Dramatic increases in the speed and quality of imaging, segmentation and reconstruction in electron microscopy now allow large-scale, dense connectomic studies of nervous systems. Such studies can reveal the chemical synapses between all neurons, generating a complete connectivity map. Connectomics is particularly useful in generating biological insights when applied to an ensemble of neurons with interesting behavioral functions that have already been extensively studied experimentally. Knowing the effects on behavior and physiology of perturbing individual cell types that can also be unambiguously identified in the connectome is of considerable value. Here, we present a connectomic analysis of one such neuronal ensemble, the mushroom body (MB) of an adult Drosophila melanogaster.

Understanding how memories of past events are formed and then used to influence ongoing behavior are key challenges in neuroscience. It is generally accepted that parallel changes in connection strength across multiple circuits underlie the formation of a memory and that these changes are integrated to produce net changes in behavior. Animals learn to predict the value of sensory cues based on temporal correlations with reward or punishment (Pavlov and Thompson, 1902). Such associative learning entails lasting changes in connections between neurons (reviewed in Abraham et al., 2019; Martin et al., 2000). It is now clear that different parts of the brain process and store different aspects of the information learned in a single event (reviewed in Josselyn and Frankland, 2018). In both flies and mammals, dopaminergic neurons play a key role in conveying information about whether an event has a positive or negative valence, and there are compelling parallels between the molecular diversity of dopaminergic cell types across these evolutionarily distant animals (Watabe-Uchida and Uchida, 2018). However, we have limited understanding of how information about the outside world or internal brain state reaches different dopaminergic populations. Nor do we understand the nature of the information that is stored in each parallel memory system or how these parallel memories interact to guide coherent behavior. We believe such processes are governed by general and evolutionarily-conserved principles. In particular, we believe the circuit logic that allows a brain to balance the competing demands of rapid learning with long-term stability of memory are likely to be the same in flies and mammals. Developing a comprehensive understanding of these circuits at the resolution of individual neurons and synapses will require the synergistic application of a variety of experimental methods together with theory and modeling. Many of the required methods are well developed in Drosophila, where the circuits underlying learning and memory are less complex than in mammals, and where detailed anatomical knowledge of the relevant circuits, which we believe will be essential, has just now become available. Here, we provide analysis of the complete connectome of a circuit involved in parallel processing of associative memories in adult fruit flies. The core architecture of this circuit is strikingly similar to that of the vertebrate cerebellum (Figure 1; Laurent, 2002; Farris, 2011; Litwin-Kumar et al., 2017).

Figure 1 with 2 supplements see all

Download asset Open asset

The MB is the major site of associative learning in insects (reviewed in Heisenberg, 2003; Modi et al., 2020), and species that perform more complex behavioral tasks tend to have larger MBs (O'Donnell et al., 2004; Sivinski, 1989). In the MB of each brain hemisphere, sensory stimuli are represented by the sparse activity of ~2000 Kenyon cells (KCs) whose dendrites form a structure called the MB calyx and whose parallel axonal fibers form the lobes of the MB (Figure 2; Figure 1—video 1).

Figure 2 with 1 supplement see all

Download asset Open asset

The major sensory inputs to the Drosophila MB are olfactory, delivered by ~150 projection neurons (PNs) from the antennal lobe to the dendrites of the KCs in the MB calyx (Bates et al., 2020b). KCs each receive input from an average of six PNs. For a KC to fire a spike, several of its PN inputs need to be simultaneously activated (Gruntman and Turner, 2013). This requirement, together with global feedback inhibition (Lin et al., 2014a; Papadopoulou et al., 2011), ensures a sparse representation where only a small percentage of KCs are activated by an odor (Honegger et al., 2011; Perez-Orive et al., 2002). The MB has a three layered divergent-convergent architecture (Huerta et al., 2004; Jortner et al., 2007; Laurent, 2002; Litwin-Kumar et al., 2017; Shomrat et al., 2011; Stevens, 2015) in which the coherent information represented by olfactory PNs is expanded and decorrelated when delivered to the KCs (Caron et al., 2013; Zheng et al., 2020). But the degree to which the structure of the sensory input representation is maintained by the KCs has been debated. We explore this issue, taking advantage of a nearly comprehensive dataset of KC inputs and outputs.

While best studied for its role in olfactory associative learning, the MB also receives inputs from several other sensory modalities. A subset of projection neurons from the antennal lobe delivers information about temperature and humidity in both the larva (Eichler et al., 2017) and the adult (Frank et al., 2015; Liu et al., 2015; Marin et al., 2020; Stocker et al., 1990). Taste conditioning also requires the MB and is believed to depend on specific KC populations, although the relevant inputs to these KCs have not yet been reported (Kirkhart and Scott, 2015; Masek and Scott, 2010). We identified one likely path for gustatory input to the MB.

Drosophila MBs are also known to be able to form memories based on visual cues (Aso et al., 2014b; Brembs, 2009; Vogt et al., 2016; Vogt et al., 2014; Liu et al., 1999; Zhang et al., 2007). Until a few years ago, it was thought that visual input reached the Drosophila MBs using only indirect, multisynaptic pathways (Farris and Van Dyke, 2015; Tanaka et al., 2008) as direct visual input from the optic lobes to the MBs, well known in Hymenoptera (Ehmer and Gronenberg, 2002), had not been observed in any dipteran insect (Mu et al., 2012; Otsuna and Ito, 2006). In 2016, Vogt et al., 2016 identified two types of visual projection neurons (VPNs) connecting the optic lobes and the MB and additional connections have been observed recently by light microscopy (Li et al., 2020). We found that visual input was much more extensive than previously appreciated, with about 8% of KCs receiving predominantly visual input, and present here a detailed description of neuronal pathways connecting the optic lobe and the MB. Visual sensory input appears to be segregated into distinct KC populations in both the larva (Eichler et al., 2017) and the adult (Vogt et al., 2016; Li et al., 2020), as is the case in honeybees (Ehmer and Gronenberg, 2002). We found two classes of KCs that receive predominantly visual sensory input, as well as MBONs that get the majority of their input from these segregated KC populations.

MBONs provide the convergence element of the MB’s three layer divergent-convergent circuit architecture. Previous work has identified 22 types of MBONs whose dendrites receive input from specific axonal segments of the KCs. The outputs of the MBONs drive learned behaviors. Approximately 20 types of dopaminergic neurons (DANs) innervate corresponding regions along the KC axons and are required for associative olfactory conditioning. Specifically, the presynaptic arbors of the DANs and postsynaptic dendrites of the MBONs overlap in distinct zones along the KC axons, defining the 15 compartmental units of the MB lobes (Aso et al., 2014a; Mao and Davis, 2009; Takemura et al., 2017; Tanaka et al., 2008; Figure 2; Figure 1—video 2). A large body of evidence indicates that these anatomically defined compartments of the MB are the units of associative learning (Aso et al., 2012; Aso et al., 2010; Aso et al., 2019; Aso et al., 2014a; Aso et al., 2014b; Aso and Rubin, 2016; Berry et al., 2018; Blum et al., 2009; Bouzaiane et al., 2015; Burke et al., 2012; Claridge-Chang et al., 2009; Isabel et al., 2004; Jacob and Waddell, 2020; Krashes et al., 2009; Lin et al., 2014b; Liu et al., 2012; Owald et al., 2015; Pai et al., 2013; Perisse et al., 2016; Qin et al., 2012; Plaçais et al., 2013; Schwaerzel et al., 2003; Séjourné et al., 2011; Trannoy et al., 2011; Yamagata et al., 2015; Zars et al., 2000).

The DANs innervating different MBON compartments appear to play distinct roles in signaling reward vs. punishment, novelty vs. familiarity, the presence of olfactory cues and the activity state of the fly (Aso et al., 2010; Aso and Rubin, 2016; Burke et al., 2012; Cohn et al., 2015; Hattori et al., 2017; Liu et al., 2012; Sitaraman et al., 2015b; Tsao et al., 2018). These differences between DAN cell types presumably reflect in large part the nature of the inputs that each DAN receives, but our knowledge of these inputs is just emerging (Otto et al., 2020) and is far from comprehensive. DANs adjust synaptic weights between KCs and MBONs with cell type-specific rules and, in at least some cases, these differences arise from the effects of co-transmitters (Aso et al., 2019). In general, a causal association of KC responses with the activation of a DAN in a compartment results in depression of the synapses from the active KCs onto MBONs innervating that compartment (Hige et al., 2015; Handler et al., 2019). Different MB compartments are known to store and update non-redundant information as an animal experiences a series of learning events (Berry et al., 2018; Felsenberg et al., 2017; Felsenberg et al., 2018). In rodent and primate brains, recent studies have revealed that dopaminergic neurons are also molecularly diverse and encode prediction errors and other information based on cell type-specific rules (Hu, 2016; Menegas et al., 2018; Poulin et al., 2020; Watabe-Uchida et al., 2017).

MBONs convey information about learned associations to the rest of the brain. Activation of individual MBONs can cause behavioral attraction or repulsion, according to the compartment in which their dendrites arborize (Aso et al., 2014b; Owald et al., 2015; Perisse et al., 2016). The combined output of multiple MBONs is likely to be integrated in downstream networks, but we do not understand how memories stored in multiple MB compartments alter these integrated signals to guide coherent and appropriate behaviors. Prior anatomical studies implied the existence of multiple layers of interneurons between MBONs and descending motor pathways (Aso et al., 2014a). What is the nature of information processing in those layers? Anatomical studies using light microscopy provided the first hints. MBONs from different compartments send their outputs to the same brain regions, suggesting that they might converge on shared downstream targets. DANs often project to these same brain areas, raising the possibility of direct interaction between MBONs and DANs. The functional significance of such interactions has just begun to be investigated (Felsenberg et al., 2017; Felsenberg et al., 2018; Ichinose et al., 2015; Jacob and Waddell, 2020; Pavlowsky et al., 2018; Perisse et al., 2016; Zhao et al., 2018b), and studies of the Drosophila larva, where a connectome of a numerically less complex MB is available (Eichler et al., 2017), are providing valuable insights (Eschbach et al., 2020a; Eschbach et al., 2020b; Saumweber et al., 2018).

The recently determined connectome of a portion of an adult female fly brain (hemibrain; see Figure 2—figure supplement 1) provides connectivity data for ~22,500 neurons (Scheffer et al., 2020). Among them, ~2600 neurons have axons or dendrites in the MB, while ~1500 neurons are directly downstream of MBONs (using a threshold of 10 synapses from each MBON to each downstream target) and ~3200 are upstream of MB dopaminergic neurons (using a threshold of five synapses from each upstream neuron to each DAN). Thus we will consider approximately one-third of the neurons in the central brain in our analysis of the MB ensemble.

Throughout the paper we set synaptic thresholds in order to focus our descriptions and analyses on the most strongly connected neurons. In the above analysis, we chose a higher threshold for MBON connections to downstream targets than for DAN inputs because the typical MBON has many more output synapses than a DAN has input synapses. At a thresholds of five synapses, DANs have a median of 31 different input neurons, but if we increased the threshold to 10 synapses this would decrease to only six different neurons. In contrast, at the threshold of 10 synapses, MBONs are connected to a median of 90 downstream neurons. There were some limitations resulting from not having a wiring diagram of the full central nervous system, as we lacked complete connectivity information for neurons with processes that extended outside the hemibrain volume (Figure 2—figure supplement 1). We were generally able to mitigate these limitations by identifying the corresponding neurons in other EM or light microscopic datasets when the missing information was important for our analyses. Thus the hemibrain dataset was able to support a nearly comprehensive examination of the full neural network underlying the MB ensemble.

Studies of the larval MB are providing parallel information on the structure and function of an MB with most of the same cell types, albeit fewer copies of each (Eichler et al., 2017; Eschbach et al., 2020a; Eschbach et al., 2020b; Saumweber et al., 2018). The microcircuits inside three MB compartments in the adult were previously described (Takemura et al., 2017) and we report here that the overall organization of these three compartments is conserved in a second individual of a different gender. More importantly, we extend the analysis of microcircuits within the MB lobes to all 15 compartments, revealing additional aspects of spatial organization within individual compartments.

In the current study, we were able to discover new morphological subtypes of KCs and to determine the sensory inputs delivered to the dendrites of each of the ~2000 KC. We found considerable structure in the organization of those inputs and unexpectedly high levels of visual input, which was the majority sensory input for two classes of KCs. This segregation of distinct sensory representations into channels is maintained across the MB, such that MBONs, by sampling from different KCs, have access to different sensory modalities and representations. We discovered a new class of ‘atypical’ MBONs, consisting of 14 cell types, that have part of their dendritic arbors outside the MB lobes, allowing them to integrate input from KCs with other information; at least five of them make strong, direct synaptic contact onto descending neurons that drive motor action. We describe how MBONs from different compartments interact with each other to potentially integrate and transform the signals passed from the MB to the rest of the brain, revealing a number of circuit motifs including multi-layered MBON-to-MBON feedforward networks and extensive convergence both onto common targets and onto each other through axo-axonal connections. Finally, we analyzed the inputs to all 158 DANs that innervate the MB. We found extensive direct feedback from MBONs to the dendrites of DANs, providing a mechanism of communication within and between MB compartments. We also found groups of DANs that share common inputs, providing mechanistic insights into the distributed parallel processing of aversive and appetitive reinforcement and other experimental observations.

The hemibrain dataset we have analyzed contains the most comprehensive survey of the cell types and connectivity of MB neurons available to date. This connectome allowed us to probe in fine detail the circuitry underlying canonically proposed functions of the MB, including the representation of olfactory information by KCs, computation of valence by MBONs, and reinforcement of associations by DANs. We found patterns in the input to DANs, MBON-to-DAN and MBON-to-MBON connectivity that suggest how associative learning in the MB can affect both the acquisition of new information through learning and the expression of previously learned responses. The connectome also reveals circuitry that supports non-canonical MB functions, including selective structure in non-olfactory pathways, a network of atypical MBONs, extensive heterogeneity in DAN inputs, and connections to central brain areas involved in navigation and movement.

Sensory input to KCs

Previous theoretical work has emphasized the advantages of mixing sensory input to the KCs so that they provide a high-dimensional representation from which MBONs, guided by DAN modulation, can derive associations between sensory input and stimulus-valence (Litwin-Kumar et al., 2017). The connectome data modifies this viewpoint in two ways. First, although olfactory input to the KCs is highly mixed, various structural features reduce the dimensionality of the KC odor representation. A recent analysis of EM data from an adult Drosophila MB identified groups of PNs thought to represent food odors that are preferentially sampled by certain KCs (Zheng et al., 2020). Consistent with this observation, our analysis revealed subtype-specific biases in PN sampling by KCs, including an overrepresentation of specific glomeruli by α/β and α′/β′ KCs (Figure 13B). These biases, as well as other structural features, appear to arise from the stereotyped arrangement of PN axons within the calyx and their local sampling by KCs. This may reflect a developmental strategy by which the KC representation is organized to preferentially represent particular PN combinations. Further analyses of KC connectomes across hemispheres and animals, as well as experimental studies, will help evaluate the impact of this structure.

The hemibrain connectome also revealed a second, more dramatic, structural feature of sensory input to the MB: non-olfactory input streams corresponding to visual and thermo-hygro sensation are strongly segregated. This organization may reflect the nature of stimulus-valence associations experienced by flies. In purely olfactory learning, when valence is associated with particular sets of olfactory receptors, MBONs need to be able to sample those combinations to successfully identify the stimulus. This requires that KCs mix input from multiple glomeruli within the olfactory stream. However, KCs that mix across streams corresponding to different sensory modalities may not be necessary if each modality can be used separately to identify valence. For example, either the visual appearance of an object or its odor may individually be sufficient to identify it as a food source (for a discussion of multi-sensory learning in flies, see Guo and Guo, 2005). We asked whether there is an advantage in having separate modality-specific sensory pathways, as seen in the connectome data, when valences can be decomposed in this way.

To address this question we considered a model in which KC input is divided into two groups, visual and olfactory (Figure 38A). In one version of the model (‘shared KC population’), all KCs receive sparse random input from both types of PNs, corresponding to a high degree of mixing across modalities. In the other (‘separate KC populations’), half of the KCs receive sparse random input exclusively from visual PNs and half only from olfactory PNs, corresponding to no cross-modal mixing. We define two tasks that differ in the way valences are defined. In the first (factorizable) version of the task, each olfactory stimulus and each visual stimulus is assigned a positive or neutral subvalence, and the net valence of the combined stimulus is positive if either of these components is positive (olfactory OR visual). In the second (unfactorizable) version, a valence is randomly assigned independently to each olfactory+visual stimulus pair. We evaluated the ability of a model MBON, acting as a linear readout, to determine valence from these two different KC configurations. Separate modality-specific KC populations are indeed beneficial when the valence can be identified from either one modality or the other (the 'factorizable' case in Figure 38B). Dedicating different KC subtypes to distinct sensory modalities allows the predictive value of each modality to be learned separately. This result suggests that the divisions into KCs specialized for visual, thermo/hygro and olfactory signals may reflect how natural stimuli of different modalities are predictive of valence.

Figure 38

Download asset Open asset

Sub-compartmental modulation by DANs

On the basis of light-level studies, DAN modulation of KC-to-MBON synapses has been considered to operate at the resolution of MB compartments. However, taken with other recent studies (Lee et al., 2020; Otto et al., 2020), the morphology and connectivity data indicate that functionally distinct PAM-DAN subtypes operate within a MB compartment. DAN subtypes receive different inputs and likely modulate different KC-MBON synapses within a compartment.

A prior analysis showed that PAM01-fb DANs were required to reinforce the absence of expected shock during aversive memory extinction, whereas a different set of γ5 DANs were needed for learning with sugar reinforcement (Otto et al., 2020). It is conceivable that another γ5 DAN subtype is required for male flies to learn courtship rejection (Keleman et al., 2012). The connectome data revealed DAN subtypes in every compartment that is innervated by PAM DANs.

The γ3 compartment provides an interesting example of subcompartmental targeting of modulation by DANs and of KC input onto MBONs (Figure 39). There are two subtypes of PAM DANs and three types of MBONs in the γ3 compartment. MBON09 and MBON30 primarily receive olfactory information from γm KCs whereas MBON33 primarily receives visual information from γd KCs. PAM12-dd and PAM12-md DANs appear to modulate KC inputs to MBON09/MBON30 or MBON33, respectively. Although existing driver lines do not separate the dd and md subtypes, PAM-γ3 DAN activity is suppressed by sugar (Yamagata et al., 2016) and activated by electric shock (Jacob and Waddell, 2020). We found that PAM12-dd DANs share input with PPL1 DANs conveying punishment signals, while PAM12-md DANs are co-wired with PAM08 (γ4) DANs conveying reward signals. Thus, synaptic transmission from two sets of modality-specific KCs to different MBONs can be independently modulated by DANs signaling different valences, all within a single compartment.

Figure 39

Download asset Open asset

To explore the implications of DAN modulation that is specific to sensory modality, we extended the model presented above (Figure 38A) by including two DANs, one conveying the visual component of valence and the other the olfactory component (Figure 38C). KCs were divided into visual and olfactory modalities, and we considered two configurations for DAN modulation, one, ‘shared reward signal’, that is compartment-wide and non-specific, and the other, ‘separate reward signals’, in which each DAN only induces plasticity onto KC synapses matching its own modality. This latter case models a set of DANs that affect synapses from visual KCs onto the MBON and another set that affect olfactory synapses (alternatively, it could model two MBONs in different compartments that are modulated independently and converge onto a common target). We find that, when KCs are divided into separate populations and separate modalities can be used to identify valence, learning is more efficient if the pathways are modulated individually (Figure 38D).

MBON-MBON interactions

Feedforward MBON-MBON connections were postulated, based on behavioral and light microscopic anatomical observations, to propagate local plasticity between compartments (Aso et al., 2014a; Aso et al., 2014b). The GABAergic MBON11 is poised to play a key role in such propagation as it is connected with 17 other MBONs (at a threshold of 10 synapses). Thus local depression of KC-MBON11 synapses by the shock responsive PPL1-γ1pedc DAN would be expected to result in disinhibition of MBONs in other compartments. Indeed enhanced CS+ responses of MBON01 and MBON03 after odor-shock conditioning have been ascribed to release from MBON11 inhibition (Owald et al., 2015; Perisse et al., 2016; Felsenberg et al., 2018). The consequence of releasing other strongly connected MBONs (MBON07, MBON14 and MBON29) from MBON11 inhibition awaits future study. As discussed above, MBON11 is also connected to DANs innervating its cognate compartment and to DANs innervating other compartments. MBON11 may therefore coordinate MBON network activity via both direct and indirect mechanisms. We also found analogous feedforward inhibitory connections from MBON09 (γ3β′1) to MBON01 and MBON03. Aversive learning therefore reduces both MBON11- and MBON09-mediated inhibition of these MBONs, which further skews the MBON network toward directing avoidance of the previously punished odor (Figure 40).

Figure 40

Download asset Open asset

Disinhibition likely also plays an important role in appetitive memory (Figure 41). The connectivity of MBON06 (β1>α) revealed here indicates that local plasticity in the β1 compartment can propagate to other MBONs. Similar to MBON11, MBON06 is directly connected with nine other MBONs with a threshold of 10 synapses. MBON06 gradually increases its response to repeated odor exposure (Hattori et al., 2017) and odor-evoked responses of β1 DANs vary with metabolic state (Siju et al., 2020). More compellingly, artificially triggering PAM10 (β1) DANs can assign appetitive valence to odors (Perisse et al., 2013; Huetteroth et al., 2015; Aso and Rubin, 2016). However, the role of MBON06 in appetitive memory and how the PAM10 (β1) DANs modulate KC synapses to MBON06 has not been investigated. The glutamatergic MBON06 (β1>α) makes a large number of reciprocal axoaxonic connections with the GABAergic MBON11 (Takemura et al., 2017), whose activation favors approach (Aso et al., 2014b; Perisse et al., 2016). This reciprocal network motif and the positive sign of behavior resulting from β1 DAN-driven memory suggests that MBON06 released glutamate is likely to be inhibitory to MBON11 and its other downstream targets. MBON06 also makes twice as many connections onto the glutamatergic MBON07 and the cholinergic MBON14 as does MBON11. MBON14 (α3) and MBON07 (α1) have established roles in appetitive memory (Plaçais et al., 2013; Huetteroth et al., 2015; Yamagata et al., 2015; Ichinose et al., 2015; Widmer et al., 2018). Assuming that PAM10 (β1) DANs encode appetitive memory by depressing synapses from odor-specific KCs onto MBON06 (β1>α), MBON06 suppression will release the feedforward inhibition of MBON06 onto MBON07 (α1), freeing it to participate in driving the PAM11-aad (α1) DANs (Ichinose et al., 2015). Release of MBON06 inhibition should also simultaneously potentiate the responses of MBON14 (α3) to the conditioned odor (Plaçais et al., 2013). Lastly, releasing the strong inhibition from MBON06 frees MBON11 to provide weaker inhibition that would further favor odor-driven approach (Perisse et al., 2016; Sayin et al., 2019).

Figure 41

Download asset Open asset

Aversive learning will also alter the function of the MBON06:MBON11 network motif (Figure 40). Aversive reinforcement through the PPL101 (γ1pedc) DAN depresses KC-MBON11 connections. This depression releases MBON06 from MBON11-mediated suppression and allows MBON06 to then suppress output through MBON07 and MBON11, further favoring odor-driven avoidance.

A possible role for MB output in the control of movement

A role for the MB in guiding locomotion and navigation in ants and other insects has been proposed (Ardin et al., 2016; Collett and Collett, 2018; Kamhi et al., 2020; Kim et al., 2019; Mizunami et al., 1998; Le Möel and Wystrach, 2020; Paulk and Gronenberg, 2008; Sun et al., 2020). The strong and direct connections we observed from the majority of MBONs to the CX, the fly’s navigation and locomotion control center, provide one circuit path for the MB to exert influence on motor behaviors. Discovering how this input is utilized by the CX will require additional experimental work.

Optogenetic activation of Drosophila MBONs can promote attraction or avoidance by influencing turning at the border between regions with and without stimulating light (Aso et al., 2014b). The effect of MBON activation is additive: coactivation of positive-valence MBONs produced stronger attraction, whereas coactivation of positive and negative-valence MBONs cancelled each other out. Because the fly needs to balance the outputs of different compartments, we expect that those downstream neurons that integrate inputs from multiple MBONs will have a privileged role in motor control.

The activity of some DANs has been shown to correlate with motor activity (Berry et al., 2015; Cohn et al., 2015), and the optogenetic activation of PAM-β′2 or PPL1-α3 DANs can attract flies, indicating that DAN activity can itself in some circumstances drive motor behavior. The circuit mechanisms generating the correlation between DAN activity and motor behavior remain to be discovered. Downstream targets of MBONs provide extensive input to DANs, and we found that neurons downstream of multiple MBONs are twice as likely as other MBON targets to provide such direct input to DANs.

We also discovered a direct pathway mediated by atypical MBONs that connects to the descending neurons (DNs) that control turning, an observation that provides additional support for the importance of the MB in the control of movement. The connections of these MBONs appear to be structured so as to promote directional movement, often involving a push-pull arrangement of MBONs signaling approach and avoidance. In addition to direct connections to DNs, there is a network of connections mediated by both local LAL interneurons and interneurons that connect the right and left hemisphere LALs. Examples of these circuit motifs are diagrammed in Figure 42. The atypical MBONs that connect directly to the descending steering system, MBON26, MBON27, MBON31 and MBON32, appear to be among the most integrative neurons in the MB system in the sense that they combine direct KC input from the MB compartments with both input from many other MBONs and non-MB input. At the level of the descending neurons, the highly processed signals from these MBONs are combined with inputs from many other sources, including the central complex, to affect a decision to turn. This high degree of integration presumably reflects the complexity and importance of this decision, with many factors involved that might act individually or in combination.

Figure 42 with 1 supplement see all

Download asset Open asset

Visual input to the MB is over-represented in the output to the descending neurons, predominantly through MBON27. Short- and long-term learning based on features in a visual scene has been reported to involve the CX (Liu et al., 2006; Neuser et al., 2008). Plasticity in the CX enables visual feature input from the sky and surrounding scenery to be mapped flexibly onto the fly’s internal compass (Fisher et al., 2019; Kim et al., 2019). The visual input conveyed to the MB and, presumably, the learning at the synapses between visual KCs and MBON27, may be of lower resolution, encoding broader features such as color and contrast (Guo and Guo, 2005; Tang and Guo, 2001; Zhang et al., 2007). An early study demonstrated that the MB is dispensable for flying Drosophila to learn shapes but that it is required for them to generalize their learning if the visual context changes between training and testing (Liu et al., 1999). Memory of visual features and the ability to generalize context could allow visual landmarks to help guide navigation either through the CX or by directly influencing descending neurons. The thermo/hygrosensory features conveyed by MBON26 could play a similar role, as could the large amount of odor-related information present in this MBON output pathway.

There is no institutional resource for hosting connectome data. All the primary data used in this study are freely available through a publicly accessible web site, neuprint.janelia.org. All the underlying data behind that server are open source (CC-BY). We commit to keeping this available for at least 10 years, and provide procedures that allow users to copy any or all of it to their own computer. Login is via any Google account; users who wish to remain anonymous can create a separate account for access purposes only.

1. 1. Scheffer LK
   2. Xu CS
   3. Januszewski M
   4. Luo Z
   5. Takemura Sy
   6. Hayworth K

   (2020) Figshare

   Resource Collection for a Connectome and Analysis of the Adult Drosophila Central Brain.

   https://doi.org/10.25378/janelia.12818645.v1

1. 1. Abraham WC
   2. Jones OD
   3. Glanzman DL

   (2019) Is plasticity of synapses the mechanism of long-term memory storage?

   Npj Science of Learning 4:9.

   https://doi.org/10.1038/s41539-019-0048-y

   • PubMed
   • Google Scholar
2. 1. Alpert MH
   2. Frank DD
   3. Kaspi E
   4. Flourakis M
   5. Zaharieva EE
   6. Allada R
   7. Para A
   8. Gallio M

   (2020) A circuit encoding absolute cold temperature in Drosophila

   Current Biology 30:2275–2288.

   https://doi.org/10.1016/j.cub.2020.04.038

   • PubMed
   • Google Scholar
3. 1. Ardin P
   2. Peng F
   3. Mangan M
   4. Lagogiannis K
   5. Webb B

   (2016) Using an insect mushroom body circuit to encode route memory in complex natural environments

   PLOS Computational Biology 12:e1004683.

   https://doi.org/10.1371/journal.pcbi.1004683

   • PubMed
   • Google Scholar
4. 1. Aso Y
   2. Grübel K
   3. Busch S
   4. Friedrich AB
   5. Siwanowicz I
   6. Tanimoto H

   (2009) The mushroom body of adult Drosophila characterized by GAL4 drivers

   Journal of Neurogenetics 23:156–172.

   https://doi.org/10.1080/01677060802471718

   • PubMed
   • Google Scholar
5. 1. Aso Y
   2. Siwanowicz I
   3. Bräcker L
   4. Ito K
   5. Kitamoto T
   6. Tanimoto H

   (2010) Specific dopaminergic neurons for the formation of labile aversive memory

   Current Biology 20:1445–1451.

   https://doi.org/10.1016/j.cub.2010.06.048

   • PubMed
   • Google Scholar
6. 1. Aso Y
   2. Herb A
   3. Ogueta M
   4. Siwanowicz I
   5. Templier T
   6. Friedrich AB
   7. Ito K
   8. Scholz H
   9. Tanimoto H

   (2012) Three dopamine pathways induce aversive odor memories with different stability

   PLOS Genetics 8:e1002768.

   https://doi.org/10.1371/journal.pgen.1002768

   • PubMed
   • Google Scholar
7. 1. Aso Y
   2. Hattori D
   3. Yu Y
   4. Johnston RM
   5. Iyer NA
   6. Ngo TT
   7. Dionne H
   8. Abbott LF
   9. Axel R
   10. Tanimoto H
   11. Rubin GM

   (2014a) The neuronal architecture of the mushroom body provides a logic for associative learning

   eLife 3:e04577.

   https://doi.org/10.7554/eLife.04577

   • PubMed
   • Google Scholar
8. 1. Aso Y
   2. Sitaraman D
   3. Ichinose T
   4. Kaun KR
   5. Vogt K
   6. Belliart-Guérin G
   7. Plaçais PY
   8. Robie AA
   9. Yamagata N
   10. Schnaitmann C
   11. Rowell WJ
   12. Johnston RM
   13. Ngo TT
   14. Chen N
   15. Korff W
   16. Nitabach MN
   17. Heberlein U
   18. Preat T
   19. Branson KM
   20. Tanimoto H
   21. Rubin GM

   (2014b) Mushroom body output neurons encode Valence and guide memory-based action selection in Drosophila

   eLife 3:e04580.

   https://doi.org/10.7554/eLife.04580

   • PubMed
   • Google Scholar
9. 1. Aso Y
   2. Ray RP
   3. Long X
   4. Bushey D
   5. Cichewicz K
   6. Ngo TT
   7. Sharp B
   8. Christoforou C
   9. Hu A
   10. Lemire AL
   11. Tillberg P
   12. Hirsh J
   13. Litwin-Kumar A
   14. Rubin GM

   (2019) Nitric oxide acts as a cotransmitter in a subset of dopaminergic neurons to diversify memory dynamics

   eLife 8:e49257.

   https://doi.org/10.7554/eLife.49257

   • PubMed
   • Google Scholar
10. 1. Aso Y
    2. Rubin GM

    (2016) Dopaminergic neurons write and update memories with cell-type-specific rules

    eLife 5:e16135.

    https://doi.org/10.7554/eLife.16135

    • PubMed
    • Google Scholar
11. 1. Awasaki T
    2. Lai SL
    3. Ito K
    4. Lee T

    (2008) Organization and postembryonic development of glial cells in the adult central brain of Drosophila

    Journal of Neuroscience 28:13742–13753.

    https://doi.org/10.1523/JNEUROSCI.4844-08.2008

    • PubMed
    • Google Scholar
12. 1. Awata H
    2. Takakura M
    3. Kimura Y
    4. Iwata I
    5. Masuda T
    6. Hirano Y

    (2019) The neural circuit linking mushroom body parallel circuits induces memory consolidation in Drosophila

    PNAS 116:16080–16085.

    https://doi.org/10.1073/pnas.1901292116

    • PubMed
    • Google Scholar
13. 1. Bailey M
    2. Clark D

    (1998) Using ChromaDepth to obtain inexpensive Single-image stereovision for scientific visualization

    Journal of Graphics Tools 3:1–9.

    https://doi.org/10.1080/10867651.1998.10487491

    • Google Scholar
14. 1. Bargmann CI
    2. Marder E

    (2013) From the connectome to brain function

    Nature Methods 10:483–490.

    https://doi.org/10.1038/nmeth.2451

    • PubMed
    • Google Scholar
15. 1. Barnstedt O
    2. Owald D
    3. Felsenberg J
    4. Brain R
    5. Moszynski JP
    6. Talbot CB
    7. Perrat PN
    8. Waddell S

    (2016) Memory-Relevant mushroom body output synapses are cholinergic

    Neuron 89:1237–1247.

    https://doi.org/10.1016/j.neuron.2016.02.015

    • PubMed
    • Google Scholar
16. 1. Bates AS
    2. Manton JD
    3. Jagannathan SR
    4. Costa M
    5. Schlegel P
    6. Rohlfing T
    7. Jefferis GS

    (2020a) The Natverse, a versatile toolbox for combining and analysing neuroanatomical data

    eLife 9:e53350.

    https://doi.org/10.7554/eLife.53350

    • PubMed
    • Google Scholar
17. 1. Bates AS
    2. Schlegel P
    3. Roberts RJV
    4. Drummond N
    5. Tamimi IFM
    6. Turnbull R
    7. Zhao X
    8. Marin EC
    9. Popovici PD
    10. Dhawan S
    11. Jamasb A
    12. Javier A
    13. Serratosa Capdevila L
    14. Li F
    15. Rubin GM
    16. Waddell S
    17. Bock DD
    18. Costa M
    19. Jefferis G

    (2020b) Complete connectomic reconstruction of olfactory projection neurons in the fly brain

    Current Biology 30:3183–3199.

    https://doi.org/10.1016/j.cub.2020.06.042

    • PubMed
    • Google Scholar
18. Software

    1. Bates AS
    2. Jefferis G

    (2020) hemibrainr: Code for working with data from Janelia FlyEM's hemibrain project, version 8ab212c

    Github.

    https://github.com/natverse/hemibrainr
19. 1. Berry JA
    2. Cervantes-Sandoval I
    3. Chakraborty M
    4. Davis RL

    (2015) Sleep facilitates memory by blocking dopamine Neuron-Mediated forgetting

    Cell 161:1656–1667.

    https://doi.org/10.1016/j.cell.2015.05.027

    • PubMed
    • Google Scholar
20. 1. Berry JA
    2. Phan A
    3. Davis RL

    (2018) Dopamine neurons mediate learning and forgetting through bidirectional modulation of a memory trace

    Cell Reports 25:651–662.

    https://doi.org/10.1016/j.celrep.2018.09.051

    • PubMed
    • Google Scholar
21. 1. Bittner KC
    2. Milstein AD
    3. Grienberger C
    4. Romani S
    5. Magee JC

    (2017) Behavioral time scale synaptic plasticity underlies CA1 place fields

    Science 357:1033–1036.

    https://doi.org/10.1126/science.aan3846

    • PubMed
    • Google Scholar
22. 1. Blum AL
    2. Li W
    3. Cressy M
    4. Dubnau J

    (2009) Short- and Long-Term Memory in Drosophila Require cAMP Signaling in Distinct Neuron Types

    Current Biology 19:1341–1350.

    https://doi.org/10.1016/j.cub.2009.07.016

    • Google Scholar
23. Preprint

    1. Bogovic JA
    2. Otsuna H
    3. Heinrich L
    4. Ito M
    5. Jeter J
    6. Meissner GW
    7. Nern A
    8. Colonell J
    9. Malkesman O
    10. Ito K
    11. Saalfeld S

    (2018) An unbiased template of the Drosophila brain and ventral nerve cord

    bioRxiv.

    https://doi.org/10.1101/376384

    • Google Scholar
24. Conference

    1. Borst A

    (2014) Neural circuits for motion vision in the fly

    Cold Spring Harbor Symposia on Quantitative Biology. pp. 131–139.

    https://doi.org/10.1101/sqb.2014.79.024695

    • Google Scholar
25. 1. Boto T
    2. Stahl A
    3. Zhang X
    4. Louis T
    5. Tomchik SM

    (2019) Independent contributions of discrete dopaminergic circuits to cellular plasticity, memory strength, and Valence in Drosophila

    Cell Reports 27:2014–2021.

    https://doi.org/10.1016/j.celrep.2019.04.069

    • PubMed
    • Google Scholar
26. 1. Bouzaiane E
    2. Trannoy S
    3. Scheunemann L
    4. Plaçais PY
    5. Preat T

    (2015) Two independent mushroom body output circuits retrieve the six discrete components of Drosophila aversive memory

    Cell Reports 11:1280–1292.

    https://doi.org/10.1016/j.celrep.2015.04.044

    • PubMed
    • Google Scholar
27. 1. Brembs B

    (2009) Mushroom bodies regulate habit formation in Drosophila

    Current Biology 19:1351–1355.

    https://doi.org/10.1016/j.cub.2009.06.014

    • PubMed
    • Google Scholar
28. 1. Burke CJ
    2. Huetteroth W
    3. Owald D
    4. Perisse E
    5. Krashes MJ
    6. Das G
    7. Gohl D
    8. Silies M
    9. Certel S
    10. Waddell S

    (2012) Layered reward signalling through octopamine and dopamine in Drosophila

    Nature 492:433–437.

    https://doi.org/10.1038/nature11614

    • PubMed
    • Google Scholar
29. 1. Busch S
    2. Selcho M
    3. Ito K
    4. Tanimoto H

    (2009) A map of octopaminergic neurons in the Drosophila brain

    The Journal of Comparative Neurology 513:643–667.

    https://doi.org/10.1002/cne.21966

    • PubMed
    • Google Scholar
30. 1. Butcher NJ
    2. Friedrich AB
    3. Lu Z
    4. Tanimoto H
    5. Meinertzhagen IA

    (2012) Different classes of input and output neurons reveal new features in microglomeruli of the adult Drosophila mushroom body Calyx

    The Journal of Comparative Neurology 520:2185–2201.

    https://doi.org/10.1002/cne.23037

    • PubMed
    • Google Scholar
31. 1. Caron SJC
    2. Ruta V
    3. Abbott LF
    4. Axel R

    (2013) Random convergence of olfactory inputs in the Drosophila mushroom body

    Nature 497:113–117.

    https://doi.org/10.1038/nature12063

    • Google Scholar
32. 1. Cassenaer S
    2. Laurent G

    (2012) Conditional modulation of spike-timing-dependent plasticity for olfactory learning

    Nature 482:47–52.

    https://doi.org/10.1038/nature10776

    • PubMed
    • Google Scholar
33. 1. Claridge-Chang A
    2. Roorda RD
    3. Vrontou E
    4. Sjulson L
    5. Li H
    6. Hirsh J
    7. Miesenböck G

    (2009) Writing memories with light-addressable reinforcement circuitry

    Cell 139:405–415.

    https://doi.org/10.1016/j.cell.2009.08.034

    • PubMed
    • Google Scholar
34. Preprint

    1. Clements J
    2. Dolafi T
    3. Umayam L
    4. Neubarth NL
    5. Berg S
    6. Scheffer LK
    7. Plaza SM

    (2020) neuPrint: Analysis Tools for EM Connectomics.

    bioRxiv.

    https://doi.org/10.1101/2020.01.16.909465

    • Google Scholar
35. 1. Cohn R
    2. Morantte I
    3. Ruta V

    (2015) Coordinated and compartmentalized neuromodulation shapes sensory processing in Drosophila

    Cell 163:1742–1755.

    https://doi.org/10.1016/j.cell.2015.11.019

    • PubMed
    • Google Scholar
36. 1. Collett M
    2. Collett TS

    (2018) How does the insect central complex use mushroom body output for steering?

    Current Biology 28:R733–R734.

    https://doi.org/10.1016/j.cub.2018.05.060

    • PubMed
    • Google Scholar
37. 1. Costa M
    2. Manton JD
    3. Ostrovsky AD
    4. Prohaska S
    5. Jefferis GS

    (2016) NBLAST: rapid, sensitive comparison of neuronal structure and construction of neuron family databases

    Neuron 91:293–311.

    https://doi.org/10.1016/j.neuron.2016.06.012

    • PubMed
    • Google Scholar
38. 1. Crittenden JR
    2. Skoulakis EM
    3. Han KA
    4. Kalderon D
    5. Davis RL

    (1998) Tripartite mushroom body architecture revealed by antigenic markers

    Learning & Memory 5:38–51.

    https://doi.org/10.1101/lm.5.1.38

    • PubMed
    • Google Scholar
39. 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
40. 1. Dag U
    2. Lei Z
    3. Le JQ
    4. Wong A
    5. Bushey D
    6. Keleman K

    (2019) Neuronal reactivation during post-learning sleep consolidates long-term memory in Drosophila

    eLife 8:e42786.

    https://doi.org/10.7554/eLife.42786

    • PubMed
    • Google Scholar
41. 1. Das G
    2. Klappenbach M
    3. Vrontou E
    4. Perisse E
    5. Clark CM
    6. Burke CJ
    7. Waddell S

    (2014) Drosophila learn opposing components of a compound food stimulus

    Current Biology 24:1723–1730.

    https://doi.org/10.1016/j.cub.2014.05.078

    • PubMed
    • Google Scholar
42. 1. Davis RL

    (2005) Olfactory memory formation in Drosophila : from molecular to systems neuroscience

    Annual Review of Neuroscience 28:275–302.

    https://doi.org/10.1146/annurev.neuro.28.061604.135651

    • PubMed
    • Google Scholar
43. 1. Dolan MJ
    2. Frechter S
    3. Bates AS
    4. Dan C
    5. Huoviala P
    6. Roberts RJ
    7. Schlegel P
    8. Dhawan S
    9. Tabano R
    10. Dionne H
    11. Christoforou C
    12. Close K
    13. Sutcliffe B
    14. Giuliani B
    15. Li F
    16. Costa M
    17. Ihrke G
    18. Meissner GW
    19. Bock DD
    20. Aso Y
    21. Rubin GM
    22. Jefferis GS

    (2019) Neurogenetic dissection of the Drosophila lateral horn reveals major outputs, diverse behavioural functions, and interactions with the mushroom body

    eLife 8:e43079.

    https://doi.org/10.7554/eLife.43079

    • PubMed
    • Google Scholar
44. Preprint

    1. Eckstein N
    2. Bates AS
    3. Du M
    4. Hartenstein V
    5. Jefferis G
    6. Funke J

    (2020) Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila

    bioRxiv.

    https://doi.org/10.1101/2020.06.12.148775

    • Google Scholar
45. 1. Ehmer B
    2. Gronenberg W

    (2002) Segregation of visual input to the mushroom bodies in the honeybee (Apis mellifera)

    The Journal of Comparative Neurology 451:362–373.

    https://doi.org/10.1002/cne.10355

    • PubMed
    • Google Scholar
46. 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
47. 1. Elkahlah NA
    2. Rogow JA
    3. Ahmed M
    4. Clowney EJ

    (2020) Presynaptic developmental plasticity allows robust sparse wiring of the Drosophila mushroom body

    eLife 9:e52278.

    https://doi.org/10.7554/eLife.52278

    • PubMed
    • Google Scholar
48. 1. Eschbach C
    2. Fushiki A
    3. Winding M
    4. Schneider-Mizell CM
    5. Shao M
    6. Arruda R
    7. Eichler K
    8. Valdes-Aleman J
    9. Ohyama T
    10. Thum AS
    11. Gerber B
    12. Fetter RD
    13. Truman JW
    14. Litwin-Kumar A
    15. Cardona A
    16. Zlatic M

    (2020a) Recurrent architecture for adaptive regulation of learning in the insect brain

    Nature Neuroscience 23:544–555.

    https://doi.org/10.1038/s41593-020-0607-9

    • PubMed
    • Google Scholar
49. Preprint

    1. Eschbach C
    2. Fushiki A
    3. Winding M
    4. Afonso B
    5. Andrade IV CBT
    6. Eichler K
    7. Gepner R
    8. Si G
    9. Valdes-Aleman J
    10. Gershow M
    11. Jefferis GS
    12. Truman JW
    13. Fetter RD
    14. Samuel A
    15. Cardona A
    16. Zlatic M

    (2020b) Circuits for integrating learnt and innate valences in the fly brain

    bioRxiv.

    https://doi.org/10.1101/2020.04.23.058339

    • Google Scholar
50. 1. Farris SM

    (2011) Are mushroom bodies cerebellum-like structures?

    Arthropod Structure & Development 40:368–379.

    https://doi.org/10.1016/j.asd.2011.02.004

    • PubMed
    • Google Scholar
51. 1. Farris SM
    2. Van Dyke JW

    (2015) Evolution and function of the insect mushroom bodies: contributions from comparative and model systems studies

    Current Opinion in Insect Science 12:19–25.

    https://doi.org/10.1016/j.cois.2015.08.006

    • Google Scholar
52. 1. Felsenberg J
    2. Barnstedt O
    3. Cognigni P
    4. Lin S
    5. Waddell S

    (2017) Re-evaluation of learned information in Drosophila

    Nature 544:240–244.

    https://doi.org/10.1038/nature21716

    • PubMed
    • Google Scholar
53. 1. Felsenberg J
    2. Jacob PF
    3. Walker T
    4. Barnstedt O
    5. Edmondson-Stait AJ
    6. Pleijzier MW
    7. Otto N
    8. Schlegel P
    9. Sharifi N
    10. Perisse E
    11. Smith CS
    12. Lauritzen JS
    13. Costa M
    14. Jefferis G
    15. Bock DD
    16. Waddell S

    (2018) Integration of parallel opposing memories underlies memory extinction

    Cell 175:709–722.

    https://doi.org/10.1016/j.cell.2018.08.021

    • PubMed
    • Google Scholar
54. 1. Fisher YE
    2. Lu J
    3. D'Alessandro I
    4. Wilson RI

    (2019) Sensorimotor experience remaps visual input to a heading-direction network

    Nature 576:121–125.

    https://doi.org/10.1038/s41586-019-1772-4

    • PubMed
    • Google Scholar
55. 1. Frank DD
    2. Jouandet GC
    3. Kearney PJ
    4. Macpherson LJ
    5. Gallio M

    (2015) Temperature representation in the Drosophila brain

    Nature 519:358–361.

    https://doi.org/10.1038/nature14284

    • PubMed
    • Google Scholar
56. 1. Frechter S
    2. Bates AS
    3. Tootoonian S
    4. Dolan MJ
    5. Manton J
    6. Jamasb AR
    7. Kohl J
    8. Bock D
    9. Jefferis G

    (2019) Functional and anatomical specificity in a higher olfactory centre

    eLife 8:e44590.

    https://doi.org/10.7554/eLife.44590

    • PubMed
    • Google Scholar
57. 1. Galili DS
    2. Dylla KV
    3. Lüdke A
    4. Friedrich AB
    5. Yamagata N
    6. Wong JY
    7. Ho CH
    8. Szyszka P
    9. Tanimoto H

    (2014) Converging circuits mediate temperature and shock aversive olfactory conditioning in Drosophila

    Current Biology 24:1712–1722.

    https://doi.org/10.1016/j.cub.2014.06.062

    • PubMed
    • Google Scholar
58. 1. Galili T

    (2015) Dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering

    Bioinformatics 31:3718–3720.

    https://doi.org/10.1093/bioinformatics/btv428

    • PubMed
    • Google Scholar
59. 1. Gallio M
    2. Ofstad TA
    3. Macpherson LJ
    4. Wang JW
    5. Zuker CS

    (2011) The coding of temperature in the Drosophila brain

    Cell 144:614–624.

    https://doi.org/10.1016/j.cell.2011.01.028

    • PubMed
    • Google Scholar
60. 1. Gambino F
    2. Pagès S
    3. Kehayas V
    4. Baptista D
    5. Tatti R
    6. Carleton A
    7. Holtmaat A

    (2014) Sensory-evoked LTP driven by dendritic plateau potentials in vivo

    Nature 515:116–119.

    https://doi.org/10.1038/nature13664

    • PubMed
    • Google Scholar
61. 1. Gao R
    2. Asano SM
    3. Upadhyayula S
    4. Pisarev I
    5. Milkie DE
    6. Liu TL
    7. Singh V
    8. Graves A
    9. Huynh GH
    10. Zhao Y
    11. Bogovic J
    12. Colonell J
    13. Ott CM
    14. Zugates C
    15. Tappan S
    16. Rodriguez A
    17. Mosaliganti KR
    18. Sheu SH
    19. Pasolli HA
    20. Pang S
    21. Xu CS
    22. Megason SG
    23. Hess H
    24. Lippincott-Schwartz J
    25. Hantman A
    26. Rubin GM
    27. Kirchhausen T
    28. Saalfeld S
    29. Aso Y
    30. Boyden ES
    31. Betzig E

    (2019) Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution

    Science 363:eaau8302.

    https://doi.org/10.1126/science.aau8302

    • PubMed
    • Google Scholar
62. 1. Gerber B
    2. Yarali A
    3. Diegelmann S
    4. Wotjak CT
    5. Pauli P
    6. Fendt M

    (2014) Pain-relief learning in flies, rats, and man: basic research and applied perspectives

    Learning & Memory 21:232–252.

    https://doi.org/10.1101/lm.032995.113

    • PubMed
    • Google Scholar
63. 1. Groschner LN
    2. Chan Wah Hak L
    3. Bogacz R
    4. DasGupta S
    5. Miesenböck G

    (2018) Dendritic integration of sensory evidence in perceptual Decision-Making

    Cell 173:894–905.

    https://doi.org/10.1016/j.cell.2018.03.075

    • PubMed
    • Google Scholar
64. 1. Gruntman E
    2. Turner GC

    (2013) Integration of the olfactory code across dendritic claws of single mushroom body neurons

    Nature Neuroscience 16:1821–1829.

    https://doi.org/10.1038/nn.3547

    • PubMed
    • Google Scholar
65. 1. Guo J
    2. Guo A

    (2005) Crossmodal interactions between olfactory and visual learning in Drosophila

    Science 309:307–310.

    https://doi.org/10.1126/science.1111280

    • PubMed
    • Google Scholar
66. 1. Hamada FN
    2. Rosenzweig M
    3. Kang K
    4. Pulver SR
    5. Ghezzi A
    6. Jegla TJ
    7. Garrity PA

    (2008) An internal thermal sensor controlling temperature preference in Drosophila

    Nature 454:217–220.

    https://doi.org/10.1038/nature07001

    • PubMed
    • Google Scholar
67. 1. Handler A
    2. Graham TGW
    3. Cohn R
    4. Morantte I
    5. Siliciano AF
    6. Zeng J
    7. Li Y
    8. Ruta V

    (2019) Distinct dopamine receptor pathways underlie the temporal sensitivity of associative learning

    Cell 178:60–75.

    https://doi.org/10.1016/j.cell.2019.05.040

    • PubMed
    • Google Scholar
68. 1. Hanesch U
    2. Fischbach K-F
    3. Heisenberg M

    (1989) Neuronal architecture of the central complex in Drosophila melanogaster

    Cell and Tissue Research 257:343–366.

    https://doi.org/10.1007/BF00261838

    • Google Scholar
69. 1. Hattori D
    2. Aso Y
    3. Swartz KJ
    4. Rubin GM
    5. Abbott LF
    6. Axel R

    (2017) Representations of novelty and familiarity in a mushroom body compartment

    Cell 169:956–969.

    https://doi.org/10.1016/j.cell.2017.04.028

    • PubMed
    • Google Scholar
70. 1. Haynes PR
    2. Christmann BL
    3. Griffith LC

    (2015) A single pair of neurons links sleep to memory consolidation in Drosophila melanogaster

    eLife 4:e03868.

    https://doi.org/10.7554/eLife.03868

    • Google Scholar
71. 1. Heinze S
    2. Homberg U

    (2009) Linking the input to the output: new sets of neurons complement the polarization vision network in the locust central complex

    Journal of Neuroscience 29:4911–4921.

    https://doi.org/10.1523/JNEUROSCI.0332-09.2009

    • PubMed
    • Google Scholar
72. 1. Heisenberg M

    (2003) Mushroom body memoir: from maps to models

    Nature Reviews Neuroscience 4:266–275.

    https://doi.org/10.1038/nrn1074

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

    (2004) Neurobiology of the fruit fly's circadian clock

    Genes, Brain and Behavior 4:65–76.

    https://doi.org/10.1111/j.1601-183X.2004.00092.x

    • Google Scholar
74. 1. Hige T
    2. Aso Y
    3. Modi MN
    4. Rubin GM
    5. Turner GC

    (2015) Heterosynaptic plasticity underlies aversive olfactory learning in Drosophila

    Neuron 88:985–998.

    https://doi.org/10.1016/j.neuron.2015.11.003

    • PubMed
    • Google Scholar
75. 1. Honegger KS
    2. Campbell RA
    3. Turner GC

    (2011) Cellular-resolution population imaging reveals robust sparse coding in the Drosophila mushroom body

    The Journal of Neuroscience 31:11772–11785.

    https://doi.org/10.1523/JNEUROSCI.1099-11.2011

    • PubMed
    • Google Scholar
76. 1. Hu H

    (2016) Reward and aversion

    Annual Review of Neuroscience 39:297–324.

    https://doi.org/10.1146/annurev-neuro-070815-014106

    • PubMed
    • Google Scholar
77. 1. Hu W
    2. Peng Y
    3. Sun J
    4. Zhang F
    5. Zhang X
    6. Wang L
    7. Li Q
    8. Zhong Y

    (2018) Fan-Shaped body neurons in the Drosophila Brain Regulate Both Innate and Conditioned Nociceptive Avoidance

    Cell Reports 24:1573–1584.

    https://doi.org/10.1016/j.celrep.2018.07.028

    • PubMed
    • Google Scholar
78. 1. Huang CC
    2. Sugino K
    3. Shima Y
    4. Guo C
    5. Bai S
    6. Mensh BD
    7. Nelson SB
    8. Hantman AW

    (2013) Convergence of pontine and proprioceptive streams onto multimodal cerebellar granule cells

    eLife 2:e00400.

    https://doi.org/10.7554/eLife.00400

    • PubMed
    • Google Scholar
79. Software

    1. Hubbard P

    (2020) The neuVid Animation Generation Codebase, version 4f12086

    Github.

    https://github.com/connectome-neuprint/neuVid
80. 1. Huerta R
    2. Nowotny T
    3. García-Sanchez M
    4. Abarbanel HD
    5. Rabinovich MI

    (2004) Learning classification in the olfactory system of insects

    Neural Computation 16:1601–1640.

    https://doi.org/10.1162/089976604774201613

    • PubMed
    • Google Scholar
81. 1. Huetteroth W
    2. Perisse E
    3. Lin S
    4. Klappenbach M
    5. Burke C
    6. Waddell S

    (2015) Sweet taste and nutrient value subdivide rewarding dopaminergic neurons in Drosophila

    Current Biology 25:751–758.

    https://doi.org/10.1016/j.cub.2015.01.036

    • PubMed
    • Google Scholar
82. Preprint

    1. Hulse BK
    2. Haberkern H
    3. Franconville R
    4. Turner-Evans DB
    5. Takemura S
    6. Wolff T
    7. Noorman M
    8. Dreher M
    9. Dan C
    10. Parekh R
    11. Hermundstad AM
    12. Rubin GM
    13. Jayaraman V

    (2020) A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

    bioRxiv.

    https://doi.org/10.1101/2020.12.08.413955

    • Google Scholar
83. Preprint

    1. Huoviala P
    2. Dolan M-J
    3. Love FM
    4. Myers P
    5. Frechter S
    6. Namiki S
    7. Pettersson L
    8. Roberts RJV
    9. Turnbull R
    10. Mitrevica Z
    11. Breads P
    12. Schlegel P
    13. Bates AS
    14. Rodrigues T
    15. Aso Y
    16. Bock D
    17. Rubin GM
    18. Stensmyr M
    19. Card G
    20. Costa M
    21. Jefferis GXE

    (2020) Neural circuit basis of aversive odour processing in Drosophila from sensory input to descending output

    bioRxiv.

    https://doi.org/10.1101/394403

    • Google Scholar
84. 1. Ichinose T
    2. Aso Y
    3. Yamagata N
    4. Abe A
    5. Rubin GM
    6. Tanimoto H

    (2015) Reward signal in a recurrent circuit drives appetitive long-term memory formation

    eLife 4:e10719.

    https://doi.org/10.7554/eLife.10719

    • PubMed
    • Google Scholar
85. 1. Isabel G
    2. Pascual A
    3. Preat T

    (2004) Exclusive consolidated memory phases in Drosophila

    Science 304:1024–1027.

    https://doi.org/10.1126/science.1094932

    • PubMed
    • Google Scholar
86. 1. Ito K
    2. Awano W
    3. Suzuki K
    4. Hiromi Y
    5. Yamamoto D

    (1997)

    The Drosophila mushroom body is a quadruple structure of clonal units each of which contains a virtually identical set of neurones and glial cells

    Development 124:761–771.

    • PubMed
    • Google Scholar
87. 1. Jacob PF
    2. Waddell S

    (2020) Spaced training forms complementary Long-Term memories of opposite Valence in Drosophila

    Neuron 106:977–991.

    https://doi.org/10.1016/j.neuron.2020.03.013

    • PubMed
    • Google Scholar
88. Software

    1. Jari Oksanen F

    (2019) vegan: Community Ecology Package, version 2.5-6

    Github.

    https://github.com/vegandevs/vegan
89. 1. Jeanne JM
    2. Fişek M
    3. Wilson RI

    (2018) The organization of projections from olfactory glomeruli onto Higher-Order neurons

    Neuron 98:1198–1213.

    https://doi.org/10.1016/j.neuron.2018.05.011

    • PubMed
    • Google Scholar
90. 1. Jenett A
    2. Rubin GM
    3. Ngo TT
    4. Shepherd D
    5. Murphy C
    6. Dionne H
    7. Pfeiffer BD
    8. Cavallaro A
    9. Hall D
    10. Jeter J
    11. Iyer N
    12. Fetter D
    13. Hausenfluck JH
    14. Peng H
    15. Trautman ET
    16. Svirskas RR
    17. Myers EW
    18. Iwinski ZR
    19. Aso Y
    20. DePasquale GM
    21. Enos A
    22. Hulamm P
    23. Lam SC
    24. Li HH
    25. Laverty TR
    26. Long F
    27. Qu L
    28. Murphy SD
    29. Rokicki K
    30. Safford T
    31. Shaw K
    32. Simpson JH
    33. Sowell A
    34. Tae S
    35. Yu Y
    36. Zugates CT

    (2012) A GAL4-driver line resource for Drosophila neurobiology

    Cell Reports 2:991–1001.

    https://doi.org/10.1016/j.celrep.2012.09.011

    • PubMed
    • Google Scholar
91. 1. Johansen J
    2. Halpern ME
    3. Johansen KM
    4. Keshishian H

    (1989) Stereotypic morphology of glutamatergic synapses on identified muscle cells of Drosophila larvae

    The Journal of Neuroscience 9:710–725.

    https://doi.org/10.1523/JNEUROSCI.09-02-00710.1989

    • PubMed
    • Google Scholar
92. 1. Jortner RA
    2. Farivar SS
    3. Laurent G

    (2007) A simple connectivity scheme for sparse coding in an olfactory system

    Journal of Neuroscience 27:1659–1669.

    https://doi.org/10.1523/JNEUROSCI.4171-06.2007

    • PubMed
    • Google Scholar
93. 1. Josselyn SA
    2. Frankland PW

    (2018) Memory allocation: mechanisms and function

    Annual Review of Neuroscience 41:389–413.

    https://doi.org/10.1146/annurev-neuro-080317-061956

    • PubMed
    • Google Scholar
94. 1. Kamhi JF
    2. Barron AB
    3. Narendra A

    (2020) Vertical lobes of the mushroom bodies are essential for View-Based navigation in australian myrmecia ants

    Current Biology 30:3432–3437.

    https://doi.org/10.1016/j.cub.2020.06.030

    • PubMed
    • Google Scholar
95. 1. Keene AC
    2. Krashes MJ
    3. Leung B
    4. Bernard JA
    5. Waddell S

    (2006) Drosophila dorsal paired medial neurons provide a general mechanism for memory consolidation

    Current Biology 16:1524–1530.

    https://doi.org/10.1016/j.cub.2006.06.022

    • PubMed
    • Google Scholar
96. 1. Keleman K
    2. Vrontou E
    3. Krüttner S
    4. Yu JY
    5. Kurtovic-Kozaric A
    6. Dickson BJ

    (2012) Dopamine neurons modulate pheromone responses in Drosophila courtship learning

    Nature 489:145–149.

    https://doi.org/10.1038/nature11345

    • PubMed
    • Google Scholar
97. 1. Kim YC
    2. Lee HG
    3. Han KA

    (2007) D1 dopamine receptor dDA1 is required in the mushroom body neurons for aversive and appetitive learning in Drosophila

    Journal of Neuroscience 27:7640–7647.

    https://doi.org/10.1523/JNEUROSCI.1167-07.2007

    • PubMed
    • Google Scholar
98. 1. Kim SS
    2. Hermundstad AM
    3. Romani S
    4. Abbott LF
    5. Jayaraman V

    (2019) Generation of stable heading representations in diverse visual scenes

    Nature 576:126–131.

    https://doi.org/10.1038/s41586-019-1767-1

    • PubMed
    • Google Scholar
99. 1. Kirkhart C
    2. Scott K

    (2015) Gustatory learning and processing in the Drosophila mushroom bodies

    The Journal of Neuroscience 35:5950–5958.

    https://doi.org/10.1523/JNEUROSCI.3930-14.2015

    • PubMed
    • Google Scholar
100. 1. Krashes MJ
     2. DasGupta S
     3. Vreede A
     4. White B
     5. Armstrong JD
     6. Waddell S

     (2009) A neural circuit mechanism integrating motivational state with memory expression in Drosophila

     Cell 139:416–427.

     https://doi.org/10.1016/j.cell.2009.08.035

     • PubMed
     • Google Scholar
101. 1. Kremer MC
     2. Jung C
     3. Batelli S
     4. Rubin GM
     5. Gaul U

     (2017) The Glia of the adult Drosophila nervous system

     Glia 65:606–638.

     https://doi.org/10.1002/glia.23115

     • PubMed
     • Google Scholar
102. 1. Lacefield CO
     2. Pnevmatikakis EA
     3. Paninski L
     4. Bruno RM

     (2019) Reinforcement learning recruits somata and apical dendrites across layers of primary sensory cortex

     Cell Reports 26:2000–2008.

     https://doi.org/10.1016/j.celrep.2019.01.093

     • PubMed
     • Google Scholar
103. 1. Larkum M

     (2013) A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex

     Trends in Neurosciences 36:141–151.

     https://doi.org/10.1016/j.tins.2012.11.006

     • PubMed
     • Google Scholar
104. 1. Laurent G

     (2002) Olfactory network dynamics and the coding of multidimensional signals

     Nature Reviews Neuroscience 3:884–895.

     https://doi.org/10.1038/nrn964

     • PubMed
     • Google Scholar
105. 1. Le Möel F
     2. Wystrach A

     (2020) Opponent processes in visual memories: a model of attraction and repulsion in navigating insects' mushroom bodies

     PLOS Computational Biology 16:e1007631.

     https://doi.org/10.1371/journal.pcbi.1007631

     • PubMed
     • Google Scholar
106. 1. Lee T
     2. Lee A
     3. Luo L

     (1999)

     Development of the Drosophila mushroom bodies: sequential generation of three distinct types of neurons from a neuroblast

     Development 126:4065–4076.

     • PubMed
     • Google Scholar
107. 1. Lee PT
     2. Lin HW
     3. Chang YH
     4. Fu TF
     5. Dubnau J
     6. Hirsh J
     7. Lee T
     8. Chiang AS

     (2011) Serotonin-mushroom body circuit modulating the formation of anesthesia-resistant memory in Drosophila

     PNAS 108:13794–13799.

     https://doi.org/10.1073/pnas.1019483108

     • PubMed
     • Google Scholar
108. 1. Lee YJ
     2. Yang CP
     3. Miyares RL
     4. Huang YF
     5. He Y
     6. Ren Q
     7. Chen HM
     8. Kawase T
     9. Ito M
     10. Otsuna H
     11. Sugino K
     12. Aso Y
     13. Ito K
     14. Lee T

     (2020) Conservation and divergence of related neuronal lineages in the Drosophila central brain

     eLife 9:e53518.

     https://doi.org/10.7554/eLife.53518

     • PubMed
     • Google Scholar
109. Book

     1. Legendre P
     2. Legendre LFJ

     (2012) Numerical Ecology

     Elsevier.

     https://doi.org/10.1016/S0304-3800(00)00291-X

     • Google Scholar
110. 1. Leiss F
     2. Groh C
     3. Butcher NJ
     4. Meinertzhagen IA
     5. Tavosanis G

     (2009) Synaptic organization in the adult Drosophila mushroom body Calyx

     The Journal of Comparative Neurology 517:808–824.

     https://doi.org/10.1002/cne.22184

     • PubMed
     • Google Scholar
111. 1. Lewis LP
     2. Siju KP
     3. Aso Y
     4. Friedrich AB
     5. Bulteel AJ
     6. Rubin GM
     7. Grunwald Kadow IC

     (2015) A higher brain circuit for immediate integration of conflicting sensory information in Drosophila

     Current Biology 25:2203–2214.

     https://doi.org/10.1016/j.cub.2015.07.015

     • PubMed
     • Google Scholar
112. Preprint

     1. Li J
     2. Mahoney BD
     3. Jacob MS
     4. Caron SJC

     (2020) Two parallel pathways convey distinct visual information to the Drosophila mushroom body

     bioRxiv.

     https://doi.org/10.1101/2020.02.07.935924

     • Google Scholar
113. 1. Lin HH
     2. Lai JS
     3. Chin AL
     4. Chen YC
     5. Chiang AS

     (2007) A map of olfactory representation in the Drosophila mushroom body

     Cell 128:1205–1217.

     https://doi.org/10.1016/j.cell.2007.03.006

     • PubMed
     • Google Scholar
114. 1. Lin CY
     2. Chuang CC
     3. Hua TE
     4. Chen CC
     5. Dickson BJ
     6. Greenspan RJ
     7. Chiang AS

     (2013) A comprehensive wiring diagram of the protocerebral bridge for visual information processing in the Drosophila brain

     Cell Reports 3:1739–1753.

     https://doi.org/10.1016/j.celrep.2013.04.022

     • PubMed
     • Google Scholar
115. 1. Lin AC
     2. Bygrave AM
     3. de Calignon A
     4. Lee T
     5. Miesenböck G

     (2014a) Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination

     Nature Neuroscience 17:559–568.

     https://doi.org/10.1038/nn.3660

     • PubMed
     • Google Scholar
116. 1. Lin S
     2. Owald D
     3. Chandra V
     4. Talbot C
     5. Huetteroth W
     6. Waddell S

     (2014b) Neural correlates of water reward in thirsty Drosophila

     Nature Neuroscience 17:1536–1542.

     https://doi.org/10.1038/nn.3827

     • PubMed
     • Google Scholar
117. 1. Litwin-Kumar A
     2. Harris KD
     3. Axel R
     4. Sompolinsky H
     5. Abbott LF

     (2017) Optimal degrees of synaptic connectivity

     Neuron 93:1153–1164.

     https://doi.org/10.1016/j.neuron.2017.01.030

     • PubMed
     • Google Scholar
118. 1. Liu L
     2. Wolf R
     3. Ernst R
     4. Heisenberg M

     (1999) Context generalization in Drosophila visual learning requires the mushroom bodies

     Nature 400:753–756.

     https://doi.org/10.1038/23456

     • PubMed
     • Google Scholar
119. 1. Liu G
     2. Seiler H
     3. Wen A
     4. Zars T
     5. Ito K
     6. Wolf R
     7. Heisenberg M
     8. Liu L

     (2006) Distinct memory traces for two visual features in the Drosophila brain

     Nature 439:551–556.

     https://doi.org/10.1038/nature04381

     • PubMed
     • Google Scholar
120. 1. Liu C
     2. Plaçais PY
     3. Yamagata N
     4. Pfeiffer BD
     5. Aso Y
     6. Friedrich AB
     7. Siwanowicz I
     8. Rubin GM
     9. Preat T
     10. Tanimoto H

     (2012) A subset of dopamine neurons signals reward for odour memory in Drosophila

     Nature 488:512–516.

     https://doi.org/10.1038/nature11304

     • PubMed
     • Google Scholar
121. 1. Liu WW
     2. Mazor O
     3. Wilson RI

     (2015) Thermosensory processing in the Drosophila brain

     Nature 519:353–357.

     https://doi.org/10.1038/nature14170

     • PubMed
     • Google Scholar
122. 1. Liu X
     2. Davis RL

     (2009) The GABAergic anterior paired lateral neuron suppresses and is suppressed by olfactory learning

     Nature Neuroscience 12:53–59.

     https://doi.org/10.1038/nn.2235

     • PubMed
     • Google Scholar
123. 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
124. 1. Mao Z
     2. Davis RL

     (2009) Eight different types of dopaminergic neurons innervate the Drosophila mushroom body neuropil: anatomical and physiological heterogeneity

     Frontiers in Neural Circuits 3:5.

     https://doi.org/10.3389/neuro.04.005.2009

     • PubMed
     • Google Scholar
125. 1. Marin EC
     2. Büld L
     3. Theiss M
     4. Sarkissian T
     5. Roberts RJV
     6. Turnbull R
     7. Tamimi IFM
     8. Pleijzier MW
     9. Laursen WJ
     10. Drummond N
     11. Schlegel P
     12. Bates AS
     13. Li F
     14. Landgraf M
     15. Costa M
     16. Bock DD
     17. Garrity PA
     18. Jefferis G

     (2020) Connectomics analysis reveals first-, Second-, and Third-Order thermosensory and hygrosensory neurons in the adult Drosophila Brain

     Current Biology 30:3167–3182.

     https://doi.org/10.1016/j.cub.2020.06.028

     • PubMed
     • Google Scholar
126. 1. Martin SJ
     2. Grimwood PD
     3. Morris RG

     (2000) Synaptic plasticity and memory: an evaluation of the hypothesis

     Annual Review of Neuroscience 23:649–711.

     https://doi.org/10.1146/annurev.neuro.23.1.649

     • PubMed
     • Google Scholar
127. 1. Masek P
     2. Worden K
     3. Aso Y
     4. Rubin GM
     5. Keene AC

     (2015) A dopamine-modulated neural circuit regulating aversive taste memory in Drosophila

     Current Biology 25:1535–1541.

     https://doi.org/10.1016/j.cub.2015.04.027

     • PubMed
     • Google Scholar
128. 1. Masek P
     2. Scott K

     (2010) Limited taste discrimination in Drosophila

     PNAS 107:14833–14838.

     https://doi.org/10.1073/pnas.1009318107

     • PubMed
     • Google Scholar
129. 1. Menegas W
     2. Akiti K
     3. Amo R
     4. Uchida N
     5. Watabe-Uchida M

     (2018) Dopamine neurons projecting to the posterior striatum reinforce avoidance of threatening stimuli

     Nature Neuroscience 21:1421–1430.

     https://doi.org/10.1038/s41593-018-0222-1

     • PubMed
     • Google Scholar
130. 1. Mizunami M
     2. Weibrecht JM
     3. Strausfeld NJ

     (1998) Mushroom bodies of the cockroach: their participation in place memory

     The Journal of Comparative Neurology 402:520–537.

     https://doi.org/10.1002/(SICI)1096-9861(19981228)402:4<520::AID-CNE6>3.0.CO;2-K

     • PubMed
     • Google Scholar
131. 1. Modi MN
     2. Shuai Y
     3. Turner GC

     (2020) The Drosophila mushroom body: from architecture to algorithm in a learning circuit

     Annual Review of Neuroscience 43:465–484.

     https://doi.org/10.1146/annurev-neuro-080317-0621333

     • PubMed
     • Google Scholar
132. 1. Mu L
     2. Ito K
     3. Bacon JP
     4. Strausfeld NJ

     (2012) Optic glomeruli and their inputs in Drosophila share an organizational ground pattern with the antennal lobes

     Journal of Neuroscience 32:6061–6071.

     https://doi.org/10.1523/JNEUROSCI.0221-12.2012

     • PubMed
     • Google Scholar
133. 1. Namiki S
     2. Dickinson MH
     3. Wong AM
     4. Korff W
     5. Card GM

     (2018) The functional organization of descending sensory-motor pathways in Drosophila

     eLife 7:e34372.

     https://doi.org/10.7554/eLife.34272

     • PubMed
     • Google Scholar
134. 1. Namiki S
     2. Kanzaki R

     (2016) Comparative neuroanatomy of the lateral accessory lobe in the insect brain

     Frontiers in Physiology 7:244.

     https://doi.org/10.3389/fphys.2016.00244

     • PubMed
     • Google Scholar
135. 1. Nern A
     2. Pfeiffer BD
     3. Rubin GM

     (2015) Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system

     PNAS 112:E2967–E2976.

     https://doi.org/10.1073/pnas.1506763112

     • PubMed
     • Google Scholar
136. 1. Neuser K
     2. Triphan T
     3. Mronz M
     4. Poeck B
     5. Strauss R

     (2008) Analysis of a spatial orientation memory in Drosophila

     Nature 453:1244–1247.

     https://doi.org/10.1038/nature07003

     • PubMed
     • Google Scholar
137. 1. O'Donnell S
     2. Donlan NA
     3. Jones TA

     (2004) Mushroom body structural change is associated with division of labor in eusocial wasp workers (Polybia aequatorialis, hymenoptera: vespidae)

     Neuroscience Letters 356:159–162.

     https://doi.org/10.1016/j.neulet.2003.11.053

     • PubMed
     • Google Scholar
138. Preprint

     1. Otsuna H
     2. Ito M
     3. Kawase T

     (2018) Color depth MIP mask search: a new tool to expedite Split-GAL4 creation

     bioRxiv.

     https://doi.org/10.1101/318006

     • Google Scholar
139. 1. Otsuna H
     2. Ito K

     (2006) Systematic analysis of the visual projection neurons of Drosophila melanogaster. I. Lobula-specific pathways

     The Journal of Comparative Neurology 497:928–958.

     https://doi.org/10.1002/cne.21015

     • PubMed
     • Google Scholar
140. 1. Otto N
     2. Pleijzier MW
     3. Morgan IC
     4. Edmondson-Stait AJ
     5. Heinz KJ
     6. Stark I
     7. Dempsey G
     8. Ito M
     9. Kapoor I
     10. Hsu J
     11. Schlegel PM
     12. Bates AS
     13. Feng L
     14. Costa M
     15. Ito K
     16. Bock DD
     17. Rubin GM
     18. Jefferis G
     19. Waddell S

     (2020) Input connectivity reveals additional heterogeneity of dopaminergic reinforcement in Drosophila

     Current Biology 30:3200–3211.

     https://doi.org/10.1016/j.cub.2020.05.077

     • PubMed
     • Google Scholar
141. 1. Owald D
     2. Felsenberg J
     3. Talbot CB
     4. Das G
     5. Perisse E
     6. Huetteroth W
     7. Waddell S

     (2015) Activity of defined mushroom body output neurons underlies learned olfactory behavior in Drosophila

     Neuron 86:417–427.

     https://doi.org/10.1016/j.neuron.2015.03.025

     • PubMed
     • Google Scholar
142. 1. Pai TP
     2. Chen CC
     3. Lin HH
     4. Chin AL
     5. Lai JS
     6. Lee PT
     7. Tully T
     8. Chiang AS

     (2013) Drosophila ORB protein in two mushroom body output neurons is necessary for long-term memory formation

     PNAS 110:7898–7903.

     https://doi.org/10.1073/pnas.1216336110

     • PubMed
     • Google Scholar
143. 1. Papadopoulou M
     2. Cassenaer S
     3. Nowotny T
     4. Laurent G

     (2011) Normalization for sparse encoding of odors by a wide-field interneuron

     Science 332:721–725.

     https://doi.org/10.1126/science.1201835

     • PubMed
     • Google Scholar
144. 1. Park S
     2. Sonn JY
     3. Oh Y
     4. Lim C
     5. Choe J

     (2014) SIFamide and SIFamide receptor defines a novel neuropeptide signaling to promote sleep in Drosophila

     Molecules and Cells 37:295–301.

     https://doi.org/10.14348/molcells.2014.2371

     • PubMed
     • Google Scholar
145. 1. Paulk AC
     2. Gronenberg W

     (2008) Higher order visual input to the mushroom bodies in the bee, bombus impatiens

     Arthropod Structure & Development 37:443–458.

     https://doi.org/10.1016/j.asd.2008.03.002

     • PubMed
     • Google Scholar
146. Book

     1. Pavlov IP
     2. Thompson WH

     (1902)

     The Work of the Digestive Glands

     W. H. Thompson.

     • Google Scholar
147. 1. Pavlowsky A
     2. Schor J
     3. Plaçais PY
     4. Preat T

     (2018) A GABAergic feedback shapes dopaminergic input on the Drosophila Mushroom Body to Promote Appetitive Long-Term Memory

     Current Biology 28:1783–1793.

     https://doi.org/10.1016/j.cub.2018.04.040

     • PubMed
     • Google Scholar
148. 1. Perez-Orive J
     2. Mazor O
     3. Turner GC
     4. Cassenaer S
     5. Wilson RI
     6. Laurent G

     (2002) Oscillations and sparsening of odor representations in the mushroom body

     Science 297:359–365.

     https://doi.org/10.1126/science.1070502

     • PubMed
     • Google Scholar
149. 1. Perisse E
     2. Burke C
     3. Huetteroth W
     4. Waddell S

     (2013) Shocking revelations and saccharin sweetness in the study of Drosophila olfactory memory

     Current Biology 23:R752–R763.

     https://doi.org/10.1016/j.cub.2013.07.060

     • PubMed
     • Google Scholar
150. 1. Perisse E
     2. Owald D
     3. Barnstedt O
     4. Talbot CB
     5. Huetteroth W
     6. Waddell S

     (2016) Aversive learning and appetitive motivation toggle Feed-Forward inhibition in the Drosophila Mushroom Body

     Neuron 90:1086–1099.

     https://doi.org/10.1016/j.neuron.2016.04.034

     • PubMed
     • Google Scholar
151. 1. Pitman JL
     2. Huetteroth W
     3. Burke CJ
     4. Krashes MJ
     5. Lai SL
     6. Lee T
     7. Waddell S

     (2011) A pair of inhibitory neurons are required to sustain labile memory in the Drosophila mushroom body

     Current Biology 21:855–861.

     https://doi.org/10.1016/j.cub.2011.03.069

     • PubMed
     • Google Scholar
152. 1. Plaçais PY
     2. Trannoy S
     3. Friedrich AB
     4. Tanimoto H
     5. Preat T

     (2013) Two pairs of mushroom body efferent neurons are required for appetitive long-term memory retrieval in Drosophila

     Cell Reports 5:769–780.

     https://doi.org/10.1016/j.celrep.2013.09.032

     • PubMed
     • Google Scholar
153. 1. Poulin JF
     2. Gaertner Z
     3. Moreno-Ramos OA
     4. Awatramani R

     (2020) Classification of midbrain dopamine neurons using Single-Cell gene expression profiling approaches

     Trends in Neurosciences 43:155–169.

     https://doi.org/10.1016/j.tins.2020.01.004

     • PubMed
     • Google Scholar
154. 1. Qin H
     2. Cressy M
     3. Li W
     4. Coravos JS
     5. Izzi SA
     6. Dubnau J

     (2012) Gamma neurons mediate dopaminergic input during aversive olfactory memory formation in Drosophila

     Current Biology 22:608–614.

     https://doi.org/10.1016/j.cub.2012.02.014

     • PubMed
     • Google Scholar
155. 1. Ravenscroft TA
     2. Janssens J
     3. Lee PT
     4. Tepe B
     5. Marcogliese PC
     6. Makhzami S
     7. Holmes TC
     8. Aerts S
     9. Bellen HJ

     (2020) Drosophila Voltage-Gated sodium channels are only expressed in active neurons and are localized to distal axonal initial Segment-like domains

     The Journal of Neuroscience 40:7999–8024.

     https://doi.org/10.1523/JNEUROSCI.0142-20.2020

     • PubMed
     • Google Scholar
156. Preprint

     1. Rayshubskiy A
     2. Holtz SL
     3. D’Alessandro I
     4. Aa L
     5. Vanderbeck QX
     6. Haber IS
     7. Gibb PW
     8. Wilson RI

     (2020) Neural circuit mechanisms for steering control in walking Drosophila

     bioRxiv.

     https://doi.org/10.1101/2020.04.04.024703

     • Google Scholar
157. Report

     1. Ropinski T
     2. Steinicke F
     3. Hinrichs K

     (2006)

     Visually Supporting Depth Perception in Angiography Imaging

     Smart Graphics.

     • Google Scholar
158. 1. Saumweber T
     2. Rohwedder A
     3. Schleyer M
     4. Eichler K
     5. Chen YC
     6. Aso Y
     7. Cardona A
     8. Eschbach C
     9. Kobler O
     10. Voigt A
     11. Durairaja A
     12. Mancini N
     13. Zlatic M
     14. Truman JW
     15. Thum AS
     16. Gerber B

     (2018) Functional architecture of reward learning in mushroom body extrinsic neurons of larval Drosophila

     Nature Communications 9:1104.

     https://doi.org/10.1038/s41467-018-03130-1

     • PubMed
     • Google Scholar
159. 1. Sayin S
     2. De Backer JF
     3. Siju KP
     4. Wosniack ME
     5. Lewis LP
     6. Frisch LM
     7. Gansen B
     8. Schlegel P
     9. Edmondson-Stait A
     10. Sharifi N
     11. Fisher CB
     12. Calle-Schuler SA
     13. Lauritzen JS
     14. Bock DD
     15. Costa M
     16. Jefferis G
     17. Gjorgjieva J
     18. Grunwald Kadow IC

     (2019) A neural circuit arbitrates between persistence and withdrawal in hungry Drosophila

     Neuron 104:544–558.

     https://doi.org/10.1016/j.neuron.2019.07.028

     • PubMed
     • Google Scholar
160. 1. Scaplen KM
     2. Talay M
     3. Nunez KM
     4. Salamon S
     5. Waterman AG
     6. Gang S
     7. Song SL
     8. Barnea G
     9. Kaun KR

     (2020) Circuits that encode and guide alcohol-associated preference

     eLife 9:e48730.

     https://doi.org/10.7554/eLife.48730

     • PubMed
     • Google Scholar
161. 1. Scheffer LK
     2. Xu CS
     3. Januszewski M
     4. Lu Z
     5. Takemura SY
     6. Hayworth KJ
     7. Huang GB
     8. Shinomiya K
     9. Maitlin-Shepard J
     10. Berg S
     11. Clements J
     12. Hubbard PM
     13. Katz WT
     14. Umayam L
     15. Zhao T
     16. Ackerman D
     17. Blakely T
     18. Bogovic J
     19. Dolafi T
     20. Kainmueller D
     21. Kawase T
     22. Khairy KA
     23. Leavitt L
     24. Li PH
     25. Lindsey L
     26. Neubarth N
     27. Olbris DJ
     28. Otsuna H
     29. Trautman ET
     30. Ito M
     31. Bates AS
     32. Goldammer J
     33. Wolff T
     34. Svirskas R
     35. Schlegel P
     36. Neace E
     37. Knecht CJ
     38. Alvarado CX
     39. Bailey DA
     40. Ballinger S
     41. Borycz JA
     42. Canino BS
     43. Cheatham N
     44. Cook M
     45. Dreher M
     46. Duclos O
     47. Eubanks B
     48. Fairbanks K
     49. Finley S
     50. Forknall N
     51. Francis A
     52. Hopkins GP
     53. Joyce EM
     54. Kim S
     55. Kirk NA
     56. Kovalyak J
     57. Lauchie SA
     58. Lohff A
     59. Maldonado C
     60. Manley EA
     61. McLin S
     62. Mooney C
     63. Ndama M
     64. Ogundeyi O
     65. Okeoma N
     66. Ordish C
     67. Padilla N
     68. Patrick CM
     69. Paterson T
     70. Phillips EE
     71. Phillips EM
     72. Rampally N
     73. Ribeiro C
     74. Robertson MK
     75. Rymer JT
     76. Ryan SM
     77. Sammons M
     78. Scott AK
     79. Scott AL
     80. Shinomiya A
     81. Smith C
     82. Smith K
     83. Smith NL
     84. Sobeski MA
     85. Suleiman A
     86. Swift J
     87. Takemura S
     88. Talebi I
     89. Tarnogorska D
     90. Tenshaw E
     91. Tokhi T
     92. Walsh JJ
     93. Yang T
     94. Horne JA
     95. Li F
     96. Parekh R
     97. Rivlin PK
     98. Jayaraman V
     99. Costa M
     100. Jefferis GS
     101. Ito K
     102. Saalfeld S
     103. George R
     104. Meinertzhagen IA
     105. Rubin GM
     106. Hess HF
     107. Jain V
     108. Plaza SM

     (2020) A connectome and analysis of the adult Drosophila central brain

     eLife 9:e57443.

     https://doi.org/10.7554/eLife.57443

     • PubMed
     • Google Scholar
162. Software

     1. Schlegel P

     (2018) Python 3 library for Neuron Analysis and Visualization, version 7c9e687

     Github.

     https://github.com/schlegelp/navis
163. Preprint

     1. Schlegel P
     2. Bates AS
     3. Stürner T
     4. Jagannathan SR
     5. Drummond N
     6. Hsu J
     7. Capdevila LS
     8. Javier A
     9. Marin EC
     10. Barth-Maron A
     11. Tamimi IFM
     12. Li F
     13. Rubin GM
     14. Plaza SM
     15. Costa M
     16. Jefferis G

     (2020) Information flow, cell types and stereotypy in a full olfactory connectome

     bioRxiv.

     https://doi.org/10.1101/2020.12.15.401257

     • Google Scholar
164. 1. Schleyer M
     2. Weiglein A
     3. Thoener J
     4. Strauch M
     5. Hartenstein V
     6. Kantar Weigelt M
     7. Schuller S
     8. Saumweber T
     9. Eichler K
     10. Rohwedder A
     11. Merhof D
     12. Zlatic M
     13. Thum AS
     14. Gerber B

     (2020) Identification of dopaminergic neurons that can both establish associative memory and acutely terminate its behavioral expression

     The Journal of Neuroscience 40:5990–6006.

     https://doi.org/10.1523/JNEUROSCI.0290-20.2020

     • PubMed
     • Google Scholar
165. 1. Schwaerzel M
     2. Monastirioti M
     3. Scholz H
     4. Friggi-Grelin F
     5. Birman S
     6. Heisenberg M

     (2003) Dopamine and octopamine differentiate between aversive and appetitive olfactory memories in Drosophila

     The Journal of Neuroscience 23:10495–10502.

     https://doi.org/10.1523/JNEUROSCI.23-33-10495.2003

     • PubMed
     • Google Scholar
166. 1. Séjourné J
     2. Plaçais PY
     3. Aso Y
     4. Siwanowicz I
     5. Trannoy S
     6. Thoma V
     7. Tedjakumala SR
     8. Rubin GM
     9. Tchénio P
     10. Ito K
     11. Isabel G
     12. Tanimoto H
     13. Preat T

     (2011) Mushroom body efferent neurons responsible for aversive olfactory memory retrieval in Drosophila

     Nature Neuroscience 14:903–910.

     https://doi.org/10.1038/nn.2846

     • PubMed
     • Google Scholar
167. 1. Senapati B
     2. Tsao CH
     3. Juan YA
     4. Chiu TH
     5. Wu CL
     6. Waddell S
     7. Lin S

     (2019) A neural mechanism for deprivation state-specific expression of relevant memories in Drosophila

     Nature Neuroscience 22:2029–2039.

     https://doi.org/10.1038/s41593-019-0515-z

     • PubMed
     • Google Scholar
168. 1. Shafer OT
     2. Helfrich-Förster C
     3. Renn SC
     4. Taghert PH

     (2006) Reevaluation of Drosophila melanogaster's neuronal circadian pacemakers reveals new neuronal classes

     The Journal of Comparative Neurology 498:180–193.

     https://doi.org/10.1002/cne.21021

     • PubMed
     • Google Scholar
169. 1. Shi J
     2. Malik J

     (2000) Normalized cuts and image segmentation

     IEEE Transactions on Pattern Analysis and Machine Intelligence 22.8:888–905.

     https://doi.org/10.1109/34.868688

     • Google Scholar
170. 1. Shomrat T
     2. Graindorge N
     3. Bellanger C
     4. Fiorito G
     5. Loewenstein Y
     6. Hochner B

     (2011) Alternative sites of synaptic plasticity in two homologous "fan-out fan-in" learning and memory networks

     Current Biology 21:1773–1782.

     https://doi.org/10.1016/j.cub.2011.09.011

     • PubMed
     • Google Scholar
171. 1. Shyu WH
     2. Chiu TH
     3. Chiang MH
     4. Cheng YC
     5. Tsai YL
     6. Fu TF
     7. Wu T
     8. Wu CL

     (2017) Neural circuits for long-term water-reward memory processing in thirsty Drosophila

     Nature Communications 8:15230.

     https://doi.org/10.1038/ncomms15230

     • PubMed
     • Google Scholar
172. 1. Siju KP
     2. Štih V
     3. Aimon S
     4. Gjorgjieva J
     5. Portugues R
     6. Grunwald Kadow IC

     (2020) Valence and State-Dependent population coding in dopaminergic neurons in the fly mushroom body

     Current Biology 30:2104–2115.

     https://doi.org/10.1016/j.cub.2020.04.037

     • PubMed
     • Google Scholar
173. 1. Sitaraman D
     2. Aso Y
     3. Jin X
     4. Chen N
     5. Felix M
     6. Rubin GM
     7. Nitabach MN

     (2015a) Propagation of homeostatic sleep signals by segregated synaptic microcircuits of the Drosophila Mushroom Body

     Current Biology 25:2915–2927.

     https://doi.org/10.1016/j.cub.2015.09.017

     • PubMed
     • Google Scholar
174. 1. Sitaraman D
     2. Aso Y
     3. Rubin GM
     4. Nitabach MN

     (2015b) Control of sleep by dopaminergic inputs to the Drosophila Mushroom Body

     Frontiers in Neural Circuits 9:73.

     https://doi.org/10.3389/fncir.2015.00073

     • PubMed
     • Google Scholar
175. 1. Sivinski J

     (1989) Mushroom body development in nymphalid butterflies: a correlate of learning?

     Journal of Insect Behavior 2:277–283.

     https://doi.org/10.1007/BF01053299

     • Google Scholar
176. 1. Stevens CF

     (2015) What the fly's nose tells the fly's brain

     PNAS 112:9460–9465.

     https://doi.org/10.1073/pnas.1510103112

     • PubMed
     • Google Scholar
177. 1. Stocker RF
     2. Lienhard MC
     3. Borst A
     4. Fischbach KF

     (1990) Neuronal architecture of the antennal lobe in Drosophila melanogaster

     Cell and Tissue Research 262:9–34.

     https://doi.org/10.1007/BF00327741

     • PubMed
     • Google Scholar
178. 1. Strausfeld NJ
     2. Sinakevitch I
     3. Vilinsky I

     (2003) The mushroom bodies of Drosophila melanogaster : an immunocytological and golgi study of kenyon cell organization in the calyces and lobes

     Microscopy Research and Technique 62:151–169.

     https://doi.org/10.1002/jemt.10368

     • PubMed
     • Google Scholar
179. 1. Sun X
     2. Yue S
     3. Mangan M

     (2020) A decentralised neural model explaining optimal integration of navigational strategies in insects

     eLife 9:e54026.

     https://doi.org/10.7554/eLife.54026

     • PubMed
     • Google Scholar
180. 1. Tabone CJ
     2. de Belle JS

     (2011) Second-order conditioning in Drosophila

     Learning & Memory 18:250–253.

     https://doi.org/10.1101/lm.2035411

     • PubMed
     • Google Scholar
181. 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
182. 1. Takemura SY
     2. Aso Y
     3. Hige T
     4. Wong A
     5. Lu Z
     6. Xu CS
     7. Rivlin PK
     8. Hess H
     9. Zhao T
     10. Parag T
     11. Berg S
     12. Huang G
     13. Katz W
     14. Olbris DJ
     15. Plaza S
     16. Umayam L
     17. Aniceto R
     18. Chang LA
     19. Lauchie S
     20. Ogundeyi O
     21. Ordish C
     22. Shinomiya A
     23. Sigmund C
     24. Takemura S
     25. Tran J
     26. Turner GC
     27. Rubin GM
     28. Scheffer LK

     (2017) A connectome of a learning and memory center in the adult Drosophila brain

     eLife 6:e26975.

     https://doi.org/10.7554/eLife.26975

     • PubMed
     • Google Scholar
183. 1. Tanaka NK
     2. Tanimoto H
     3. Ito K

     (2008) Neuronal assemblies of the Drosophila mushroom body

     The Journal of Comparative Neurology 508:711–755.

     https://doi.org/10.1002/cne.21692

     • PubMed
     • Google Scholar
184. 1. Tang S
     2. Guo A

     (2001) Choice behavior of Drosophila facing contradictory visual cues

     Science 294:1543–1547.

     https://doi.org/10.1126/science.1058237

     • PubMed
     • Google Scholar
185. 1. Tanimoto H
     2. Heisenberg M
     3. Gerber B

     (2004) Experimental psychology: event timing turns punishment to reward

     Nature 430:983.

     https://doi.org/10.1038/430983a

     • PubMed
     • Google Scholar
186. 1. Tomchik SM

     (2013) Dopaminergic neurons encode a distributed, asymmetric representation of temperature in Drosophila

     Journal of Neuroscience 33:2166–2176.

     https://doi.org/10.1523/JNEUROSCI.3933-12.2013

     • PubMed
     • Google Scholar
187. 1. Trannoy S
     2. Redt-Clouet C
     3. Dura JM
     4. Preat T

     (2011) Parallel processing of appetitive short- and long-term memories in Drosophila

     Current Biology 21:1647–1653.

     https://doi.org/10.1016/j.cub.2011.08.032

     • PubMed
     • Google Scholar
188. 1. Tsao CH
     2. Chen CC
     3. Lin CH
     4. Yang HY
     5. Lin S

     (2018) Drosophila mushroom bodies integrate hunger and satiety signals to control innate food-seeking behavior

     eLife 7:e35264.

     https://doi.org/10.7554/eLife.35264

     • PubMed
     • Google Scholar
189. 1. Ueoka Y
     2. Hiroi M
     3. Abe T
     4. Tabata T

     (2017) Suppression of a single pair of mushroom body output neurons in Drosophila triggers aversive associations

     FEBS Open Bio 7:562–576.

     https://doi.org/10.1002/2211-5463.12203

     • PubMed
     • Google Scholar
190. 1. Verleyen P
     2. Huybrechts J
     3. Baggerman G
     4. Van Lommel A
     5. De Loof A
     6. Schoofs L

     (2004) SIFamide is a highly conserved neuropeptide: a comparative study in different insect species

     Biochemical and Biophysical Research Communications 320:334–341.

     https://doi.org/10.1016/j.bbrc.2004.05.173

     • PubMed
     • Google Scholar
191. 1. Vogt K
     2. Schnaitmann C
     3. Dylla KV
     4. Knapek S
     5. Aso Y
     6. Rubin GM
     7. Tanimoto H

     (2014) Shared mushroom body circuits underlie visual and olfactory memories in Drosophila

     eLife 3:e02395.

     https://doi.org/10.7554/eLife.02395

     • PubMed
     • Google Scholar
192. 1. Vogt K
     2. Aso Y
     3. Hige T
     4. Knapek S
     5. Ichinose T
     6. Friedrich AB
     7. Turner GC
     8. Rubin GM
     9. Tanimoto H

     (2016) Direct neural pathways convey distinct visual information to Drosophila mushroom bodies

     eLife 5:e14009.

     https://doi.org/10.7554/eLife.14009

     • PubMed
     • Google Scholar
193. 1. Waddell S
     2. Armstrong JD
     3. Kitamoto T
     4. Kaiser K
     5. Quinn WG

     (2000) The amnesiac gene product is expressed in two neurons in the Drosophila brain that are critical for memory

     Cell 103:805–813.

     https://doi.org/10.1016/S0092-8674(00)00183-5

     • PubMed
     • Google Scholar
194. 1. Watabe-Uchida M
     2. Eshel N
     3. Uchida N

     (2017) Neural circuitry of reward prediction error

     Annual Review of Neuroscience 40:373–394.

     https://doi.org/10.1146/annurev-neuro-072116-031109

     • PubMed
     • Google Scholar
195. Conference

     1. Watabe-Uchida M
     2. Uchida N

     (2018) Multiple dopamine systems: weal and woe of dopamine

     Cold Spring Harbor Symposia on Quantitative Biology. pp. 83–95.

     https://doi.org/10.1101/sqb.2018.83.037648

     • Google Scholar
196. 1. Widmer YF
     2. Fritsch C
     3. Jungo MM
     4. Almeida S
     5. Egger B
     6. Sprecher SG

     (2018) Multiple neurons encode CrebB dependent appetitive long-term memory in the mushroom body circuit

     eLife 7:e39196.

     https://doi.org/10.7554/eLife.39196

     • PubMed
     • Google Scholar
197. 1. Wolff T
     2. Iyer NA
     3. Rubin GM

     (2015) Neuroarchitecture and neuroanatomy of the Drosophila central complex: a GAL4-based dissection of protocerebral bridge neurons and circuits

     Journal of Comparative Neurology 523:997–1037.

     https://doi.org/10.1002/cne.23705

     • PubMed
     • Google Scholar
198. 1. Wu CL
     2. Shih MF
     3. Lai JS
     4. Yang HT
     5. Turner GC
     6. Chen L
     7. Chiang AS

     (2011) Heterotypic gap junctions between two neurons in the Drosophila brain are critical for memory

     Current Biology : CB 21:848–854.

     https://doi.org/10.1016/j.cub.2011.02.041

     • PubMed
     • Google Scholar
199. 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
200. 1. Yadlapalli S
     2. Jiang C
     3. Bahle A
     4. Reddy P
     5. Meyhofer E
     6. Shafer OT

     (2018) Circadian clock neurons constantly monitor environmental temperature to set sleep timing

     Nature 555:98–102.

     https://doi.org/10.1038/nature25740

     • PubMed
     • Google Scholar
201. 1. Yagi R
     2. Mabuchi Y
     3. Mizunami M
     4. Tanaka NK

     (2016) Convergence of multimodal sensory pathways to the mushroom body Calyx in Drosophila melanogaster

     Scientific Reports 6:29481.

     https://doi.org/10.1038/srep29481

     • PubMed
     • Google Scholar
202. 1. Yamagata N
     2. Ichinose T
     3. Aso Y
     4. Plaçais PY
     5. Friedrich AB
     6. Sima RJ
     7. Preat T
     8. Rubin GM
     9. Tanimoto H

     (2015) Distinct dopamine neurons mediate reward signals for short- and long-term memories

     PNAS 112:578–583.

     https://doi.org/10.1073/pnas.1421930112

     • PubMed
     • Google Scholar
203. 1. Yamagata N
     2. Hiroi M
     3. Kondo S
     4. Abe A
     5. Tanimoto H

     (2016) Suppression of dopamine neurons mediates reward

     PLOS Biology 14:e1002586.

     https://doi.org/10.1371/journal.pbio.1002586

     • PubMed
     • Google Scholar
204. 1. Yasuyama K
     2. Meinertzhagen IA
     3. Schürmann FW

     (2002) Synaptic organization of the mushroom body Calyx in Drosophila melanogaster

     Journal of Comparative Neurology 445:211–226.

     https://doi.org/10.1002/cne.10155

     • PubMed
     • Google Scholar
205. 1. Yu D
     2. Keene AC
     3. Srivatsan A
     4. Waddell S
     5. Davis RL

     (2005) Drosophila DPM neurons form a delayed and branch-specific memory trace after olfactory classical conditioning

     Cell 123:945–957.

     https://doi.org/10.1016/j.cell.2005.09.037

     • PubMed
     • Google Scholar
206. 1. Yu HH
     2. Kao CF
     3. He Y
     4. Ding P
     5. Kao JC
     6. Lee T

     (2010) A complete developmental sequence of a Drosophila neuronal lineage as revealed by twin-spot MARCM

     PLOS Biology 8:e1000461.

     https://doi.org/10.1371/journal.pbio.1000461

     • PubMed
     • Google Scholar
207. 1. Zars T
     2. Fischer M
     3. Schulz R
     4. Heisenberg M

     (2000) Localization of a short-term memory in Drosophila

     Science 288:672–675.

     https://doi.org/10.1126/science.288.5466.672

     • PubMed
     • Google Scholar
208. 1. Zhang K
     2. Guo JZ
     3. Peng Y
     4. Xi W
     5. Guo A

     (2007) Dopamine-mushroom body circuit regulates saliency-based decision-making in Drosophila

     Science 316:1901–1904.

     https://doi.org/10.1126/science.1137357

     • PubMed
     • Google Scholar
209. 1. Zhao T
     2. Olbris DJ
     3. Yu Y
     4. Plaza SM

     (2018a) NeuTu: software for collaborative, Large-Scale, Segmentation-Based connectome reconstruction

     Frontiers in Neural Circuits 12:101.

     https://doi.org/10.3389/fncir.2018.00101

     • PubMed
     • Google Scholar
210. 1. Zhao X
     2. Lenek D
     3. Dag U
     4. Dickson BJ
     5. Keleman K

     (2018b) Persistent activity in a recurrent circuit underlies courtship memory in Drosophila

     eLife 7:e31425.

     https://doi.org/10.7554/eLife.31425

     • PubMed
     • Google Scholar
211. 1. Zheng Z
     2. Lauritzen JS
     3. Perlman E
     4. Robinson CG
     5. Nichols M
     6. Milkie D
     7. Torrens O
     8. Price J
     9. Fisher CB
     10. Sharifi N
     11. Calle-Schuler SA
     12. Kmecova L
     13. Ali IJ
     14. Karsh B
     15. Trautman ET
     16. Bogovic JA
     17. Hanslovsky P
     18. Jefferis G
     19. Kazhdan M
     20. Khairy K
     21. Saalfeld S
     22. Fetter RD
     23. Bock DD

     (2018) A complete electron microscopy volume of the brain of adult Drosophila melanogaster

     Cell 174:730–743.

     https://doi.org/10.1016/j.cell.2018.06.019

     • PubMed
     • Google Scholar
212. Preprint

     1. Zheng Z
     2. Li F
     3. Fisher C
     4. Ali IJ
     5. Sharifi N
     6. Calle-Schuler S
     7. Hsu J
     8. Masoodpanah N
     9. Kmecova L
     10. Kazimiers T
     11. Perlman E
     12. Nichols M
     13. Ph L
     14. Jain V
     15. Bock DD

     (2020) Structured sampling of olfactory input by the fly mushroom body

     bioRxiv.

     https://doi.org/10.1101/2020.04.17.047167

     • Google Scholar
213. 1. Zhu S
     2. Chiang AS
     3. Lee T

     (2003) Development of the Drosophila mushroom bodies: elaboration, remodeling and spatial organization of dendrites in the Calyx

     Development 130:2603–2610.

     https://doi.org/10.1242/dev.00466

     • PubMed
     • Google Scholar