweennavigation strategies is how the desired heading is computed(Fig. 6A–D). Once a neural representation of that direction exists,the process of comparing it with the current heading and thegeneration of steering commands faces the same constraints and,therefore, could be solved in a similar way. This suggests that, inprinciple, the circuit motifs found in the CX could be the basis forelementary navigational decisions.Given that all main types of CX cells known from across species

are required for the outlined circuit to function, the computation itperforms likely emerged when the CX evolved as a neuropil andmight constitute the principle computation of this brain area sincethe origin of insects. However, it is difficult to conceive that thecomplex navigational strategies of today’s insects are equallyancient. What relevance could this circuit have had when itoriginally evolved?We propose that elementary navigation decisions were the prime

challenge faced by early arthropods. Having evolved keen sensesand matching motor abilities, purely reflex-driven chains ofautomatic movements would not have put these new abilities toadequate use. Planned, target-directed navigation becamenecessary, a task achievable by a simplified CX path integrationcircuit (Fig. 6E). Whenever the animal moves into a specificdirection (towards or relative to a visual feature), the heading signalof the PB would be transferred to a recurrent circuit formed by theCPU4 cells. The resulting activity bump resonating between the PBand the noduli would be more stable than the actual headingdirection bump in the PB, and mismatches between the two could becompared by the CPU1 steering cells. Upon deviation from thestored direction, the circuit would push the movement directionback on track, an idea that is consistent with the findingssurrounding the principle of insect ‘turn alternation’ (e.g. Dingle,1964). Without sophisticated working memory implemented in theCPU4 circuit, the stored activity bump would only allow the insectto return to the original course for a short time, and a new targetwould be followed once the previous memory has faded. Thiswould enable a strategy during which an early insect could haveexplored its environment in a series of short, straight movements,selecting targets based on its sensory abilities and species-specificpreferences (Fig. 6E).Given the fundamental nature of elementary navigational

decisions and the existence of unpaired midline neuropils beyondinsects (Homberg, 2008; Thoen et al., 2017), this circuit might haveeven preceded the CX. A simpler circuit with lower spatialresolution than the one resulting from the population code based

on eight static reference vectors found in today’s insect CX wouldstill have enabled navigation decisions, albeit with less precision.Systematically exploring the structure and function of CX circuitsin basal insects and more distantly related arthropods could thuslead the way towards identifying the origins of elementarybehavioural decisions.

Competing interestsThe authors declare no competing or financial interests.

FundingThe authors are grateful for financial support from the following organizations:the Swedish Research Council (Vetenskapsrådet; 621-2012-2213 to S.H.), theEuropean Research Council (ERC) under the European Union’s Horizon 2020research and innovation program (grant agreement no. 714599 to S.H.), the AirForce Office of Scientific Research (2012-02205 to Eric Warrant and S.H.) andthe Wenner-Gren Foundation (to A.H.).

ReferencesArdin, P., Peng, F., Mangan, M., Lagogiannis, K. and Webb, B. (2016). Using an

insect mushroom body circuit to encode route memory in complex naturalenvironments. PLoS Comput. Biol. 12, e1004683.

Aso, Y., Sitaraman, D., Ichinose, T., Kaun, K. R., Vogt, K., Belliart-Guerin, G.,Plaçais, P.-Y., Robie, A. A., Yamagata, N., Schnaitmann, C. et al. (2014).Mushroom body output neurons encode valence and guide memory-based actionselection in Drosophila. Elife 3, e04580.

Bender, J. A., Pollack, A. J. and Ritzmann, R. E. (2010). Neural activity in thecentral complex of the insect brain is linked to locomotor changes. Curr. Biol. 20,921-926.

Byrne, M. J., Dacke, M., Nordstrom, P., Scholtz, C. H. and Warrant, E. J. (2003).Visual cues used by ball-rolling dung beetles for orientation. J. Comp. Physiol. A189, 411-418.

Cheng, K., Srinivasan,M. V. and Zhang, S. (1999). Error is proportional to distancemeasured by honeybees: Weber’s law in the odometer. Anim. Cogn. 2, 11-16.

Cheung, A., Zhang, S., Stricker, C. and Srinivasan, M. V. (2007). Animalnavigation: the difficulty of moving in a straight line. Biol. Cybern. 97, 47-61.

Collett, M. (2010). How desert ants use a visual landmark for guidance along ahabitual route. Proc. Natl. Acad. Sci. USA 107, 11638-11643.

Collett, M. (2012). How navigational guidance systems are combined in a desertant. Curr. Biol. 22, 927-932.

Collett, T. S. and Collett, M. (2002). Memory use in insect visual navigation. Nat.Rev. Neurosci. 3, 542-552.

Collett, M. and Collett, T. S. (2018). How does the insect central complex usemushroom body output for steering? Curr. Biol. 28, R733-R734.

Collett, M., Chittka, L. and Collett, T. S. (2013). Spatial memory in insectnavigation. Curr. Biol. 23, R789-R800.

Dacke, M., Byrne, M. J., Scholtz, C. H. andWarrant, E. J. (2004). Lunar orientationin a beetle. Proc. Biol. Sci. 271, 361-365.

Dacke, M., Nilsson, D.-E., Scholtz, C. H., Byrne, M. J. and Warrant, E. J. (2003).Animal behaviour: insect orientation to polarized moonlight. Nature 424, 33-33.

Dacke, M., Baird, E., Byrne, M. J., Scholtz, C. H. and Warrant, E. J. (2013). Dungbeetles use the Milky Way for orientation. Curr. Biol. 23, 298-300.

Dacke, M., el Jundi, B., Smolka, J., Byrne, M. J. and Baird, E. (2014). The role ofthe sun in the celestial compass of dung beetles. Phil. Trans. R. Soc. B 369,20130036.

Degen, J., Kirbach, A., Reiter, L., Lehmann, K., Norton, P., Storms, M.,Koblofsky, M., Winter, S., Georgieva, P. B., Nguyen, H. et al. (2016).Honeybees learn landscape features during exploratory orientation flights. Curr.Biol. 26, 2800-2804.

de Vries, L., Pfeiffer, K., Trebels, B., Adden, A. K., Green, K., Warrant, E. J. andHeinze, S. (2017). Comparison of navigation-related brain regions in migratoryversus non-migratory noctuid moths. Front. Behav. Neurosci. 11, 158.

Dingle, H. (1964). Turn alternation by bugs on causeways as a delayedcompensatory response and the effects of varying visual inputs and length ofstraight path. Anim. Behav. 13, 171-177.

Donlea, J. M., Piment, D., Talbot, C. B., Kempf, A., Omoto, J. J., Hartenstein, V.and Miesenbock, G. (2018). Recurrent circuitry for balancing sleep need andsleep. Neuron 97, 378-389.e4.

Dreyer, D., Frost, B., Mouritsen, H., Gunther, A., Green, K., Whitehouse, M.,Johnsen, S., Heinze, S. and Warrant, E. J. (2018). The Earth’s magnetic fieldand visual landmarks steer migratory flight behavior in the nocturnal Australianbogong moth. Curr. Biol. 28, 2160-2166.e5.

el Jundi, B., Heinze, S., Lenschow, C., Kurylas, A., Rohlfing, T. and Homberg,U. (2009). The locust standard brain: a 3D standard of the central complex as aplatform for neural network analysis. Front. Syst. Neurosci. 3, 21.

13

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

el Jundi, B., Pfeiffer, K., Heinze, S. and Homberg, U. (2014a). Integration ofpolarisation and chromatic cues in the insect sky compass. J. Comp. Physiol. A200, 575-589.

el Jundi, B., Smolka, J., Baird, E., Byrne, M. J. and Dacke, M. (2014b). Diurnaldung beetles use the intensity gradient and the polarisation pattern of the sky fororientation. J. Exp. Biol. 217, 2422-2429.

el Jundi, B., Foster, J. J., Byrne, M. J., Baird, E. and Dacke, M. (2015a). Spectralinformation as an orientation cue in dung beetles. Biol. Lett. 11, 20150656.

el Jundi, B., Warrant, E. J., Byrne, M. J., Khaldy, L., Baird, E., Smolka, J. andDacke, M. (2015b). Neural coding underlying the cue preference for celestialorientation. Proc. Natl. Acad. Sci. USA 112, 11395-11400.

el Jundi, B., Foster, J. J., Khaldy, L., Byrne, M. J., Dacke, M. andBaird, E. (2016).A snapshot-based mechanism for celestial orientation. Curr. Biol. 26, 1456-1462.

Esch, H. and Burns, J. (1996). Distance estimation by foraging honeybees.J. J. Exp. Biol. 199, 155-162.

Evangelista, C., Kraft, P., Dacke, M., Labhart, T. and Srinivasan, M. V. (2014).Honeybee navigation: critically examining the role of the polarization compass.Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130037-20130037.

Fleischmann, P. N., Grob, R., Muller, V. L., Wehner, R. and Rossler, W. (2018).The geomagnetic field is a compass cue in Cataglyphis ant navigation. Curr. Biol.28, 1440-1444.e2.

Franconville, R., Beron, C. and Jayaraman, V. (2018). Building a functionalconnectome of the Drosophila central complex. eLife 7, e04577.

Giraldo, Y. M., Leitch, K. J., Ros, I. K., Warren, T. L., Weir, P. T. and Dickinson,M. H. (2018). Sun navigation requires compass neurons in Drosophila. Curr. Biol.(in press) 28, 2845-2852.e4.

Green, J., Adachi, A., Shah, K. K., Hirokawa, J. D., Magani, P. S. andMaimon, G.(2017). A neural circuit architecture for angular integration in Drosophila. Nature546, 101-106.

Green, J., Vijayan, V., Mussells Pires, P., Adachi, A. and Maimon, G. (2018).Walking Drosophila aim to maintain a neural heading estimate at an internal goalangle. bioRxiv 1-43.

Guerra, P. A. and Reppert, S. M. (2013). Coldness triggers northward flight inremigrant monarch butterflies. Curr. Biol. 23, 419-423.

Guerra, P. A., Gegear, R. J. and Reppert, S. M. (2014). A magnetic compass aidsmonarch butterfly migration. Nat. Comm. 5, 4164.

Guo, P. and Ritzmann, R. E. (2013). Neural activity in the central complex of thecockroach brain is linked to turning behaviours. J. Exp. Biol. 216, 992-1002.

Harley, C. M. and Ritzmann, R. E. (2010). Electrolytic lesions within centralcomplex neuropils of the cockroach brain affect negotiation of barriers. J. Exp.Biol. 213, 2851-2864.

Heinze, S. (2017). Unraveling the neural basis of insect navigation. Curr. Opin.Insect. Sci. 24, 58-67.

Heinze, S. and Homberg, U. (2007). Maplike representation of celestial E-vectororientations in the brain of an insect. Science 315, 995-997.

Heinze, S. andHomberg, U. (2008). Neuroarchitecture of the central complex of thedesert locust: intrinsic and columnar neurons. J. Comp. Neurol. 511, 454-478.

Heinze, S. and Homberg, U. (2009). Linking the input to the output: new sets ofneurons complement the polarisation vision network in the locust central complex.J. Neurosci. 29, 4911-4921.

Heinze, S. and Reppert, S. M. (2011). Sun compass integration of skylight cues inmigratory monarch butterflies. Neuron 69, 345-358.

Heinze, S., Florman, J., Asokaraj, S., el Jundi, B. and Reppert, S. M. (2013).Anatomical basis of sun compass navigation II: the neuronal composition of thecentral complex of the monarch butterfly. J. Comp. Neurol. 521, 267-298.

Heinze, S., Narendra, A. and Cheung, A. (2018). Principles of insect pathintegration. Curr. Biol. 8, PR1043-PR1058.

Homberg, U. (2008). Evolution of the central complex in the arthropod brain withrespect to the visual system. Arthropod. Struct. Dev. 37, 347-362.

Homberg, U. (2015). Sky compass orientation in desert locusts—evidence fromfield and laboratory studies. Front. Behav. Neurosci. 9, 346.

Homberg, U., Heinze, S., Pfeiffer, K., Kinoshita, M. and el Jundi, B. (2011).Central neural coding of sky polarisation in insects. Philos. Trans. R. Soc. B 366,680-687.

Immonen, E.-V., Dacke, M., Heinze, S. and el Jundi, B. (2017). Anatomicalorganization of the brain of a diurnal and a nocturnal dung beetle. J. Comp.Neurol.525, 1879-1908.

Jenett, A., Rubin, G. M., Ngo, T.-T. B., Shepherd, D., Murphy, C., Dionne, H.,Pfeiffer, B. D., Cavallaro, A., Hall, D., Jeter, J. et al. (2012). A GAL4-driver lineresource for Drosophila neurobiology. Cell Rep. 2, 991-1001.

Kim, I. S. and Dickinson, M. H. (2017). Idiothetic path integration in the fruit flyDrosophila melanogaster. Curr. Biol. 27, 2227-2238.e3.

Kim, S. S., Rouault, H., Druckmann, S. and Jayaraman, V. (2017). Ring attractordynamics in the Drosophila central brain. Science 356, 849-853.

Kohler, M. and Wehner, R. (2005). Idiosyncratic route-based memories in desertants, Melophorus bagoti: how do they interact with path-integration vectors?Neurobiol. Learn. Mem. 83, 1-12.

Lin, C.-Y., Chuang, C.-C., Hua, T.-E., Chen, C.-C., Dickson, B. J., Greenspan,R. J. and Chiang, A.-S. (2013). A comprehensive wiring diagram of the

protocerebral bridge for visual information processing in theDrosophila brain.CellRep 3, 1739-1753.

Liu, G., Seiler, H., Wen, A., Zars, T., Ito, K., Wolf, R., Heisenberg, M. and Liu, L.(2006). Distinct memory traces for two visual features in the Drosophila brain.Nature 439, 551-556.

Martin, J. P., Guo, P., Mu, L., Harley, C. M. and Ritzmann, R. E. (2015). Central-complex control of movement in the freely walking cockroach. Curr. Biol. 25,2795-2803.

Merlin, C., Heinze, S. andReppert, S. M. (2012). Unraveling navigational strategiesin migratory insects. Curr. Opin. Neurobiol. 22, 353-361.

Mishima, T. and Kanzaki, R. (1998). Coordination of flipflopping neural signals andhead turning during pheromone-mediated walking in a male silkworm mothBombyx mori. J. Comp. Physiol. A 183, 273-282.

Mishima, T. and Kanzaki, R. (1999). Physiological and morphologicalcharacterization of olfactory descending interneurons of the male silkwormmoth, Bombyx mori. J. Comp. Physiol. A 184, 143-160.

Namiki, S. and Kanzaki, R. (2016). The neurobiological basis of orientation ininsects: insights from the silkmoth mating dance.Curr. Opin. Insect Sci. 15, 16-26.

Narendra, A. (2007). Homing strategies of the Australian desert ant Melophorusbagoti II. Interaction of the path integrator with visual cue information. J. Exp. Biol.210, 1804-1812.

Narendra, A., Gourmaud, S. and Zeil, J. (2013). Mapping the navigationalknowledge of individually foraging ants, Myrmecia croslandi. Proc. R. Soc. Lond.B 280, 20130683.

Neuser, K., Triphan, T., Mronz, M., Poeck, B. andStrauss, R. (2008). Analysis of aspatial orientation memory in Drosophila. Nature 453, 1244-1247.

Ofstad, T. A., Zuker, C. S. and Reiser, M. B. (2011). Visual place learning inDrosophila melanogaster. Nature 474, 204-207.

Olberg, R. M. (1983). Pheromone-triggered flip-flopping interneurons in the ventralnerve cord of the silkwormmoth, Bombyx mori. J. Comp. Physiol. A 152, 297-307.

Park, J.-Y., Dus, M., Kim, S., Abu, F., Kanai, M. I., Rudy, B. and Suh, G. S. B.(2016). Drosophila SLC5A11 mediates hunger by regulating K+ channel activity.Curr. Biol. 26, 1965-1974.

Pegel, U., Pfeiffer, K. and Homberg, U. (2018). Integration of celestial compasscues in the central complex of the locust brain. J. J. Exp. Biol. 221, jeb171207.

Pfeiffer, K. and Homberg, U. (2007). Coding of azimuthal directions via time-compensated combination of celestial compass cues. Curr. Biol. 17, 960-965.

Pfeiffer, K. and Homberg, U. (2014). Organization and functional roles of thecentral complex in the insect brain. Annu. Rev. Entomol. 59, 165-184.

Pfeiffer, K. and Kinoshita, M. (2012). Segregation of visual inputs from differentregions of the compound eye in two parallel pathways through the anterior optictubercle of the bumblebee (Bombus ignitus). J. Comp. Neurol. 520, 212-229.

Reppert, S. M., and de Roode, J. C. (2018). Demystifying Monarch ButterflyMigration. Curr. Biol. 28, R1009-R1022.

Ridgel, A. L., Alexander, B. E. and Ritzmann, R. E. (2007). Descending control ofturning behaviour in the cockroach, Blaberus discoidalis. J. Comp. Physiol. A 193,385-402.

Seelig, J. D. and Jayaraman, V. (2013). Feature detection and orientation tuning inthe Drosophila central complex. Nature 503, 262-266.

Seelig, J. D. and Jayaraman, V. (2015). Neural dynamics for landmark orientationand angular path integration. Nature 521, 186-191.

Shlizerman, E., Phillips-Portillo, J., Forger, D. B. and Reppert, S. M. (2016).Neural integration underlying a time-compensated sun compass in the migratorymonarch butterfly. Cell Rep 15, 683-691.

Srinivasan, M. V. (2015). Where paths meet and cross: navigation by pathintegration in the desert ant and the honeybee. J. Comp. Physiol. A 201, 533-546.

Srinivasan, M. V., Zhang, S., Altwein, M. and Tautz, J. (2000). Honeybeenavigation: Nature and calibration of the “odometer”. Science 287, 851-853.

Stone, T., Webb, B., Adden, A., Weddig, N. B., Honkanen, A., Templin, R.,Wcislo, W., Scimeca, L., Warrant, E. J. and Heinze, S. (2017). An anatomicallyconstrained model for path integration in the bee brain. Curr. Biol. 27,3069-3085.e11.

Strausfeld, N. J. (1999). A brain region in insects that supervises walking. Prog.Brain Res. 123, 273-284.

Strausfeld, N. J. (2009). Brain organization and the origin of insects: anassessment. Proc. Biol. Sci. 276, 1929-1937.

Strausfeld, N. J. (2012). Arthropod Brains. Cambridge, MA: Harvard UniversityPress.

Strauss, R. (2002). The central complex and the genetic dissection of locomotorbehaviour. Curr. Opin. Neurobiol. 12, 633-638.

Strauss, R. and Heisenberg, M. (1993). A higher control centre of locomotorbehaviour in the Drosophila brain. J. Neurosci. 13, 1852-1861.

Thoen, H. H., Marshall, J., Wolff, G. H. and Strausfeld, N. J. (2017). Insect-likeorganization of the stomatopod central complex: functional and phylogeneticimplications. Front. Behav. Neurosci. 11, 12.

Triphan, T., Poeck, B., Neuser, K. and Strauss, R. (2010). Visual targeting ofmotor actions in climbing Drosophila. Curr. Biol. 20, 663-668.

Turner-Evans, D. B. and Jayaraman, V. (2016). The insect central complex. Curr.Biol. 26, R453-R457.

14

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology

Turner-Evans, D., Wegener, S., Rouault, H., Franconville, R., Wolff, T., Seelig,J. D., Druckmann, S. and Jayaraman, V. (2017). Angular velocity integration in afly heading circuit. Elife 6, e04577.

Varga, A. G. and Ritzmann, R. E. (2016). Cellular basis of head direction andcontextual cues in the insect brain. Curr. Biol. 26, 1816-1828.

Warrant, E. J., Frost, B., Green, K., Mouritsen, H., Dreyer, D., Adden, A.,Brauburger, K. and Heinze, S. (2016). The Australian bogong moth Agrotisinfusa: a long-distance nocturnal navigator. Front. Behav. Neurosci. 10, 77.

Wehner, R. (2003). Desert ant navigation: how miniature brains solve complextasks. J. Comp. Physiol. A 189, 579-588.

Wehner, R. and Muller, M. (2006). The significance of direct sunlight and polarizedskylight in the ant’s celestial system of navigation. Proc. Natl. Acad. Sci. USA 103,12575-12579.

Wehner, R. and Srinivasan, M. V. (1981). Searching behaviour of desert ants,genus Cataglyphis (Formicidae, Hymenoptera). J. Comp. Physiol. A Neuroethol.Sens. Neural. Behav. Physiol. 142, 315-338.

Wehner, R. and Srinivasan, M. V. (2003). Path integration in insects. In TheNeurobiology of Spatial Behaviour (ed. K. K. Jeffrey), pp. 9-30. Oxford UniversityPress.

Wehner, R., Michel, B. and Antonsen, P. (1996). Visual navigation ininsects: coupling of egocentric and geocentric information. J. Exp. Biol. 199,129-140.

Wei, H., el Jundi, B., Homberg, U. and Stengl, M. (2010). Implementation ofpigment-dispersing factor-immunoreactive neurons in a standardized atlas ofthe brain of the cockroach Leucophaea maderae. J. Comp. Neurol. 518,4113-4133.

Weir, P. T. and Dickinson, M. H. (2015). Functional divisions for visual processing inthe central brain of flyingDrosophila. Proc. Natl. Acad. Sci. USA 112, E5523-E5532.

Weir, P. T., Schnell, B. and Dickinson, M. H. (2014). Central complex neuronsexhibit behaviourally gated responses to visual motion in Drosophila.J. Neurophysiol. 111, 62-71.

Williams, L. (1975). Anatomical studies of the insect central nervous system: aground-plan of the midbrain and an introduction to the central complex in thelocust, Schistocerca gregaria (Orthoptera). J. Zool. 176, 67-86.

Wittlinger,M.,Wehner, R. andWolf, H. (2006). The ant odometer: stepping on stiltsand stumps. Science 312, 1965-1967.

Wolff, T. and Rubin, G. M. (2018). Neuroarchitecture of the Drosophila centralcomplex: a catalog of nodulus and asymmetrical body neurons and a revision ofthe protocerebral bridge catalog. J. Comp. Neurol. 526, 2585-2611.

Wolff, T., Iyer, N. A. and Rubin, G. M. (2015). Neuroarchitecture and neuroanatomyof the Drosophila central complex: a GAL4-based dissection of protocerebralbridge neurons and circuits. J. Comp. Neurol. 523, 997-1037.

Wystrach, A., Mangan, M. and Webb, B. (2015). Optimal cue integration in ants.Proc. Biol. Sci. 282, 20151484.

Young, J. M. and Armstrong, J. D. (2010). Structure of the adult central complex inDrosophila: organization of distinct neuronal subsets. J. Comp. Neurol. 518,1500-1524.

Zeil, J. (2012). Visual homing: an insect perspective. Curr. Opin. Neurobiol. 22,285-293.

Zeil, J., Hofmann, M. I. and Chahl, J. S. (2003). Catchment areas of panoramicsnapshots in outdoor scenes. J. Opt. Soc. Am. A 20, 450-469.

Zeil, J., Narendra, A. and Sturzl, W. (2014). Looking and homing: how displacedants decide where to go. Philos. Trans. R. Soc. Lond. B 369, 20130034.

Zeller, M., Held, M., Bender, J., Berz, A., Heinloth, T., Hellfritz, T. and Pfeiffer, K.(2015). Transmedulla neurons in the sky compass network of the honeybee(Apis mellifera) are a possible site of circadian input. PLoS ONE 10,e0143244-e25.

15

REVIEW Journal of Experimental Biology (2019) 222, jeb188854. doi:10.1242/jeb.188854

Journal

ofEx

perim

entalB

iology