Light-dependent cues from the surrounding world are perceived by specialized photoreceptor neurons (PRs) in the eye. Insect compound eyes are elaborate systems capable of mediating flight in a rapidly changing 3D environment. In contrast, larval stages present much simpler visual organs, which combined with their tractability, make them great models to link neural circuit processing and behavior (Kane et al., 2013; Randel et al., 2014; 2015; Gepner et al., 2015). Larvae of the fruit fly Drosophila melanogaster employ their visual system for a range of diverse behaviors including navigation, entrainment of circadian rhythms, formation of associative memories and may respond to the presence of other larvae (Kane et al., 2013; Humberg and Sprecher, 2017; Slepian et al., 2015; Justice et al., 2012; Yamanaka et al., 2013; von Essen et al., 2011; Gong, 2009; Mazzoni et al., 2005; Gerber et al., 2004; Sawin-McCormack et al., 1995). The simple eyes of the larva (also termed Bolwig Organ, BO) consist of only about 12 PRs each and yet drive a wide range of behaviors, raising questions on the organizational logic of the underlying visual circuit. Spectral sensitivity of PRs is defined by the Rhodopsin gene they express. Larval eyes contain two PR-types, either expressing the blue-tuned Rhodopsin5 (Rh5) or the green-tuned Rhodopsin6 (Rh6) (Malpel et al., 2002; Hassan et al., 2005; Rodriguez Moncalvo and Campos, 2005; Sprecher et al., 2007). Interestingly, for rapid navigation away from light exposure only the Rh5-PRs seem essential, whereas to entrain the molecular clock either PR subtype suffices (Keene et al., 2011). In the past, several neurons of the larval visual neural circuit have been identified but the logic of circuit wiring as well as the precise numbers of neurons involved in its first order visual processing center remain unknown.

Larval PRs project their axons in a joint nerve (Bolwig nerve) terminating in a small neuropil domain termed the larval optic neuropil (LON). Previous studies identified eleven neurons innervating the LON in each brain hemisphere. This includes four lateral neurons (LaNs) expressing the pigment dispersing factor (Pdf) neuropeptide (Pdf-LaNs) and a fifth non-Pdf-expressing LaN (5th-LaN), all being part of the clock circuit (Kaneko et al., 1997), as well as a serotonergic neuron and three optic lobe pioneer cells (OLPs) (Helfrich-Förster, 1997; Rodriguez Moncalvo and Campos, 2005; 2009; Tix et al., 1989). Recently, two unpaired median octopaminergic/tyraminergic neurons were described to extend neurites into these neuropils (Selcho et al., 2014). It remained unknown, however, whether these previously identified neurons constitute the entire neuronal components of the LON and how visual neuronal components connect to each other to form a functional network. In a recent study we started to investigate the anatomy of the LON using serial-section transmission electron microscopy (ssTEM) showing that PRs’ axons form large globular boutons with polyadic synapses and that the OLPs were parts of their direct targets (Sprecher et al., 2011).

Here, we mapped the synaptic wiring diagram of the LON by reconstructing all its innervating neurons from a new ssTEM volume of a whole first instar larval central nervous system (Ohyama et al., 2015). We characterized and quantified the connectivity of all previously described LON-associated neurons and identified new components in this circuit. We found per hemisphere eleven second order interneurons, two third-order interneurons and one serotonergic neuron innervating the LON, plus two unpaired octopaminergic/tyraminergic neurons contacting both hemispheres. We highlighted the separation of light signal flow at the first synapse level as the two PR subtypes connect onto distinct subsets of interneurons. Network analysis suggests that the resulting circuits may encode both ambient light intensity information and information about the changes of light intensity. Comparison with the visual circuit of the adult fruit fly highlights common principles in organization of visual information processing, for example the stratification of PRs inputs as well as the existence of distinct photoreceptor pathways that are involved in detecting temporal light cues. Furthermore, the comparison with the olfactory wiring diagram (Berck et al., 2016) highlights common strategies for early sensory information processing, relay to higher order areas such as the mushroom bodies for associative memory, and control from the central brain. By mapping the connectivity of visual circuits and analyzing its architecture we have the opportunity to study the circuit structure-function relationship and advance our understanding of how neural circuits govern behavior.

A shared characteristic of many visual systems is the retinotopic organization allowing visual processing in a spatially segregated fashion by the transformation of the surrounding environment into a 2D virtual map (for review, Sanes and Zipursky, 2010). Within the compound eye of the adult fruit fly, this is achieved by the prominent organization of PRs in ommatidia in the retina that is maintained through underlying cartridges in the lamina and columns in the medulla. The Drosophila larval eye lacks ommatidia or a similar spatial organization of PRs. Nevertheless larvae can navigate directional light gradients and form light associative memories using their simple eyes (Kane et al., 2013; von Essen et al., 2011; Humberg and Sprecher, 2017).

In this study, we described the synapse-level connectome of the larval first visual center by reconstructing neurons recursively from the optic nerves to third-order neurons following all chemical synapses in a nanometer-resolution EM volume of the whole central nervous system. We found that the two PR subtypes synapse onto distinct target interneurons, showing a clear separation of visual information flow at the first synapse level. Rh5-PRs predominantly synapse onto VPNs, which transfer light information to distinct regions including the lateral horn and the mushroom body calyx, whereas Rh6-PRs strongly synapse onto two VLNs (cha- and glu-lOLPs). Moreover, the flow of information is convergent as these two main VLNs in turn synapse onto most VPNs. These two main VLNs also receive input from both the serotonergic as well as the octopaminergic/tyraminergic systems, suggesting that their activity may be modulated by input from central brain circuitry. Thus, a key feature of the larval visual circuit is that the Rh6-PRs-pathway feeds into the Rh5-PRs-pathway, suggesting a tuning function for Rh6-PRs and the two VLNs that also integrate external modulatory inputs (Figure 2F; Figure 3—figure supplement 2). This is not excluding the possibility of electrical connections mediated by gap junctions that are not visible in the ssTEM volume.

Hypothesized functional interpretation of circuit architecture

The Rh5-PRs-VPNs pathway may provide information about the ambient light intensity

Larval PRs responses to light remain currently unknown. Moreover, compared to adult flies PRs, larval PRs lack a clear rhabdomeric structure, do not present intracellular pigment granules and the Bolwig organ is not equipped with focusing optics or other accessory cells (Green et al., 1993; Sprecher et al., 2007). Nevertheless, larval PRs express the main actors of the phototransduction pathway like adult PRs (Rosenbaum et al., 2011; Mishra et al., 2016; Bernardo-Garcia et al., 2016). It therefore remains difficult to predict how larval PRs response to light. Larval PRs might either adapt and detect the mean light intensity, like adult PRs (for review Clark and Demb, 2016), or might detect the absolute light intensity. In either case, one can propose that larval PRs provide information about the ambient light intensity of the larval environment.

In other sensory systems such as the olfactory system of the adult (Rybak et al., 2016) and larval Drosophila (Berck et al., 2016) and in the chordotonal mechanosensory systems of the locust (Yuan et al., 2011) and Drosophila larva (Ohyama et al., 2015; Jovanic et al., 2016) axonal terminals of sensory neurons receive abundant inhibitory inputs from central neurons. Such presynaptic inhibition of sensory terminals may be employed to mediate lateral inhibition (Wilson and Laurent, 2005; Olsen and Wilson, 2008), to implement divisive normalization (Olsen et al., 2010) or to encode a predicted future stimulation (Wolf and Burrows, 1995). However, in the larval visual circuit, we do not observe significant synaptic connections onto the PRs (Figures 2E and 5B), suggesting that the light information encoded by PRs is passed on with no alteration at the level of the first synapse.

We therefore propose that larval Rh5-PRs may provide information about the ambient light intensity directly onto the larval visual system output neurons, the VPNs.

The Rh6-PRs-VLNs pathway may compute variations in light intensity

Compared to Rh5-PRs that directly connect the VPNs, the Rh6-PRs inputs are relayed to the VPNs only via the two VLNs, the cha- and glu-lOLPs. In dissociated larval Pdf-LaNs, it was shown that applying acetylcholine increased their calcium response whereas applying glutamate reduced their calcium response (Wegener et al., 2004; Hamasaka et al., 2007), suggesting that acetylcholine is an excitatory neurotransmitter for these cells whereas glutamate would be inhibitory. Acetylcholine can act as an inhibitory neurotransmitter through a metabotropic muscarinic receptor (Shinomiya et al., 2014; Ren et al., 2015), however, it more often acts as an excitatory neurotransmitter in Drosophila (Baines and Bate, 1998; Burrows, 1996). If both cha and glu-lOLPs were to be excitatory, the positive feedback loop between these two cells would likely result in an enhanced signal propagation of Rh6-PRs inputs towards VPNs. Such a positive feedforward feature downstream of the two PR-subtypes could make sense if they detected qualitatively distinct visual features such as spectral cues or intensity. If for instance Rh6-PRs responded to a vastly distinct light-spectrum than Rh5-PRs or if Rh6-PRs were low-light sensitive, this positive feedforward loop could result in spectral integration or amplification of low-light input. However, absorption spectra of both larval PRs are largely overlapping and there is currently no evidence for distinct detection capacities of both PRs (Salcedo et al., 1999; Humberg and Sprecher, 2017). Therefore, in our view a more favorable scenario is that outputs from cha-lOLP are likely excitatory while the outputs from glu-lOLP are likely inhibitory, similarly as glutamate has been found to mediate inhibition in the adult fly lobula plate (Mauss et al., 2014) and in the antennal lobe (Liu and Wilson, 2013; Berck et al., 2016). Therefore, in Figures 2F and 3 one could interpret cholinergic arrows (black) as excitatory and glutamatergic arrows (red) as inhibitory (also see Figure 3—figure supplement 2).

If we consider cha-lOLP as excitatory and glu-lOLP as inhibitory, then Rh6-PRs signals appears to be positively transferred to VPNs only via the cha-lOLP (Figure 3A). In addition, cha-lOLP then receives strong inputs from the inhibitory glu-lOLP (Figures 2E and 3A), which is also almost exclusively driven by Rh6-PRs inputs. Therefore, this inhibition of glu-lOLP onto cha-lOLP could mediate a form of indirect presynaptic inhibition of the Rh6-PRs inputs. This motif made by the cholinergic Rh6-PRs driving both the cha-lOLP and the glu-lOLP, and with the glu-lOLP inhibiting the cha-lOLP, creates then an incoherent feedforward loop (Alon, 2007). Therefore this motif could make the cha-lOLP responsive to the derivative of Rh6-PRs activity, that is to the variations in light intensity. As cha-lOLP VLN is likely to be a positive relay of Rh6-PRs inputs onto VPNs, VPNs could therefore respond to increments in light intensity (ON response).

Moreover, cha-lOLP inputs onto the glu-lOLP and both cha- and glu-lOLPs further connect onto the VPNs. Considering cha-lOLP as excitatory and glu-lOLP as inhibitory, this creates a second incoherent feedforward motif making VPNs potentially responsive to the derivative of cha-lOLP inputs. As cha-lOLP inputs may already represent the first derivative of Rh6-PRs inputs, this second incoherent feedforward motif may therefore make VPNs responsive to the acceleration of light intensity raises.

In summary, glu-lOLP may provide both presynaptic inhibition of Rh6-PRs inputs by inhibiting the relay neuron cha-lOLP, and postsynaptic inhibition by directly inhibiting most VPNs. Consequently, VPNs could respond to the ambient light intensity (from Rh5-PRs) and to the variations of light intensity (from the Rh6-PRs-VLNs pathway). Interestingly, olfactory projection interneurons in the adult Drosophila also respond to a mixture of odorant concentration and of the acceleration of odorant concentration (Kim et al., 2015). Comparable to other sensory system (Klein et al., 2015; Schulze et al., 2015), a measure of changes in light intensity over time would enable light gradient navigation (Kane et al., 2013; Humberg and Sprecher, 2017).

Phasic inhibition could sharpen ON and OFF responses

In the wiring diagram, we found a glutamatergic (therefore putatively inhibitory) third-order interneuron, named VPLN and mainly driven by cha- and glu-OLPs. The timing of cha- and glu-lOLPs input may be critical for the VPLN function. If cha-lOLP input precedes glu-lOLP input, the VPLN may potentially be activated only upon an increase in light intensity via Rh6-PRs (Figure 3D). This is unlike the other glutamatergic interneuron, glu-lOLP, which receives direct inputs from PRs and therefore can be tonically active in the presence of light. The VPLN specifically synapses onto multiple VPNs (the 5th-LaN, the two nc-LaNs and PVL09) and therefore potentially inhibits these cells that also receive strong inputs from glu-lOLP (Figure 3C,E, Figure 3—figure supplement 1). In consequence, these VPNs may be subject to tonic inhibition (from glu-lOLP) under constant light conditions and to phasic inhibition (from the VPLN) upon an increase in Rh6-PRs dependent light intensity. Therefore, phasic inhibition from the VPLN may potentially refine the temporal resolution of VPNs responses to increment of light intensity (ON response).

An interesting aspect resulting from tonic inhibition of VPNs during constant light stimulation is what happens when the light intensity decreases (OFF response). Some neurons subject to tonic inhibition can have a rebound of activity when inhibition is lifted (Marder and Bucher, 2001; Hedwig, 2016). If, via this mechanism, VPNs were to increase their activity upon a decrease in light intensity, they would be encoding an OFF response. Potentially all VPNs under tonic inhibition from glu-lOLP could have this rebound of activity after a light intensity decrease. Interestingly, two cholinergic VPNs (the nc-LaN 1 and 2) synapse onto the VPLN that could then become more strongly activated by the OFF response (Figure 3D, Figure 2—source data 1). As the VPLN is inhibiting several VPNs (the 5th-LaN, the two nc-LaNs and PVL09), this could therefore create a second period of phasic inhibition allowing to maintain these VPNs OFF responses brief.

In conclusion, the connectivity of the third-order glutamatergic VPLN putatively inhibitory suggests that it may refine the temporal resolution of VPNs ON and OFF responses through phasic inhibition.

The visual circuit supports previous behavioral observations

In addition, this complete wiring diagram of the larval visual system can explain why in previous studies only Rh5-PRs appeared to be required for light avoidance while Rh6-PRs seemed dispensable in most experimental conditions (Keene et al., 2011; Kane et al., 2013; Humberg and Sprecher, 2017). Moreover, Rh6-PRs mutant larvae were shown to avoid light wavelengths from ultra-violet to green, supporting the idea that Rh5-PRs respond to a wider range of wavelength of light than only ‘blue’ light (Humberg and Sprecher, 2017; Salcedo et al., 2009). Taken together, our current data and the lack of evidence of color vision capability in larvae, we propose that the two larval PRs are rather specialized in detection of ambient light intensity and of temporal variations of light intensity.

Based on our connectomic analysis, when Rh6-PRs are mutated or functionally silenced, Rh5-PRs may still provide light ambient intensity information, and the cholinergic inputs from nc-LaNs onto the glutamatergic VPLN could inhibit the VPNs, computing on its own some response to changes in light intensity. Blocking the VPLN activity in a Rh6-PRs depletion background would allow to study whether ambient light information is sufficient for visual navigation. Moreover, when Rh5-PRs are disabled, VPNs do not receive ambient light information while potentially being under tonic inhibition from the glu-lOLP, which therefore could shut down their activity. Given that in Rh5-PRs depletion background, larval visual responses are highly defective, this further suggests that ON and OFF responses alone are not sufficient to allow larval light avoidance. We therefore propose that it may need both a baseline activity of VPNs, provided by ambient light intensity information, and the modulations of this baseline activity in response to light intensity variations.

Only the Pdf-LaNs involved in circadian rhythm receive direct inputs from both PRs subtypes and can therefore still receive ambient light information in either PRs’ depletion condition. This broad light information integration capacity is in agreement with evidence that both PRs subtypes are sufficient to entrain the larval clock (Keene et al., 2011). While further reconstructions of their postsynaptic partners are required, the four Pdf-LaNs appear identical in sensory inputs, local connections and anatomy, raising the question of what such redundancy would allow.

Comparisons between the larval eye and adult compound eye

The basic organization of the visual system in the larva, the adults fly as well as with the visual circuit of vertebrates show several similarities and possible shared characteristics (Figure 6). Several possible interpretations of the photoreceptor types as well as circuit motifs may be derived. One possible scenario lies in the comparison between PR types found in the different eye types and their known or putative roles in temporal coding as discussed here.

Figure 6

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First, these three visual systems have each two main types of photo-sensory neurons: Rh5- and Rh6-PRs for the Drosophila larva, inner and outer PRs for adult flies, cones and rods for vertebrates (Sprecher et al., 2007; Sprecher and Desplan, 2008; Friedrich, 2008; Sanes and Zipursky, 2010). Here we also confirmed that the LON was organized in layers (LONd, LONi, LONp) similarly as the adult flies optic lobe (lamina, medulla) and the vertebrate retina (outer and inner plexiform layers) (Sanes and Zipursky, 2010, Figure 6). Moreover, the mode of development of Drosophila larval and adult eyes reinforces similarities between Rh5-PRs with an inner-PR type and Rh6-PRs with an outer-PR type: in the adult, R8-PR precursors are formed first to recruit outer PRs, likewise in the larva, Rh5-PR precursors develop first and then recruit the Rh6-PRs, in both cases via the EGFR pathway (Sprecher et al., 2007). Also, development of both inner PRs and Rh5-PRs depend on the transcription factors senseless and spalt (Sprecher et al., 2007; Sprecher and Desplan, 2008; Mishra et al., 2013). Finally, the inputs from serotonergic and octopaminergic/tyraminergic neurons onto the LON are also a shared feature with the adult visual system where serotonin has been linked to circadian rhythmicity (Yuan et al., 2005) and where visual cues during flight are modulated by octopaminergic inputs (Suver et al., 2012; Wasserman et al., 2015). In the LON, our data suggest that the serotonergic neuron SP2-1 and octopaminergic/tyraminergic neurons sVUM might modulate the sensitivity to light increment and decrement (ON/OFF) by directly connecting the cha- and glu-lOLPs VLNs. These ON and OFF responses might be further affected by inputs from SP2-1 onto the VPLN.

A potential functional comparison between the larval and the adult visual system emerge from the similar neurotransmitters expressed and the functions proposed for the larval VLNs cha- and glu-lOLPs and the adult glutamatergic L1 and cholinergic L2 interneurons of the lamina (Figure 6A,B; Gao et al., 2008). In the adult fly, the outer R1-6 PRs connect to L1 and L2 interneurons that convey distinct responses to light increment (ON) and decrement (OFF) (Joesch et al., 2010; Clark et al., 2011; Strother et al., 2014; Behnia et al., 2014). The adult PRs are histaminergic and induce hyperpolarization (inhibition) in both L1 and L2 upon light increment, followed by a rebound of activity of these two cells upon light decrement (Hardie, 1987; Reiff et al., 2010; Clark et al., 2011); reviewed in Borst and Helmstaedter, 2015). Therefore upon light increment, PRs inhibit the glutamatergic L1, which results in the disinhibition of the downstream targets of L1 (ON response). In turn, upon light decrement PRs inhibit the cholinergic L2 less, which results in the activation of the downstream targets of L2 (OFF response). However, the larval PRs are cholinergic (Yasuyama et al., 1995; Keene et al., 2011) and, at least for the Pdf-LaNs (Yuan et al., 2011), excite their targets activity in response of light. Therefore in larvae, an ON response could result from an increase of excitation from cha-lOLP onto VPNs when light intensity increases (via the increase of Rh6-PRs inputs) and could be kept transient by inhibition from the glu-lOLP (indirect presynaptic inhibition of Rh6-PRs inputs) and by phasic inhibition from the VPLN. In turn, an OFF response could result from the disinhibition of VPNs from glu-lOLP inhibition when light intensity decreases and may also be kept transient by phasic inhibition from the VPLN. Therefore, whereas the glutamatergic L1 conveys the ON response and the cholinergic L2 conveys the OFF response in adult flies, we propose that the glu-lOLP could convey the OFF response and the cha-lOLP could convey the ON response in larvae (Figure 6A,B). Another similarity between the two lOLPs and the adult L1 and L2 is that each lOLP receives an equivalent number of inputs from each Rh6-PRs (about 20 synapses in average, Figure 2—source data 1), similarly as adult L1 and L2 receives about 50 inputs from each R1-6 PRs (Meinertzhagen, 1989), suggesting that the information transfer could have a similarly high fidelity. However, while ON/OFF responses in adult flies and vertebrates (Figure 6B,C) are involved in motion detection, such ability within one eye is achieved through downstream direction-sensitive cells that integrate information from several points in space (Clark and Demb, 2016). As larval eyes lack ommatidia such capacity seems unlikely, however the ON/OFF detection could already suffice for larval visual navigation (Kane et al., 2013; Klein et al., 2015; Schulze et al., 2015; Humberg and Sprecher, 2017). Another important difference between the larval and adult systems is that in the LON the cha- and glu-lOLPs are strongly interconnected and both input onto the same targets, whereas in the adult fly, the L1 and L2 are not chemically connected (only electrically, Joesch et al., 2010) and the ON/OFF pathways are separated for two additional synaptic levels before converging on direction-sensitive cells in the lobula plate (Takemura et al., 2013; Clark and Demb, 2016). Consequently, whereas the adult L1 and L2 have similar responses to light (Clark et al., 2011; Borst and Helmstaedter, 2015), the reciprocal connections between cha- and glu-lOLPs may uniquely shape the light response of these two cells.

If the Rh6-PRs-VLNs pathway contributes indeed to a function related to light increment or decrement remains hypothetical. Moreover, since Rh5-PRs survive metamorphosis giving rise to the adult Hofbauer-Buchner eyelet, it is possible that the larval visual circuit more closely resembles the eyelet circuitry than the one of the adult compound eye (Sprecher and Desplan, 2008).

Comparison between the visual and the olfactory first-order processing centers

The LON neural network presents many similarities with the larval olfactory wiring diagram (Berck et al., 2016), favoring a potential common organizational origin of these sensory neuropils as suggested before (Strausfeld, 1989; Strausfeld et al., 2007). Odorant cues are perceived by olfactory receptor neurons that project to the antennal lobe where they contact olfactory projection interneurons and olfactory local interneurons. Similarly to visual circuits, sensory inputs in the antennal lobe are segregated in olfactory receptor specific domains termed glomeruli (Fishilevich et al., 2005; Masuda-Nakagawa et al., 2009). Most olfactory receptor neurons and olfactory projection interneurons are cholinergic like PRs and VPNs (Keene et al., 2011; Yasuyama and Salvaterra, 1999; Python and Stocker, 2002), and we find glutamatergic, potentially inhibitory, local interneurons in both systems (Berck et al., 2016). Reciprocal synapses between cha-lOLP and glu-lOLP in the visual circuit may be functionally equivalent to the connections between some olfactory receptor neurons and the glutamatergic Picky olfactory local interneuron 0 of the antennal lobe, suggesting that this reciprocal motif in the LON could indeed contribute to detecting changes in light intensity by computing the first derivative of the stimulus (Berck et al., 2016). Additionally, we can observe presynaptic and postsynaptic inhibition in both sensory systems while the strategies of implementation are somewhat different. Interestingly, we observed that most projection interneurons of both sensory systems may bring a mixture of stimulus intensity and acceleration in stimulus intensity to the lateral horn and to the mushroom body calyx (Figure 1—figure supplement 3; Kim et al., 2015). Finally, both sensory systems are modulated by aminergic neurons inputs on specifics local interneurons. Interestingly, both the lOLPs VLNs and the olfactory Broad local interneurons Trio form feedback synapses onto the axonal termini of their respective bilateral octopaminergic/tyraminerigc neurons while this is not the case for serotonergic neurons (Berck et al., 2016).

Concluding remarks

Identification of synaptic connectivity and neurotransmitter identity within the larval visual circuit allows us to formulate clear predictions on the response profile and function of individual network components. Based on the circuit map we suggest that the Rh6-PRs-VLNs pathway might be required for the detection of light intensity changes, whereas the Rh5-PRs-VPNs pathway could provide direct ambient light intensity information. In the future, behavioral studies or physiological activity recording of visual circuit neurons will allow to add additional layers onto this functional map.

1. 1. Alon U

   (2007) Network motifs: theory and experimental approaches

   Nature Reviews Genetics 8:450–461.

   https://doi.org/10.1038/nrg2102

   • PubMed
   • Google Scholar
2. 1. Baines RA
   2. Bate M

   (1998)

   Electrophysiological development of central neurons in the Drosophila embryo

   Journal of Neuroscience 18:4673–4683.

   • PubMed
   • Google Scholar
3. 1. Behnia R
   2. Clark DA
   3. Carter AG
   4. Clandinin TR
   5. Desplan C

   (2014) Processing properties of ON and OFF pathways for Drosophila motion detection

   Nature 512:427–430.

   https://doi.org/10.1038/nature13427

   • PubMed
   • Google Scholar
4. 1. Berck ME
   2. Khandelwal A
   3. Claus L
   4. Hernandez-Nunez L
   5. Si G
   6. Tabone CJ
   7. Li F
   8. Truman JW
   9. Fetter RD
   10. Louis M
   11. Samuel AD
   12. Cardona A

   (2016) The wiring diagram of a glomerular olfactory system

   eLife 5:e14859.

   https://doi.org/10.7554/eLife.14859

   • PubMed
   • Google Scholar
5. 1. Bernardo-Garcia FJ
   2. Fritsch C
   3. Sprecher SG

   (2016) The transcription factor Glass links eye field specification with photoreceptor differentiation in Drosophila

   Development 143:1413–1423.

   https://doi.org/10.1242/dev.128801

   • PubMed
   • Google Scholar
6. 1. Borst A
   2. Helmstaedter M

   (2015) Common circuit design in fly and mammalian motion vision

   Nature Neuroscience 18:1067–1076.

   https://doi.org/10.1038/nn.4050

   • PubMed
   • Google Scholar
7. Book

   1. Burrows M

   (1996)

   The Neurobiology of an Insect Brain

   Oxford University Press.

   • Google Scholar
8. 1. Clark DA
   2. Bursztyn L
   3. Horowitz MA
   4. Schnitzer MJ
   5. Clandinin TR

   (2011) Defining the computational structure of the motion detector in Drosophila

   Neuron 70:1165–1177.

   https://doi.org/10.1016/j.neuron.2011.05.023

   • PubMed
   • Google Scholar
9. 1. Clark DA
   2. Demb JB

   (2016) Parallel Computations in Insect and mammalian visual motion processing

   Current Biology 26:R1062–R1072.

   https://doi.org/10.1016/j.cub.2016.08.003

   • PubMed
   • Google Scholar
10. 1. Dacks AM
    2. Green DS
    3. Root CM
    4. Nighorn AJ
    5. Wang JW

    (2009) Serotonin modulates olfactory processing in the antennal lobe of Drosophila

    Journal of Neurogenetics 23:366–377.

    https://doi.org/10.3109/01677060903085722

    • PubMed
    • Google Scholar
11. 1. Daniels RW
    2. Gelfand MV
    3. Collins CA
    4. DiAntonio A

    (2008) Visualizing glutamatergic cell bodies and synapses in Drosophila larval and adult CNS

    The Journal of Comparative Neurology 508:131–152.

    https://doi.org/10.1002/cne.21670

    • PubMed
    • Google Scholar
12. 1. Delgado R
    2. Barla R
    3. Latorre R
    4. Labarca P

    (1989) L-glutamate activates excitatory and inhibitory channels in Drosophila larval muscle

    FEBS Letters 243:337–342.

    https://doi.org/10.1016/0014-5793(89)80157-7

    • PubMed
    • Google Scholar
13. 1. Eichler K
    2. Li F
    3. Litwin-Kumar A
    4. Park Y
    5. Andrade I
    6. Schneider-Mizell CM
    7. Saumweber T
    8. Huser A
    9. Eschbach C
    10. Gerber B
    11. Fetter RD
    12. Truman JW
    13. Priebe CE
    14. Abbott LF
    15. Thum AS
    16. Zlatic M
    17. Cardona A

    (2017) The complete connectome of a learning and memory centre in an insect brain

    Nature 548:175–182.

    https://doi.org/10.1038/nature23455

    • PubMed
    • Google Scholar
14. 1. Fishilevich E
    2. Domingos AI
    3. Asahina K
    4. Naef F
    5. Vosshall LB
    6. Louis M

    (2005) Chemotaxis behavior mediated by single larval olfactory neurons in Drosophila

    Current Biology 15:2086–2096.

    https://doi.org/10.1016/j.cub.2005.11.016

    • PubMed
    • Google Scholar
15. 1. Frenkel L
    2. Muraro NI
    3. Beltrán González AN
    4. Marcora MS
    5. Bernabó G
    6. Hermann-Luibl C
    7. Romero JI
    8. Helfrich-Förster C
    9. Castaño EM
    10. Marino-Busjle C
    11. Calvo DJ
    12. Ceriani MF

    (2017) Organization of Circadian behavior relies on glycinergic transmission

    Cell reports 19:72–85.

    https://doi.org/10.1016/j.celrep.2017.03.034

    • PubMed
    • Google Scholar
16. 1. Friedrich M

    (2008) Opsins and cell fate in the Drosophila bolwig organ: tricky lessons in homology inference

    BioEssays 30:980–993.

    https://doi.org/10.1002/bies.20803

    • PubMed
    • Google Scholar
17. 1. Gao S
    2. Takemura SY
    3. Ting CY
    4. Huang S
    5. Lu Z
    6. Luan H
    7. Rister J
    8. Thum AS
    9. Yang M
    10. Hong ST
    11. Wang JW
    12. Odenwald WF
    13. White BH
    14. Meinertzhagen IA
    15. Lee CH

    (2008) The neural substrate of spectral preference in Drosophila

    Neuron 60:328–342.

    https://doi.org/10.1016/j.neuron.2008.08.010

    • PubMed
    • Google Scholar
18. 1. Gentile C
    2. Sehadova H
    3. Simoni A
    4. Chen C
    5. Stanewsky R

    (2013) Cryptochrome antagonizes synchronization of Drosophila's circadian clock to temperature cycles

    Current Biology 23:185–195.

    https://doi.org/10.1016/j.cub.2012.12.023

    • PubMed
    • Google Scholar
19. 1. Gepner R
    2. Mihovilovic Skanata M
    3. Bernat NM
    4. Kaplow M
    5. Gershow M

    (2015) Computations underlying Drosophila photo-taxis, odor-taxis, and multi-sensory integration

    eLife 4:06229.

    https://doi.org/10.7554/eLife.06229

    • Google Scholar
20. 1. Gerber B
    2. Scherer S
    3. Neuser K
    4. Michels B
    5. Hendel T
    6. Stocker RF
    7. Heisenberg M

    (2004) Visual learning in individually assayed Drosophila larvae

    Journal of Experimental Biology 207:179–188.

    https://doi.org/10.1242/jeb.00718

    • PubMed
    • Google Scholar
21. 1. Gong Z

    (2009) Behavioral dissection of Drosophila larval phototaxis

    Biochemical and Biophysical Research Communications 382:395–399.

    https://doi.org/10.1016/j.bbrc.2009.03.033

    • PubMed
    • Google Scholar
22. 1. Green P
    2. Hartenstein AY
    3. Hartenstein V

    (1993) The embryonic development of the Drosophila visual system

    Cell & Tissue Research 273:583–598.

    https://doi.org/10.1007/BF00333712

    • PubMed
    • Google Scholar
23. 1. Hamasaka Y
    2. Rieger D
    3. Parmentier ML
    4. Grau Y
    5. Helfrich-Förster C
    6. Nässel DR

    (2007) Glutamate and its metabotropic receptor in Drosophila clock neuron circuits

    The Journal of Comparative Neurology 505:32–45.

    https://doi.org/10.1002/cne.21471

    • PubMed
    • Google Scholar
24. 1. Hardie RC

    (1987) Is histamine a neurotransmitter in insect photoreceptors?

    Journal of Comparative Physiology A 161:201–213.

    https://doi.org/10.1007/BF00615241

    • PubMed
    • Google Scholar
25. 1. Hassan J
    2. Iyengar B
    3. Scantlebury N
    4. Rodriguez Moncalvo V
    5. Campos AR

    (2005) Photic input pathways that mediate the Drosophila larval response to light and circadian rhythmicity are developmentally related but functionally distinct

    The Journal of Comparative Neurology 481:266–275.

    https://doi.org/10.1002/cne.20383

    • PubMed
    • Google Scholar
26. 1. Hedwig BG

    (2016) Sequential filtering processes shape feature detection in crickets: a framework for song pattern recognition

    Frontiers in Physiology 7:46.

    https://doi.org/10.3389/fphys.2016.00046

    • PubMed
    • Google Scholar
27. 1. Helfrich-Förster C

    (1997) Development of pigment-dispersing hormone-immunoreactive neurons in the nervous system of Drosophila melanogaster

    The Journal of Comparative Neurology 380:335–354.

    https://doi.org/10.1002/(SICI)1096-9861(19970414)380:3<335::AID-CNE4>3.0.CO;2-3

    • PubMed
    • Google Scholar
28. 1. Humberg TH
    2. Sprecher SG

    (2017) Age- and wavelength-dependency of drosophila larval phototaxis and behavioral responses to natural lighting conditions

    Frontiers in Behavioral Neuroscience 11:66.

    https://doi.org/10.3389/fnbeh.2017.00066

    • PubMed
    • Google Scholar
29. 1. Huser A
    2. Rohwedder A
    3. Apostolopoulou AA
    4. Widmann A
    5. Pfitzenmaier JE
    6. Maiolo EM
    7. Selcho M
    8. Pauls D
    9. von Essen A
    10. Gupta T
    11. Sprecher SG
    12. Birman S
    13. Riemensperger T
    14. Stocker RF
    15. Thum AS

    (2012) The serotonergic central nervous system of the Drosophila larva: anatomy and behavioral function

    PLoS One 7:e47518.

    https://doi.org/10.1371/journal.pone.0047518

    • PubMed
    • Google Scholar
30. 1. Joesch M
    2. Schnell B
    3. Raghu SV
    4. Reiff DF
    5. Borst A

    (2010) ON and OFF pathways in Drosophila motion vision

    Nature 468:300–304.

    https://doi.org/10.1038/nature09545

    • PubMed
    • Google Scholar
31. 1. Jovanic T
    2. Schneider-Mizell CM
    3. Shao M
    4. Masson JB
    5. Denisov G
    6. Fetter RD
    7. Mensh BD
    8. Truman JW
    9. Cardona A
    10. Zlatic M

    (2016) Competitive Disinhibition Mediates Behavioral Choice and Sequences in Drosophila

    Cell 167:858–870.

    https://doi.org/10.1016/j.cell.2016.09.009

    • PubMed
    • Google Scholar
32. 1. Justice ED
    2. Macedonia NJ
    3. Hamilton C
    4. Condron B

    (2012) The simple fly larval visual system can process complex images

    Nature Communications 3:1156.

    https://doi.org/10.1038/ncomms2174

    • PubMed
    • Google Scholar
33. 1. Kane EA
    2. Gershow M
    3. Afonso B
    4. Larderet I
    5. Klein M
    6. Carter AR
    7. de Bivort BL
    8. Sprecher SG
    9. Samuel AD

    (2013) Sensorimotor structure of Drosophila larva phototaxis

    PNAS 110:E3868–3877.

    https://doi.org/10.1073/pnas.1215295110

    • PubMed
    • Google Scholar
34. 1. Kaneko M
    2. Helfrich-Förster C
    3. Hall JC

    (1997)

    Spatial and temporal expression of the period and timeless genes in the developing nervous system of Drosophila: newly identified pacemaker candidates and novel features of clock gene product cycling

    Journal of Neuroscience 17:6745–6760.

    • PubMed
    • Google Scholar
35. 1. Keene AC
    2. Mazzoni EO
    3. Zhen J
    4. Younger MA
    5. Yamaguchi S
    6. Blau J
    7. Desplan C
    8. Sprecher SG

    (2011) Distinct visual pathways mediate Drosophila larval light avoidance and circadian clock entrainment

    Journal of Neuroscience 31:6527–6534.

    https://doi.org/10.1523/JNEUROSCI.6165-10.2011

    • PubMed
    • Google Scholar
36. 1. Kim AJ
    2. Lazar AA
    3. Slutskiy YB

    (2015) Projection neurons in Drosophila antennal lobes signal the acceleration of odor concentrations

    eLife 4:e06651.

    https://doi.org/10.7554/eLife.06651

    • Google Scholar
37. 1. Klein M
    2. Afonso B
    3. Vonner AJ
    4. Hernandez-Nunez L
    5. Berck M
    6. Tabone CJ
    7. Kane EA
    8. Pieribone VA
    9. Nitabach MN
    10. Cardona A
    11. Zlatic M
    12. Sprecher SG
    13. Gershow M
    14. Garrity PA
    15. Samuel AD

    (2015) Sensory determinants of behavioral dynamics in Drosophila thermotaxis

    PNAS 112:E220–229.

    https://doi.org/10.1073/pnas.1416212112

    • PubMed
    • Google Scholar
38. 1. Kreher SA
    2. Mathew D
    3. Kim J
    4. Carlson JR

    (2008) Translation of sensory input into behavioral output via an olfactory system

    Neuron 59:110–124.

    https://doi.org/10.1016/j.neuron.2008.06.010

    • PubMed
    • Google Scholar
39. 1. Linster C
    2. Smith BH

    (1997) A computational model of the response of honey bee antennal lobe circuitry to odor mixtures: overshadowing, blocking and unblocking can arise from lateral inhibition

    Behavioural Brain Research 87:1–14.

    https://doi.org/10.1016/S0166-4328(96)02271-1

    • PubMed
    • Google Scholar
40. 1. Liu WW
    2. Wilson RI

    (2013) Glutamate is an inhibitory neurotransmitter in the Drosophila olfactory system

    PNAS 110:10294–10299.

    https://doi.org/10.1073/pnas.1220560110

    • PubMed
    • Google Scholar
41. 1. Mahr A
    2. Aberle H

    (2006) The expression pattern of the Drosophila vesicular glutamate transporter: a marker protein for motoneurons and glutamatergic centers in the brain

    Gene Expression Patterns 6:299–309.

    https://doi.org/10.1016/j.modgep.2005.07.006

    • PubMed
    • Google Scholar
42. 1. Majeed ZR
    2. Abdeljaber E
    3. Soveland R
    4. Cornwell K
    5. Bankemper A
    6. Koch F
    7. Cooper RL

    (2016) Modulatory Action by the serotonergic system: behavior and neurophysiology in drosophila melanogaster

    Neural Plasticity 2016:7291438.

    https://doi.org/10.1155/2016/7291438

    • PubMed
    • Google Scholar
43. 1. Malpel S
    2. Klarsfeld A
    3. Rouyer F

    (2002)

    Larval optic nerve and adult extra-retinal photoreceptors sequentially associate with clock neurons during Drosophila brain development

    Development 129:1443–1453.

    • PubMed
    • Google Scholar
44. 1. Marder E
    2. Bucher D

    (2001) Central pattern generators and the control of rhythmic movements

    Current Biology 11:R986–R996.

    https://doi.org/10.1016/S0960-9822(01)00581-4

    • PubMed
    • Google Scholar
45. 1. Masuda-Nakagawa LM
    2. Gendre N
    3. O'Kane CJ
    4. Stocker RF

    (2009) Localized olfactory representation in mushroom bodies of Drosophila larvae

    PNAS 106:10314–10319.

    https://doi.org/10.1073/pnas.0900178106

    • PubMed
    • Google Scholar
46. 1. Mauss AS
    2. Meier M
    3. Serbe E
    4. Borst A

    (2014) Optogenetic and pharmacologic dissection of feedforward inhibition in Drosophila motion vision

    Journal of Neuroscience 34:2254–2263.

    https://doi.org/10.1523/JNEUROSCI.3938-13.2014

    • PubMed
    • Google Scholar
47. 1. Mayer B
    2. Emery G
    3. Berdnik D
    4. Wirtz-Peitz F
    5. Knoblich JA

    (2005) Quantitative analysis of protein dynamics during asymmetric cell division

    Current Biology 15:1847–1854.

    https://doi.org/10.1016/j.cub.2005.08.067

    • PubMed
    • Google Scholar
48. 1. Mazzoni EO
    2. Desplan C
    3. Blau J

    (2005) Circadian pacemaker neurons transmit and modulate visual information to control a rapid behavioral response

    Neuron 45:293–300.

    https://doi.org/10.1016/j.neuron.2004.12.038

    • PubMed
    • Google Scholar
49. 1. Meinertzhagen IA

    (1989) Fly photoreceptor synapses: their development, evolution, and plasticity

    Journal of Neurobiology 20:276–294.

    https://doi.org/10.1002/neu.480200503

    • PubMed
    • Google Scholar
50. 1. Mishra AK
    2. Bargmann BO
    3. Tsachaki M
    4. Fritsch C
    5. Sprecher SG

    (2016) Functional genomics identifies regulators of the phototransduction machinery in the Drosophila larval eye and adult ocelli

    Developmental Biology 410:164–177.

    https://doi.org/10.1016/j.ydbio.2015.12.026

    • PubMed
    • Google Scholar
51. 1. Mishra AK
    2. Tsachaki M
    3. Rister J
    4. Ng J
    5. Celik A
    6. Sprecher SG

    (2013) Binary cell fate decisions and fate transformation in the Drosophila larval eye

    PLoS Genetics 9:e1004027.

    https://doi.org/10.1371/journal.pgen.1004027

    • PubMed
    • Google Scholar
52. 1. Nagel KI
    2. Hong EJ
    3. Wilson RI

    (2015) Synaptic and circuit mechanisms promoting broadband transmission of olfactory stimulus dynamics

    Nature Neuroscience 18:56–65.

    https://doi.org/10.1038/nn.3895

    • PubMed
    • Google Scholar
53. 1. Ohyama T
    2. Schneider-Mizell CM
    3. Fetter RD
    4. Aleman JV
    5. Franconville R
    6. Rivera-Alba M
    7. Mensh BD
    8. Branson KM
    9. Simpson JH
    10. Truman JW
    11. Cardona A
    12. Zlatic M

    (2015) A multilevel multimodal circuit enhances action selection in Drosophila

    Nature 520:633–639.

    https://doi.org/10.1038/nature14297

    • PubMed
    • Google Scholar
54. 1. Olsen SR
    2. Bhandawat V
    3. Wilson RI

    (2010) Divisive normalization in olfactory population codes

    Neuron 66:287–299.

    https://doi.org/10.1016/j.neuron.2010.04.009

    • PubMed
    • Google Scholar
55. 1. Olsen SR
    2. Wilson RI

    (2008) Lateral presynaptic inhibition mediates gain control in an olfactory circuit

    Nature 452:956–960.

    https://doi.org/10.1038/nature06864

    • PubMed
    • Google Scholar
56. 1. Python F
    2. Stocker RF

    (2002) Immunoreactivity against choline acetyltransferase, gamma-aminobutyric acid, histamine, octopamine, and serotonin in the larval chemosensory system of Dosophila melanogaster

    The Journal of Comparative Neurology 453:157–167.

    https://doi.org/10.1002/cne.10383

    • PubMed
    • Google Scholar
57. 1. Randel N
    2. Asadulina A
    3. Bezares-Calderón LA
    4. Verasztó C
    5. Williams EA
    6. Conzelmann M
    7. Shahidi R
    8. Jékely G

    (2014) Neuronal connectome of a sensory-motor circuit for visual navigation

    eLife 3:e02730.

    https://doi.org/10.7554/eLife.02730

    • Google Scholar
58. 1. Randel N
    2. Shahidi R
    3. Verasztó C
    4. Bezares-Calderón LA
    5. Schmidt S
    6. Jékely G

    (2015) Inter-individual stereotypy of the Platynereis larval visual connectome

    eLife 4:e08069.

    https://doi.org/10.7554/eLife.08069

    • PubMed
    • Google Scholar
59. 1. Ready DF
    2. Hanson TE
    3. Benzer S

    (1976) Development of the Drosophila retina, a neurocrystalline lattice

    Developmental Biology 53:217–240.

    https://doi.org/10.1016/0012-1606(76)90225-6

    • PubMed
    • Google Scholar
60. 1. Reiff DF
    2. Plett J
    3. Mank M
    4. Griesbeck O
    5. Borst A

    (2010) Visualizing retinotopic half-wave rectified input to the motion detection circuitry of Drosophila

    Nature Neuroscience 13:973–978.

    https://doi.org/10.1038/nn.2595

    • PubMed
    • Google Scholar
61. 1. Ren GR
    2. Folke J
    3. Hauser F
    4. Li S
    5. Grimmelikhuijzen CJ

    (2015) The A- and B-type muscarinic acetylcholine receptors from Drosophila melanogaster couple to different second messenger pathways

    Biochemical and Biophysical Research Communications 462:358–364.

    https://doi.org/10.1016/j.bbrc.2015.04.141

    • PubMed
    • Google Scholar
62. 1. Rodriguez Moncalvo VG
    2. Campos AR

    (2005) Genetic dissection of trophic interactions in the larval optic neuropil of Drosophila melanogaster

    Developmental Biology 286:549–558.

    https://doi.org/10.1016/j.ydbio.2005.08.030

    • PubMed
    • Google Scholar
63. 1. Rodriguez Moncalvo VG
    2. Campos AR

    (2009) Role of serotonergic neurons in the Drosophila larval response to light

    BMC Neuroscience 10:66.

    https://doi.org/10.1186/1471-2202-10-66

    • PubMed
    • Google Scholar
64. 1. Rosenbaum EE
    2. Brehm KS
    3. Vasiljevic E
    4. Liu CH
    5. Hardie RC
    6. Colley NJ

    (2011) XPORT-dependent transport of TRP and rhodopsin

    Neuron 72:602–615.

    https://doi.org/10.1016/j.neuron.2011.09.016

    • PubMed
    • Google Scholar
65. 1. Roy B
    2. Singh AP
    3. Shetty C
    4. Chaudhary V
    5. North A
    6. Landgraf M
    7. Vijayraghavan K
    8. Rodrigues V

    (2007) Metamorphosis of an identified serotonergic neuron in the Drosophila olfactory system

    Neural development 2:20.

    https://doi.org/10.1186/1749-8104-2-20

    • PubMed
    • Google Scholar
66. 1. Rybak J
    2. Talarico G
    3. Ruiz S
    4. Arnold C
    5. Cantera R
    6. Hansson BS

    (2016) Synaptic circuitry of identified neurons in the antennal lobe of Drosophila melanogaster

    Journal of Comparative Neurology 524:1920–1956.

    https://doi.org/10.1002/cne.23966

    • PubMed
    • Google Scholar
67. 1. Saalfeld S
    2. Cardona A
    3. Hartenstein V
    4. Tomancak P

    (2009) CATMAID: collaborative annotation toolkit for massive amounts of image data

    Bioinformatics 25:1984–1986.

    https://doi.org/10.1093/bioinformatics/btp266

    • PubMed
    • Google Scholar
68. 1. Saalfeld S
    2. Fetter R
    3. Cardona A
    4. Tomancak P

    (2012) Elastic volume reconstruction from series of ultra-thin microscopy sections

    Nature Methods 9:717–720.

    https://doi.org/10.1038/nmeth.2072

    • PubMed
    • Google Scholar
69. 1. Salcedo E
    2. Farrell DM
    3. Zheng L
    4. Phistry M
    5. Bagg EE
    6. Britt SG

    (2009) The green-absorbing Drosophila Rh6 visual pigment contains a blue-shifting amino acid substitution that is conserved in vertebrates

    Journal of Biological Chemistry 284:5717–5722.

    https://doi.org/10.1074/jbc.M807368200

    • PubMed
    • Google Scholar
70. 1. Sanes JR
    2. Zipursky SL

    (2010) Design principles of insect and vertebrate visual systems

    Neuron 66:15–36.

    https://doi.org/10.1016/j.neuron.2010.01.018

    • PubMed
    • Google Scholar
71. 1. Sawin-McCormack EP
    2. Sokolowski MB
    3. Campos AR

    (1995) Characterization and genetic analysis of Drosophila melanogaster photobehavior during larval development

    Journal of Neurogenetics 10:119–135.

    https://doi.org/10.3109/01677069509083459

    • PubMed
    • Google Scholar
72. 1. Schlegel P
    2. Texada MJ
    3. Miroschnikow A
    4. Schoofs A
    5. Hückesfeld S
    6. Peters M
    7. Schneider-Mizell CM
    8. Lacin H
    9. Li F
    10. Fetter RD
    11. Truman JW
    12. Cardona A
    13. Pankratz MJ

    (2016) Synaptic transmission parallels neuromodulation in a central food-intake circuit

    eLife 5:e16799.

    https://doi.org/10.7554/eLife.16799

    • PubMed
    • Google Scholar
73. 1. Schneider-Mizell CM
    2. Gerhard S
    3. Longair M
    4. Kazimiers T
    5. Li F
    6. Zwart MF
    7. Champion A
    8. Midgley FM
    9. Fetter RD
    10. Saalfeld S
    11. Cardona A

    (2016) Quantitative neuroanatomy for connectomics in Drosophila

    eLife 5:e12059.

    https://doi.org/10.7554/eLife.12059

    • PubMed
    • Google Scholar
74. 1. Schulze A
    2. Gomez-Marin A
    3. Rajendran VG
    4. Lott G
    5. Musy M
    6. Ahammad P
    7. Deogade A
    8. Sharpe J
    9. Riedl J
    10. Jarriault D
    11. Trautman ET
    12. Werner C
    13. Venkadesan M
    14. Druckmann S
    15. Jayaraman V
    16. Louis M

    (2015) Dynamical feature extraction at the sensory periphery guides chemotaxis

    eLife 4:e06694.

    https://doi.org/10.7554/eLife.06694

    • Google Scholar
75. 1. Selcho M
    2. Pauls D
    3. El Jundi B
    4. Stocker RF
    5. Thum AS

    (2012) The role of octopamine and tyramine in Drosophila larval locomotion

    The Journal of Comparative Neurology 520:3764–3785.

    https://doi.org/10.1002/cne.23152

    • PubMed
    • Google Scholar
76. 1. Selcho M
    2. Pauls D
    3. Han KA
    4. Stocker RF
    5. Thum AS

    (2009) The role of dopamine in Drosophila larval classical olfactory conditioning

    PLoS One 4:e5897.

    https://doi.org/10.1371/journal.pone.0005897

    • PubMed
    • Google Scholar
77. 1. Selcho M
    2. Pauls D
    3. Huser A
    4. Stocker RF
    5. Thum AS

    (2014) Characterization of the octopaminergic and tyraminergic neurons in the central brain of Drosophila larvae

    Journal of Comparative Neurology 522:3485–3500.

    https://doi.org/10.1002/cne.23616

    • PubMed
    • Google Scholar
78. 1. Shinomiya K
    2. Karuppudurai T
    3. Lin TY
    4. Lu Z
    5. Lee CH
    6. Meinertzhagen IA

    (2014) Candidate neural substrates for off-edge motion detection in Drosophila

    Current Biology 24:1062–1070.

    https://doi.org/10.1016/j.cub.2014.03.051

    • PubMed
    • Google Scholar
79. 1. Slepian Z
    2. Sundby K
    3. Glier S
    4. McDaniels J
    5. Nystrom T
    6. Mukherjee S
    7. Acton ST
    8. Condron B

    (2015) Visual attraction in Drosophila larvae develops during a critical period and is modulated by crowding conditions

    Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology 201:1019–1027.

    https://doi.org/10.1007/s00359-015-1034-3

    • PubMed
    • Google Scholar
80. 1. Sprecher SG
    2. Cardona A
    3. Hartenstein V

    (2011) The Drosophila larval visual system: high-resolution analysis of a simple visual neuropil

    Developmental Biology 358:33–43.

    https://doi.org/10.1016/j.ydbio.2011.07.006

    • PubMed
    • Google Scholar
81. 1. Sprecher SG
    2. Desplan C

    (2008) Switch of rhodopsin expression in terminally differentiated Drosophila sensory neurons

    Nature 454:533–537.

    https://doi.org/10.1038/nature07062

    • PubMed
    • Google Scholar
82. 1. Sprecher SG
    2. Reichert H
    3. Hartenstein V

    (2007) Gene expression patterns in primary neuronal clusters of the Drosophila embryonic brain

    Gene Expression Patterns 7:584–595.

    https://doi.org/10.1016/j.modgep.2007.01.004

    • PubMed
    • Google Scholar
83. 1. Strausfeld NJ
    2. Sinakevitch I
    3. Okamura JY

    (2007) Organization of local interneurons in optic glomeruli of the dipterous visual system and comparisons with the antennal lobes

    Developmental Neurobiology 67:1267–1288.

    https://doi.org/10.1002/dneu.20396

    • PubMed
    • Google Scholar
84. Book

    1. Strausfeld NJ

    (1989)

    Insect Vision and Olfaction: Common Design Principles of Neuronal Organization

    In: Singh R. N, Strausfeld N. J, editors. Neurobiology of Sensory Systems. Boston, MA: Springer US. pp. 319–353.

    • Google Scholar
85. 1. Strother JA
    2. Nern A
    3. Reiser MB

    (2014) Direct observation of ON and OFF pathways in the Drosophila visual system

    Current Biology 24:976–983.

    https://doi.org/10.1016/j.cub.2014.03.017

    • PubMed
    • Google Scholar
86. 1. Suver MP
    2. Mamiya A
    3. Dickinson MH

    (2012) Octopamine neurons mediate flight-induced modulation of visual processing in Drosophila

    Current Biology 22:2294–2302.

    https://doi.org/10.1016/j.cub.2012.10.034

    • PubMed
    • Google Scholar
87. 1. Takemura SY
    2. Bharioke A
    3. Lu Z
    4. Nern A
    5. Vitaladevuni S
    6. Rivlin PK
    7. Katz WT
    8. Olbris DJ
    9. Plaza SM
    10. Winston P
    11. Zhao T
    12. Horne JA
    13. Fetter RD
    14. Takemura S
    15. Blazek K
    16. Chang LA
    17. Ogundeyi O
    18. Saunders MA
    19. Shapiro V
    20. Sigmund C
    21. Rubin GM
    22. Scheffer LK
    23. Meinertzhagen IA
    24. Chklovskii DB

    (2013) A visual motion detection circuit suggested by Drosophila connectomics

    Nature 500:175–181.

    https://doi.org/10.1038/nature12450

    • PubMed
    • Google Scholar
88. 1. Takemura SY
    2. Xu CS
    3. Lu Z
    4. Rivlin PK
    5. Parag T
    6. Olbris DJ
    7. Plaza S
    8. Zhao T
    9. Katz WT
    10. Umayam L
    11. Weaver C
    12. Hess HF
    13. Horne JA
    14. Nunez-Iglesias J
    15. Aniceto R
    16. Chang LA
    17. Lauchie S
    18. Nasca A
    19. Ogundeyi O
    20. Sigmund C
    21. Takemura S
    22. Tran J
    23. Langille C
    24. Le Lacheur K
    25. McLin S
    26. Shinomiya A
    27. Chklovskii DB
    28. Meinertzhagen IA
    29. Scheffer LK

    (2015) Synaptic circuits and their variations within different columns in the visual system of Drosophila

    PNAS 112:13711–13716.

    https://doi.org/10.1073/pnas.1509820112

    • PubMed
    • Google Scholar
89. 1. Tix S
    2. Minden JS
    3. Technau GM

    (1989)

    Pre-existing neuronal pathways in the developing optic lobes of Drosophila

    Development 105:739–746.

    • PubMed
    • Google Scholar
90. 1. Tobin WF
    2. Wilson RI
    3. Lee WA

    (2017) Wiring variations that enable and constrain neural computation in a sensory microcircuit

    eLife 6:e24838.

    https://doi.org/10.7554/eLife.24838

    • PubMed
    • Google Scholar
91. 1. Varshney LR
    2. Chen BL
    3. Paniagua E
    4. Hall DH
    5. Chklovskii DB

    (2011) Structural properties of the Caenorhabditis elegans neuronal network

    PLoS computational biology 7:e1001066.

    https://doi.org/10.1371/journal.pcbi.1001066

    • PubMed
    • Google Scholar
92. 1. von Essen AM
    2. Pauls D
    3. Thum AS
    4. Sprecher SG

    (2011) Capacity of visual classical conditioning in Drosophila larvae

    Behavioral Neuroscience 125:921–929.

    https://doi.org/10.1037/a0025758

    • PubMed
    • Google Scholar
93. 1. Vosshall LB
    2. Stocker RF

    (2007) Molecular architecture of smell and taste in Drosophila

    Annual Review of Neuroscience 30:505–533.

    https://doi.org/10.1146/annurev.neuro.30.051606.094306

    • PubMed
    • Google Scholar
94. 1. Wasserman SM
    2. Aptekar JW
    3. Lu P
    4. Nguyen J
    5. Wang AL
    6. Keles MF
    7. Grygoruk A
    8. Krantz DE
    9. Larsen C
    10. Frye MA

    (2015) Olfactory neuromodulation of motion vision circuitry in Drosophila

    Current biology : CB 25:467–472.

    https://doi.org/10.1016/j.cub.2014.12.012

    • PubMed
    • Google Scholar
95. 1. Wegener C
    2. Hamasaka Y
    3. Nässel DR

    (2004) Acetylcholine increases intracellular Ca2+ via nicotinic receptors in cultured PDF-containing clock neurons of Drosophila

    Journal of Neurophysiology 91:912–923.

    https://doi.org/10.1152/jn.00678.2003

    • PubMed
    • Google Scholar
96. 1. Wilson RI
    2. Laurent G

    (2005) Role of GABAergic inhibition in shaping odor-evoked spatiotemporal patterns in the Drosophila antennal lobe

    Journal of Neuroscience 25:9069–9079.

    https://doi.org/10.1523/JNEUROSCI.2070-05.2005

    • PubMed
    • Google Scholar
97. 1. Wolf H
    2. Burrows M

    (1995)

    Proprioceptive sensory neurons of a locust leg receive rhythmic presynpatic inhibition during walking

    Journal of Neuroscience 15:5623–5636.

    • PubMed
    • Google Scholar
98. 1. Wu M
    2. Nern A
    3. Williamson WR
    4. Morimoto MM
    5. Reiser MB
    6. Card GM
    7. Rubin GM

    (2016) Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs

    eLife 5:e21022.

    https://doi.org/10.7554/eLife.21022

    • PubMed
    • Google Scholar
99. 1. Yamanaka N
    2. Romero NM
    3. Martin FA
    4. Rewitz KF
    5. Sun M
    6. O'Connor MB
    7. Léopold P

    (2013) Neuroendocrine control of Drosophila larval light preference

    Science 341:1113–1116.

    https://doi.org/10.1126/science.1241210

    • PubMed
    • Google Scholar
100. 1. Yasuyama K
     2. Kitamoto T
     3. Salvaterra PM

     (1995) Localization of choline acetyltransferase-expressing neurons in the larval visual system of Drosophila melanogaster

     Cell & Tissue Research 282:193–202.

     https://doi.org/10.1007/BF00319111

     • PubMed
     • Google Scholar
101. 1. Yasuyama K
     2. Meinertzhagen IA

     (2010) Synaptic connections of PDF-immunoreactive lateral neurons projecting to the dorsal protocerebrum of Drosophila melanogaster

     The Journal of Comparative Neurology 518:292–304.

     https://doi.org/10.1002/cne.22210

     • PubMed
     • Google Scholar
102. 1. Yasuyama K
     2. Salvaterra PM

     (1999) Localization of choline acetyltransferase-expressing neurons in Drosophila nervous system

     Microscopy research and technique 45:65–79.

     https://doi.org/10.1002/(SICI)1097-0029(19990415)45:2<65::AID-JEMT2>3.0.CO;2-0

     • PubMed
     • Google Scholar
103. 1. Yuan Q
     2. Lin F
     3. Zheng X
     4. Sehgal A

     (2005) Serotonin modulates circadian entrainment in Drosophila

     Neuron 47:115–127.

     https://doi.org/10.1016/j.neuron.2005.05.027

     • PubMed
     • Google Scholar
104. 1. Yuan Q
     2. Xiang Y
     3. Yan Z
     4. Han C
     5. Jan LY
     6. Jan YN

     (2011) Light-induced structural and functional plasticity in Drosophila larval visual system

     Science 333:1458–1462.

     https://doi.org/10.1126/science.1207121

     • PubMed
     • Google Scholar

Author details

1. Ivan Larderet

   Department of Biology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Pauline MJ Fritsch

   Competing interests

   No competing interests declared

2. Pauline MJ Fritsch

   Department of Biology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Ivan Larderet

   Competing interests

   No competing interests declared

3. Nanae Gendre

   Department of Biology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Data curation, Visualization, Methodology

   Competing interests

   No competing interests declared

4. G Larisa Neagu-Maier

   Department of Biology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Data curation, Formal analysis, Visualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

5. Richard D Fetter

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Resources, Data curation, Methodology

   Competing interests

   No competing interests declared

6. Casey M Schneider-Mizell

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Resources, Data curation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9477-3853

7. James W Truman

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Investigation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

8. Marta Zlatic

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Resources, Data curation, Investigation

   Competing interests

   No competing interests declared

9. Albert Cardona

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Investigation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4941-6536

10. Simon G Sprecher

    Department of Biology, University of Fribourg, Fribourg, Switzerland

    Contribution

    Conceptualization, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    simon.sprecher@gmail.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9060-3750

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