One of the key goals of neuroscience is to understand how specific circuits of brain cells enable animals to respond optimally to the constantly changing world around them. Such processes are more easily studied in simpler brains, and the fruit fly—with its small size, short life cycle, and well-developed genetic toolkit—is widely used to study the genes and circuits that underlie learning and behavior.

Fruit flies can learn to approach odors that have previously been paired with food, and also to avoid any odors that have been paired with an electric shock, and a part of the brain called the mushroom body has a central role in this process. When odorant molecules bind to receptors on the fly's antennae, they activate neurons in the antennal lobe of the brain, which in turn activate cells called Kenyon cells within the mushroom body. The Kenyon cells then activate output neurons that convey signals to other parts of the brain.

It is known that relatively few Kenyon cells are activated by any given odor. Moreover, it seems that a given odor activates different sets of Kenyon cells in different flies. Because the association between an odor and the Kenyon cells it activates is unique to each fly, each fly needs to learn through its own experiences what a particular pattern of Kenyon cell activation means.

Aso et al. have now applied sophisticated molecular genetic and anatomical techniques to thousands of different transgenic flies to identify the neurons of the mushroom body. The resulting map reveals that the mushroom body contains roughly 2200 neurons, including seven types of Kenyon cells and 21 types of output cells, as well as 20 types of neurons that use the neurotransmitter dopamine. Moreover, this map provides insights into the circuits that support odor-based learning. It reveals, for example, that the mushroom body can be divided into 15 anatomical compartments that are each defined by the presence of a specific set of output and dopaminergic neuron cell types. Since the dopaminergic neurons help to shape a fly's response to odors on the basis of previous experience, this organization suggests that these compartments may be semi-autonomous information processing units.

In contrast to the rest of the insect brain, the mushroom body has a flexible organization that is similar to that of the mammalian brain. Elucidating the circuits that support associative learning in fruit flies should therefore make it easier to identify the equivalent mechanisms in vertebrate animals.

Neural representations of the sensory world give rise to appropriate innate or learned behavioral responses. Innate behaviors are observed in naïve animals without prior learning or experience, suggesting that they are mediated by genetically determined neural circuits. Responses to most sensory stimuli, however, are not innate but experience-dependent, allowing an organism to respond appropriately in a variable and uncertain world. Thus, most sensory cues acquire behavioral relevance through learning. In Drosophila melanogaster, a number of different forms of learning have been observed in response to sensory stimuli (Siegel and Hall, 1979; Liu et al., 1999, 2006; Masek and Scott, 2010; Schnaitmann et al., 2010; Ofstad et al., 2011; Vogt et al., 2014). In associative olfactory learning, exposure to an odor (conditioned stimulus, CS) in association with an unconditioned stimulus (US) results in appetitive or aversive memory (Quinn et al., 1974; Tempel et al., 1983; Tully and Quinn, 1985). Olfactory memory formation and retrieval in insects require the mushroom body (MB) (Heisenberg et al., 1985; de Belle and Heisenberg, 1994, Dubnau et al., 2001; McGuire et al., 2001), an associative center in the protocerebrum (Figure 1 and Video 1).

Figure 1

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Olfactory perception in the fly is initiated by the binding of an odorant to an ensemble of olfactory sensory neurons in the antennae, resulting in the activation of a distinct and topographically fixed combination of glomeruli in the antennal lobe (Figure 1A,B; reviewed in Vosshall and Stocker (2007); Masse et al. (2009)). Most antennal lobe projection neurons (PNs) extend dendrites to a single glomerulus and project axons that bifurcate to innervate two brain regions, the lateral horn and the MB (Stocker et al., 1990; Wong et al., 2002; Jefferis et al., 2007). The invariant circuitry of the lateral horn is thought to mediate innate behaviors, whereas the MB translates olfactory sensory information into learned behavioral responses (Heisenberg et al., 1985). The PN axons synapse onto the dendrites of the Kenyon cells (KCs) in the MB calyx; the parallel axons of the KCs form the MB lobes. Odors activate sparse subpopulations of KCs distributed across the MB without spatial preference (Turner et al., 2008; Honegger et al., 2011; Campbell et al., 2013). Anatomical and physiological studies reveal that each KC receives on average 6.4 inputs from a random combination of glomeruli; that is, knowledge of a single input to a KC provides no information about the identity of the additional inputs, and connections differ in different flies (Murthy et al., 2008; Caron et al., 2013; Gruntman and Turner, 2013). Thus, the calyx of the MB discards the highly ordered structure of the antennal lobe. A restoration of order must therefore be imposed downstream to link the KC representation to an appropriate behavioral output.

Three classes of KCs extend parallel fibers that form the γ, α′/β′, and α/β lobes of the MB, where they form synapses with a relatively small number of MB output neurons (MBONs; Figure 1C) (Crittenden et al., 1998; Ito et al., 1998; Strausfeld et al., 2003; Lin et al., 2007; Tanaka et al., 2008; Busch et al., 2009). The MBONs have dendrites in the MB lobes and project axons to neuropils outside of the MB. Modulatory input neurons, including dopaminergic neurons (DANs) and octopaminergic neurons (Nassel and Elekes, 1992; Tanaka et al., 2008; Busch et al., 2009; Mao and Davis, 2009), also innervate the MB lobes. The MBONs and DANs send their processes to stereotyped locations, defining spatially restricted ‘subdomains’ in each lobe (Ito et al., 1998; Tanaka et al., 2008; Mao and Davis, 2009; Pech et al., 2013). However, these studies did not establish the precise anatomical relationships between the subdomains; knowledge of these relationships will be required to understand the structure and logic of MB circuits.

The DANs are the most prevalent modulatory neurons in the MB and dopamine is thought to act locally to modify KC–MBON synapses (Aso et al., 2010; Waddell, 2013). In accord with this model, DAN activity is required during learning (Schwaerzel et al., 2003; Aso et al., 2010, 2012; Burke et al., 2012; Liu et al., 2012) and exogenous activation of DAN subpopulations can serve as an US in associative learning paradigms (Schroll et al., 2006; Claridge-Chang et al., 2009; Aso et al., 2010, 2012; Burke et al., 2012; Liu et al., 2012). In addition, D1-like dopamine receptors in the KCs are necessary to form olfactory memories (Kim et al., 2007).

Different populations of DANs are activated by USs of different valence; see Figure 1A of the accompanying paper (Aso et al., 2014) for summary (Riemensperger et al., 2005; Mao and Davis, 2009; Liu et al., 2012; Das et al., 2014). Genetic manipulation has also implicated specific subsets of MBONs in the mediation of learned appetitive and aversive behaviors (Sejourne et al., 2011; Pai et al., 2013; Placais et al., 2013; Aso et al., 2014). These experiments implicate the DANs as the source of the learning cue and the MBONs as the mediators of behavioral output. The elucidation of the connections between KCs, DANs, and MBONs should provide insight into a problem shared by invertebrate and vertebrate nervous systems: how is meaning imposed on an unstructured ensemble of neurons and how is imposed valence translated into an appropriate behavioral response?

In this study, we developed new genetic reagents and used them to identify the cell types and projections of the neurons comprising the MB lobes. These data provide insight into the potential connections in the MB and suggest how the MB may mediate learned behaviors. We found that the MB lobes are composed of ∼2200 neurons that include 7 KC, 21 MBON, and 20 DAN cell types. The MBONs of a given type exhibit spatially stereotyped dendritic arbors in the MB lobes that form 15 compartments that collectively tile the lobes. Each DAN cell type projects axons to one or at most two of the compartments defined by the MBONs. The alignment of DAN axons with compartmentalized KC–MBON synapses creates an isolated unit for learning that can transform the disordered KC representation into ordered MBON output. The MBON axons project to five discrete neuropils outside of the MB, providing loci for the convergence of all the information necessary for learned associative responses. The elucidation of the full complement of MB neurons and the details of their projections provide an anatomical substrate from which we can infer a functional logic of olfactory learning and memory.

We designed a genetic approach to examine the architecture of the MB circuit and identified most, if not all, of the neurons innervating the MB lobes. We screened adult brains from 7000 GAL4 lines driven by known enhancers (Pfeiffer et al., 2008; Jenett et al., 2012) to identify lines containing neurons that innervate the MB lobes. These GAL4 drivers typically label many other neurons, making it difficult to disambiguate the projection patterns of the labeled MB neurons. We therefore identified lines with overlapping expression patterns for the MB neurons and used the split-GAL4 strategy (Luan et al., 2006; Pfeiffer et al., 2010) to identify lines with more restricted expression in the MB (Figure 2). After screening 2500 such intersections, we obtained more than 400 split-GAL4 combinations that had strong expression in the MB neurons. Most split-GAL4 lines that drive expression in MBONs or DANs contain a small number of neurons that share virtually identical morphologies and exhibit bilateral symmetry, and this profile is maintained across different individuals (Figure 2—figure supplements 3–6 and data not shown). The split-GAL4 lines that label the KC contain far greater cell numbers (75–600) (Figure 2—figure supplement 2). These split-GAL4 lines allowed us to classify the MB neurons into cell types (Figure 3 and Videos 2–4). We operationally define a cell type as a single neuron (per hemisphere) or a group of neurons that is not further subdivided in any of the 7000 GAL4 lines. Moreover, neurons within most cell types exhibit indistinguishable morphology. Importantly, we identified on average 12 GAL4 drivers that label the same cell type within the set of 7000 lines, indicating that the screen was near saturation in our large GAL4 collection.

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We employed an independent approach, photoactivatable GFP (PA-GFP) tracing (Patterson and Lippincott-Schwartz, 2002; Datta et al., 2008; Ruta et al., 2010), to verify the results of the split-GAL4 experiments and determine whether the neuron types identified in our screen represent the full complement of MBONs and DANs. Photoactivation of the MB labels all neurons that express PA-GFP and project to the MB (Figure 4A; for limitations, see ‘Materials and methods’). Photoactivation of the MB lobes in flies expressing PA-GFP pan-neuronally (except in the KCs) resulted in labeling of eight individual neurons and five clusters of neuronal cell bodies (Figure 4, see ‘Materials and methods’). The number and position of these PA-GFP labeled neurons matched well with the cells identified in the split-GAL4 lines.

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We performed a more refined analysis by photoactivating individual subdomains of the MB lobes (Figure 5). By labeling processes of specific MBONs or DANs, we could decorate individual subdomains of the MB lobes (Figure 1C and see below) (Tanaka et al., 2008), allowing focal photoactivation and subsequent identification of the full complements of neurons innervating each lobe subdomain. Photoactivation of individual subdomains confirmed the results obtained from the genetic approach (Figure 5), but the photoactivation experiments also revealed two MBONs not identified in the split-GAL4 lines. These MBONs were subsequently identified in the VT-GAL4 collection, allowing us to characterize their projections (Figure 5—figure supplement 1).

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The split-GAL4 approach identified 20 DAN types of the PPL1 and PAM clusters that innervate the MB. We identified about 30% less DANs in the PAM cluster in our collection of split-GAL4 lines compared to the number estimated by PA-GFP and anti-dopamine immunoreactivity (Figure 5) (Liu et al., 2012). However, these additional DANs exhibit innervation patterns similar to those of the split-GAL4 lines (see below), and therefore we assume that they either represent closely related cell types or that some of our split-GAL4 drivers are stochastic in their expression and fail to label all members of a cell type. Taken together, these data indicate that each cell type defined by our criteria likely represents an irreducible group of equivalent cells, and that the split-GAL4 screen and PA-GFP tracing identified perhaps all of the neurons in the MB lobes.

These complementary analyses allowed us to make a comprehensive list of cell types comprising the MB lobes (Table 1). We selected 92 split-GAL4 lines representing the best examples for single cell types as well as combinations of related cell types; these split-GAL4 lines will facilitate further anatomical and functional characterization of the MB cell types (see Figure 2—figure supplements 1–6, Supplementary file 1, www.janelia.org/split-gal4, and ‘Materials and methods’). In this study, we focus on the three major classes of neurons that provide the input and output of the MB lobes: 7 types of KCs, 21 types of MBONs, and 20 types of DANs (Figure 3 and Videos 2–4).

The Kenyon cells

Each MB contains ∼2000 KCs that are sequentially generated from four neuroblasts (Ito et al., 1997; Lee et al., 1999; Zhu et al., 2003; Lin et al., 2007). The dendrites of the KCs form the MB calyx and their parallel axons form the three MB lobes (Figure 1) (Crittenden et al., 1998). The main calyx primarily receives olfactory input from the antennal lobe, whereas the smaller ventral and dorsal accessory calyces are thought to receive non-olfactory input (see Figure 1C) (Tanaka et al., 2008; Butcher et al., 2012). The KCs have been divided into three classes, γ, α′/β′, and α/β, with each class projecting axons to the eponymous lobe (Crittenden et al., 1998; Lee et al., 1999). The split-GAL4 screen and the analysis of the axonal projection patterns of single cells revealed that these three classes of KCs divide into seven cell types (Figure 3A and Video 2). Each of the four neuroblasts contributes to each of the seven cell types and the dendrites of the KCs generated from the different neuroblasts remain segregated in the main calyx (Lin et al., 2007). The parallel axon fibers of each of the seven types of KCs occupy specific layers within the γ, α′/β′, and α/β lobes (Figures 6 and 7). Two KC types divide the γ lobe into the main and dorsal (d) layers, two types divide the α′/β′ lobe into the middle (m) and anterior–posterior (ap) layers, and three KC types divide the α/β lobe into the posterior (p), core (c), and surface (s) layers (Figures 6 and 7, also see Figure 1C). Examination of single cell morphologies suggests that each KC may form en passant synapses with target MBONs along the length of its axon, providing each MBON with access to a large number of KC inputs. Five of the seven types of KCs elaborate their dendrites in the main calyx, whereas two types of KCs (γd and α/βp) have dendrites exclusively in the ventral and dorsal accessory calyces, respectively (Figures 6A and 7A) (Lin et al., 2007; Tanaka et al., 2008; Butcher et al., 2012).

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The five KC types (γmain, α′/β′ap, α′/β′m, α/βc, and α/βs) that receive olfactory information are each represented by hundreds of neurons per hemisphere and have their dendrites in the main calyx. Each KC cell type sends axonal projections to a spatially segregated layer in the lobes. The dendritic arbors of each KC type also tend to be found in the same regions of the calyx (Lin et al., 2007; Leiss et al., 2009), but those dendritic zones are largely overlapping and individual KCs within a given cell type exhibit variable dendritic projection patterns (Figures 6 and 7). Moreover, the KCs receive input from an apparently random collection of glomeruli (Murthy et al., 2008; Caron et al., 2013; Gruntman and Turner, 2013). These features are in sharp contrast to most neuronal cell types in the olfactory pathway of the fly that are thought to consist of one to ten neurons that exhibit stereotyped projections (Yu et al., 2010), suggesting that their input and output connections are genetically predetermined. These observations suggest a unique function of the KCs in the processing of olfactory information (see ‘Discussion’).

The MB output neurons

The MBONs extend dendrites that overlap with the KC axons in the MB lobes and project axons outside the MB. By determining the polarity of each cell type using high-resolution confocal imaging along with an analysis of the expression of the presynaptic reporter synaptotagmin-smGFP-HA (Syt::smGFP-HA; Figure 8), we identified 34 MBONs that comprise 21 different cell types (Table 1, Figure 3B, Video 3). We employed immunostaining to identify MBON types as either cholinergic, glutamatergic, or GABAergic (Figure 9, Table 1). MBONs that use the same neurotransmitter extend dendrites to adjacent regions of the lobes; cholinergic MBONs in the vertical (α and α′) lobes, glutamatergic MBONs in the medial (β, β′, and γ) lobes, and GABAergic MBONs in an area of the lobes at the intersection between these two regions (Figure 9 and Video 5).

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Fourteen MBON cell types consist of only one cell per hemisphere, six types contain two cells, and one type eight cells per hemisphere. In split-GAL4 lines with expression in more than one neuron, single-cell resolution was achieved by using the multicolor flp-out strategy (MCFO; Nern et al., in preparation, Figure 8). Single cell analysis revealed that each member of an MBON type exhibits indistinguishable morphology as assessed by light microscopy, and these stereotyped projection patterns are invariant across flies (see below for all cell types).

The 21 MBON types elaborate dendritic arbors in insular, segregated domains of the lobes that we call compartments. MBON dendritic arbors within each compartment exhibit little, if any, overlap with arbors in neighboring compartments (Figure 10). Computational alignment of the dendritic arbors of each of the MBON types within a single reference brain revealed that these compartments collectively tile the MB lobes with minimal overlap (Figure 10G,I,K). The alignment reveals gaps between arbors at four compartment borders; staining of the MB lobes for the presynaptic marker Bruchpilot (Figure 10—figure supplement 1) suggests that these gaps represent areas of reduced synaptic density. Two-color labeling experiments confirmed that the dendritic arbors of different MBONs are segregated in spatially stereotyped compartments (Figure 11A–C). We observed ensheathing glia at the borders between the MB lobes but not between the MBON compartments in each lobe (Figure 11J–L).

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The MB lobes are divided into 15 distinct compartments containing the segregated dendritic arbors of one or a small number of MBONs (Figure 1C, Figure 12 and Figure 13). These compartments tile the MB lobes, revealing a general organizational principle of the MB output. This organization is in accord with an earlier proposal by Tanaka et al. (2008) that each of the γ, α′/β′, and α/β lobes is divided into five domains. 13 of the 21 MBON types extend dendrites to a single compartment, and 8 MBON types project to two compartments (Figure 12A–C). Most of the MBON types innervate KCs from each of the layers within a compartment, but eight types restrict their dendritic arbors to specific layers (Figure 11D, 12A,C,D).

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The identification of the full complement of 21 MBON types highlights the extensive convergence of 2000 KCs onto just 34 MBONs, a number even smaller than the number of glomeruli in the AL. Thus, the high-dimensional KC representation of odor identity is transformed into a low-dimensional MB output. This suggests that the MBONs do not represent odor identity but instead provide a representation that may bias behavioral responses (see ‘Discussion’).

Dopaminergic neurons

Two clusters of dopaminergic neurons (PPL1 and PAM) have previously been shown to project axon terminals to specific regions within the MB lobes and transmit information about reward and punishment to the MB to guide learning (Schwaerzel et al., 2003; Claridge-Chang et al., 2009; Mao and Davis, 2009; Aso et al., 2010, 2012; Burke et al., 2012; Liu et al., 2012). Our split-GAL4 screen identified over 100 DANs of 20 types (Figure 3C, Table 1 and Video 4). Each DAN type contains a small number of neurons: DAN types from the PPL1 cluster contain one or two cells per hemisphere and DAN types from the PAM cluster contain up to ∼20 cells per hemisphere (Table 1, see Figures 14–16 for each cell type, see ‘Materials and methods’ for classification).

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The axon terminals of the DANs project to specific compartments and, similar to the dendrites of MBONs, tile the entire MB lobes (Figure 10H,J,L, 14–16 and Video 4). 17 of the 20 DAN types project to only a single compartment (Figure 12). Two-color labeling and multicolor flp-out of DANs innervating neighboring compartments show clear segregation of their axon termini (Figure 11E,F and data not shown). Two-color labeling experiments revealed overlap of the axon termini of DAN types and the dendritic arbors of cognate MBON types that innervate the same compartment (Figure 11G–I). Computational alignment to a single reference brain extends these observations to all DAN types and further demonstrates that the DAN axon termini tile the MB lobes (Figure 10H,J,L). Thus, different MBON types have access to largely equivalent input from the KCs but are modulated by different DANs (Figures 12 and 13). In classical learning paradigms, different DAN types respond to different unconditioned stimuli (US) (Riemensperger et al., 2005; Mao and Davis, 2009; Burke et al., 2012; Liu et al., 2012). The compartmental organization we observe suggests that the DANs may convey information about the US to specific MBON types. Dopamine release in specific compartments may modify local KC–MBON synapses to bias behavioral output.

Interactions between the MBON compartments

We identified three MBONs and one DAN type that appear to interconnect different compartments of the MB lobes (Figure 12A, Figure 14—figure supplement 1E, and Figure 17A–D). Each of these three MBONs projects to the compartments of other MBONs but not back to the compartment occupied by its own dendrites. Thus information flows in one direction, and these MBON connections could create a multi-layered feedforward network (Figure 17J, see ‘Discussion’). 12 of the MBON types receive input from the KCs but not from other MBONs and therefore read out KC activity as a single output layer. Most of the MBONs having dendrites in the α′/β′ and γ lobes provide such a single-layer readout. In contrast, the glutamatergic MBON whose dendrites arborize in the γ4 compartment projects axons into the γ1 and γ2 compartments as well as to neuropils outside the MB (Figure 17A,G–I). Thus, the γ1 and γ2 MBONs have access to direct KC input as well as to the input provided by the γ4 MBON. The outputs of these two γ lobe compartments thus reflect a two-layer feedforward network. The readout of the α/β lobe is even more elaborate because the γ1 MBON projects to all of the compartments of the α/β lobe (Figure 17C,E,F), and the β1 MBON projects to the α1, α2, and α3 compartments (Figure 17B). The α1, α2, and α3 MBONs thus represent a four-layer feedforward network with access to the KC activity of the α/β and γ lobes, both directly and indirectly through MBON intermediaries (Figure 17J).

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The DAN cell type that projects axons to the γ4 compartment (Figure 17D) provides another form of communication between compartments. This cell type extends dendrites to the γ1 and γ2 compartments, where they appear to receive direct inputs from the axonal termini of MBON-γ4>γ1γ2 (Figure 17G–I), creating a recurrent loop involving modulatory dopamine input.

The MBON axons that innervate other compartments within the MB lobes have access to dopamine inputs and thus can potentially be modified by learning. Adaptive multi-layer or ‘deep’ feedforward networks are known to be capable of more complex readout functions than single-layer readouts (Bishop, 2006). Thus, the four-layer α lobe readout system may support more sophisticated neuronal computations than the one- and two-layer γ or α′/β′ lobe systems.

Inputs to the DANs and outputs of the MBONs

The 34 MBONs represent the sole outputs of the MB lobes. Computational alignment of single-cell images allowed us to localize the axon termini of the MBONs outside the MB. The axon terminals of the MBONs (Figure 18) converge onto five neuropils: the crepine (CRE; a region surrounding the horizontal/medial lobes), the superior medial protocerebrum (SMP), the superior intermediate protocerebrum (SIP), the superior lateral protocerebrum (SLP), and the lateral horn (LH) (Figure 18E). Most MBON types project axons to several of these neuropils (Figure 18—figure supplement 1). Within a neuropil, the axon terminals of each MBON type exhibit distinct and confined projection patterns, suggesting that different MBON types may synapse onto different neurons (Figure 19, also see Figures 14–16 for individual MBON projection patterns). However, we also observed significant overlap between the axon terminals of different MBONs, suggesting that in certain cases MBONs may converge onto the same post-synaptic target neuron (Figure 20A,D,E). These results, obtained by computational alignment, were confirmed for a subset of MBONs by two-color labeling experiments (Figure 20J–L; Video 6).

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Computational alignment of single cell images of each DAN type identified the dendritic arbors of the DANs outside the MB (Figure 18D). Interestingly, 90% of the dendritic arbors of the DANs reside in four of the neuropils targeted by the MBONs: CRE, SMP, SIP, and SLP (Figures 18E and 19). Because DANs are activated by unconditioned stimuli (US), these neuropils are likely targets of the US input (see ‘Discussion’). We observed cases where the dendrites of different DAN types in a single neuropil overlap (Figure 20B,F,G), suggesting that they may share upstream input. Furthermore, we observed overlap between MBON terminals and DAN dendrites within each neuropil, implying that some MBONs may synapse on specific DANs (Figure 20C,H,I). In some cases, the axon terminals of an MBON overlap with the dendrites of the DAN type innervating the same compartment (Figure 20H). This may provide feedback that regulates dopamine release and hence learning. In other cases, axon termini of MBONs from one compartment overlap with the dendrites of DAN types projecting to different compartments (Figure 20I). This provides a pathway through which the activity of one MBON could modulate the synapses between KCs and MBONs of other compartments.

The MBONs are thought to elicit learned behavioral responses, and it is therefore of interest that four MBON types project to the lateral horn (Figure 18—figure supplement 1), a brain region responsible for innate odor responses (Heisenberg et al., 1985). These cholinergic MBONs may exploit LH neurons capable of eliciting innate behaviors to generate learned olfactory responses (Sejourne et al., 2011). This connection may also serve to modulate innate behavioral responses as a consequence of learning. Most of the MBON outputs, however, do not target the LH but converge onto the neuropils surrounding the MB (Figure 18). These convergent loci also receive projections from the antennal lobe and the lateral horn and are likely to be major sites of integration and processing of information within the fly brain (Aso et al., 2014).

We have identified the neurons that comprise the MB and characterized their projection patterns. These data provide a comprehensive map describing the potential connections of neurons in the MB lobes. The MB lobes are innervated by approximately 2000 KCs, 34 MBONs of 21 cell types, and about 130 modulatory DANs of 20 cell types that fall into two families, PAM and PPL1. These data, extending the level of detail and completeness of previous studies of MB anatomy (Tanaka et al., 2008; Mao and Davis, 2009), describe the neuronal architecture of the MB and provide insight into the logic of MB function.

Most of the cell types in the olfactory circuit—the MBONs and the DANs as well as the olfactory sensory neurons, the PNs, and the LH neurons (Vosshall and Stocker, 2007; Masse et al., 2009)—comprise small groups containing from one to 20 neurons (Tanaka et al., 2004; Tanaka et al., 2008, 2012; Yu et al., 2010). Furthermore, neurons within these groups share highly stereotyped projection patterns that superimpose across different individuals (this work) (Marin et al., 2002; Datta et al., 2008). This suggests that the input and output connections of these neurons are genetically determined (Ruta et al., 2010; Kohl et al., 2013; Fisek and Wilson, 2014), although these may be subject to plasticity. In contrast, the KCs of the MB comprise 2000 neurons that define only seven distinct cell types, receive unstructured, rather than stereotyped, inputs (Murthy et al., 2008; Caron et al., 2013; Gruntman and Turner, 2013), and connect to multiple MBONs through plastic synapses (Cassenaer and Laurent, 2007, 2012). These distinctive features of the KCs afford the MB the ability to contextualize novel sensory experiences, consistent with its role in mediating learned olfactory associations and behavior.

Convergence zones of the MB outputs

The axonal terminals of the MBONs are largely confined to five discrete neuropils in the brain, the SIP, SMP, CRE, SLP, and the LH (Figures 18 and 19). The CRE, SMP, SIP, and SLP may be sites of convergence for all of the signals relevant for classical conditioning. These convergence zones are major sites of arborization for the dendrites of the DANs and axons of the MBONs. Since DANs are activated in response to an aversive or appetitive US, these neuropils are also likely to receive input from neurons transmitting information about the nature of the US. A US, by definition, elicits an innate behavioral response consistent with its valence, suggesting that the outputs from these neuropils may convey motor commands. Interestingly, dendrites of neurons projecting to the fan-shaped body in the central complex, a brain region coordinating motor actions (Strauss, 2002), arborize in these neuropils (Hanesch et al., 1989; Young and Armstrong, 2010). We therefore postulate an evolutionary primitive circuit in the MBON convergence zones, in which US inputs activate motor command neurons to elicit innate responses. Evolution may have built upon this simple reflex circuit, incorporating pathways from the MB that generate learned behaviors in response to a CS (Figure 21).

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1. 1. Alekseyenko OV
   2. Lee C
   3. Kravitz EA

   (2010) Targeted manipulation of serotonergic neurotransmission affects the escalation of aggression in adult male Drosophila melanogaster

   PLOS ONE 5:e10806.

   https://doi.org/10.1371/journal.pone.0010806

   • Google Scholar
2. 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

   • Google Scholar
3. 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

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

   (2014)

   Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila

   e04580, eLife, 3, 10.7554/eLife.04580.

   • 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

   • Google Scholar
6. 1. Avants BB
   2. Epstein CL
   3. Grossman M
   4. Gee JC

   (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

   Medical Image Analysis 12:26–41.

   https://doi.org/10.1016/j.media.2007.06.004

   • Google Scholar
7. 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

   The Journal of Neuroscience 28:13742–13753.

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

   • Google Scholar
8. 1. Baker NE
   2. Yu S
   3. Han D

   (1996) Evolution of proneural atonal expression during distinct regulatory phases in the developing Drosophila eye

   Current Biology 6:1290–1301.

   https://doi.org/10.1016/S0960-9822(02)70715-X

   • Google Scholar
9. 1. Beall CJ
   2. Hirsh J

   (1987) Regulation of the Drosophila dopa decarboxylase gene in neuronal and glial cells

   Genes & Development 1:510–520.

   https://doi.org/10.1101/gad.1.5.510

   • Google Scholar
10. Book

    1. Bishop CM

    (2006)

    Pattern recognition and machine learning

    New York: Springer.

    • Google Scholar
11. 1. Bräcker LB
    2. Siju KP
    3. Varela N
    4. Aso Y
    5. Zhang M
    6. Hein I
    7. Vasconcelos ML
    8. Grunwald Kadow IC

    (2013) Essential role of the mushroom body in context-dependent CO(2) avoidance in Drosophila

    Current Biology 23:1228–1234.

    https://doi.org/10.1016/j.cub.2013.05.029

    • Google Scholar
12. 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

    • Google Scholar
13. 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

    • Google Scholar
14. 1. Busch S
    2. Tanimoto H

    (2010) Cellular configuration of single octopamine neurons in Drosophila

    The Journal of Comparative Neurology 518:2355–2364.

    https://doi.org/10.1002/cne.22337

    • Google Scholar
15. 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

    • Google Scholar
16. 1. Cachero S
    2. Ostrovsky AD
    3. Yu JY
    4. Dickson BJ
    5. Jefferis GS

    (2010) Sexual dimorphism in the fly brain

    Current Biology 20:1589–1601.

    https://doi.org/10.1016/j.cub.2010.07.045

    • Google Scholar
17. 1. Campbell RA
    2. Honegger KS
    3. Qin HT
    4. Li WH
    5. Demir E
    6. Turner GC

    (2013) Imaging a population code for odor identity in the Drosophila mushroom body

    Journal of Neuroscience 33:10568–10581.

    https://doi.org/10.1523/JNEUROSCI.0682-12.2013

    • Google Scholar
18. 1. Caron SJ
    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
19. 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

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

    (2007) Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts

    Nature 448:709–713.

    https://doi.org/10.1038/nature05973

    • Google Scholar
21. 1. Chiang AS
    2. Lin CY
    3. Chuang CC
    4. Chang HM
    5. Hsieh CH
    6. Yeh CW
    7. Shih CT
    8. Wu JJ
    9. Wang GT
    10. Chen YC
    11. Wu CC
    12. Chen GY
    13. Ching YT
    14. Lee PC
    15. Lin CY
    16. Lin HH
    17. Wu CC
    18. Hsu HW
    19. Huang YA
    20. Chen JY
    21. Chiang HJ
    22. Lu CF
    23. Ni RF
    24. Yeh CY
    25. Hwang JK

    (2011) Three-dimensional reconstruction of brain-wide Wiring networks in Drosophila at single-cell resolution

    Current Biology 21:1–11.

    https://doi.org/10.1016/j.cub.2010.11.056

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

    (2009) Writing memories with light-addressable reinforcement circuitry

    Cell 139:405–415.

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

    • Google Scholar
23. 1. Cole SH
    2. Carney GE
    3. Mcclung CA
    4. Willard SS
    5. Taylor BJ
    6. Hirsh J

    (2005) Two functional but noncomplementing Drosophila tyrosine decarboxylase genes

    Journal of Biological Chemistry 280:14948–14955.

    https://doi.org/10.1074/jbc.M414197200

    • Google Scholar
24. 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.

    • Google Scholar
25. 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

    • Google Scholar
26. 1. Datta SR
    2. Vasconcelos ML
    3. Ruta V
    4. Luo S
    5. Wong A
    6. Demir E
    7. Flores J
    8. Balonze K
    9. Dickson BJ
    10. Axel R

    (2008) The Drosophila pheromone cVA activates a sexually dimorphic neural circuit

    Nature 452:473–477.

    https://doi.org/10.1038/nature06808

    • Google Scholar
27. 1. de Belle JS
    2. Heisenberg M

    (1994) Associative odor learning in Drosophila abolished by chemical ablation of mushroom bodies

    Science 263:692–695.

    https://doi.org/10.1126/science.8303280

    • Google Scholar
28. 1. Dubnau J
    2. Grady L
    3. Kitamoto T
    4. Tully T

    (2001) Disruption of neurotransmission in Drosophila mushroom body blocks retrieval but not acquisition of memory

    Nature 411:476–480.

    https://doi.org/10.1038/35078077

    • Google Scholar
29. 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

    • Google Scholar
30. 1. Farris SM
    2. Strausfeld NJ

    (2003) A unique mushroom body substructure common to basal cockroaches and to termites

    The Journal of Comparative Neurology 456:305–320.

    https://doi.org/10.1002/cne.10517

    • Google Scholar
31. 1. Fisek M
    2. Wilson RI

    (2014) Stereotyped connectivity and computations in higher-order olfactory neurons

    Nature Neuroscience 17:280–288.

    https://doi.org/10.1038/nn.3613

    • Google Scholar
32. 1. Friggi-Grelin F
    2. Coulom H
    3. Meller M
    4. Gomez D
    5. Hirsh J
    6. Birman S

    (2003) Targeted gene expression in Drosophila dopaminergic cells using regulatory sequences from tyrosine hydroxylase

    Journal of Neurobiology 54:618–627.

    https://doi.org/10.1002/neu.10185

    • Google Scholar
33. 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

    • Google Scholar
34. 1. Guizar-Sicairos M
    2. Thurman ST
    3. Fienup JR

    (2008) Efficient subpixel image registration algorithms

    Optics Letters 33:156–158.

    https://doi.org/10.1364/OL.33.000156

    • Google Scholar
35. 1. Hanesch U
    2. Fischbach KF
    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
36. 1. Heisenberg M
    2. Borst A
    3. Wagner S
    4. Byers D

    (1985) Drosophila mushroom body mutants are deficient in olfactory learning

    Journal of Neurogenetics 2:1–30.

    https://doi.org/10.3109/01677068509100140

    • Google Scholar
37. 1. Hofbauer A
    2. Ebel T
    3. Waltenspiel B
    4. Oswald P
    5. Chen YC
    6. Halder P
    7. Biskup S
    8. Lewandrowski U
    9. Winkler C
    10. Sickmann A
    11. Buchner S
    12. Buchner E

    (2009) The Wuerzburg hybridoma library against Drosophila brain

    Journal of Neurogenetics 23:78–91.

    https://doi.org/10.1080/01677060802471627

    • Google Scholar
38. 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

    • Google Scholar
39. 1. Huerta R
    2. Nowotny T
    3. Garcia-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

    • Google Scholar
40. 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

    • Google Scholar
41. 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.

    • Google Scholar
42. 1. Ito K
    2. Shinomiya K
    3. Ito M
    4. Armstrong JD
    5. Boyan G
    6. Hartenstein V
    7. Harzsch S
    8. Heisenberg M
    9. Homberg U
    10. Jenett A
    11. Keshishian H
    12. Restifo LL
    13. Rossler W
    14. Simpson JH
    15. Strausfeld NJ
    16. Strauss R
    17. Vosshall LB

    (2014) A systematic nomenclature for the insect brain

    Neuron 81:755–765.

    https://doi.org/10.1016/j.neuron.2013.12.017

    • Google Scholar
43. 1. Ito K
    2. Suzuki K
    3. Estes P
    4. Ramaswami M
    5. Yamamoto D
    6. Strausfeld NJ

    (1998)

    The organization of extrinsic neurons and their implications in the functional roles of the mushroom bodies in Drosophila melanogaster Meigen

    Learning & Memory 5:52–77.

    • Google Scholar
44. 1. Jefferis GS
    2. Potter CJ
    3. Chan AM
    4. Marin EC
    5. Rohlfing T
    6. Maurer CR Jr
    7. Luo L

    (2007) Comprehensive maps of Drosophila higher olfactory centers: spatially segregated fruit and pheromone representation

    Cell 128:1187–1203.

    https://doi.org/10.1016/j.cell.2007.01.040

    • Google Scholar
45. 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

    • Google Scholar
46. 1. Johard HA
    2. Enell LE
    3. Gustafsson E
    4. Trifilieff P
    5. Veenstra JA
    6. Nassel DR

    (2008) Intrinsic neurons of Drosophila mushroom bodies express short neuropeptide F: Relations to extrinsic neurons expressing different neurotransmitters

    Journal of Comparative Neurology 507:1479–1496.

    https://doi.org/10.1002/cne.21636

    • Google Scholar
47. 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

    The Journal of Neuroscience 27:7640–7647.

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

    • Google Scholar
48. 1. Kohl J
    2. Ostrovsky AD
    3. Frechter S
    4. Jefferis GS

    (2013) A bidirectional circuit switch reroutes pheromone signals in male and female brains

    Cell 155:1610–1623.

    https://doi.org/10.1016/j.cell.2013.11.025

    • Google Scholar
49. 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

    • Google Scholar
50. 1. Krashes MJ
    2. Keene AC
    3. Leung B
    4. Armstrong JD
    5. Waddell S

    (2007) Sequential use of mushroom body neuron subsets during Drosophila odor memory processing

    Neuron 53:103–115.

    https://doi.org/10.1016/j.neuron.2006.11.021

    • Google Scholar
51. 1. Laissue PP
    2. Reiter C
    3. Hiesinger PR
    4. Halter S
    5. Fischbach KF
    6. Stocker RF

    (1999) Three-dimensional reconstruction of the antennal lobe in Drosophila melanogaster

    The Journal of Comparative Neurology 405:543–552.

    https://doi.org/10.1002/(SICI)1096-9861(19990322)405:43.0.CO;2-A

    • Google Scholar
52. 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

    • Google Scholar
53. 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.

    • Google Scholar
54. 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

    Journal of Comparative Neurology 517:808–824.

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

    • Google Scholar
55. 1. Li H
    2. Chaney S
    3. Roberts IJ
    4. Forte M
    5. Hirsh J

    (2000) Ectopic G-protein expression in dopamine and serotonin neurons blocks cocaine sensitization in Drosophila melanogaster

    Current Biology 10:211–214.

    https://doi.org/10.1016/S0960-9822(00)00340-7

    • Google Scholar
56. 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

    • Google Scholar
57. 1. Liu C
    2. Placais 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

    • Google Scholar
58. 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

    • Google Scholar
59. 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/10.1038/22919

    • Google Scholar
60. 1. Luan H
    2. Peabody NC
    3. Vinson CR
    4. White BH

    (2006) Refined spatial manipulation of neuronal function by combinatorial restriction of transgene expression

    Neuron 52:425–436.

    https://doi.org/10.1016/j.neuron.2006.08.028

    • Google Scholar
61. 1. Mahr A
    2. Aberle H

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

    Gene Expression Patterns 6:299–309.

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

    • Google Scholar
62. 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

    • Google Scholar
63. 1. Marin EC
    2. Jefferis GS
    3. Komiyama T
    4. Zhu H
    5. Luo L

    (2002) Representation of the glomerular olfactory map in the Drosophila brain

    Cell 109:243–255.

    https://doi.org/10.1016/S0092-8674(02)00700-6

    • Google Scholar
64. 1. Masek P
    2. Scott K

    (2010) Limited taste discrimination in Drosophila

    Proceedings of the National Academy of Sciences of USA 107:14833–14838.

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

    • Google Scholar
65. 1. Masse NY
    2. Turner GC
    3. Jefferis GS

    (2009) Olfactory information processing in Drosophila

    Current Biology 19:R700–R713.

    https://doi.org/10.1016/j.cub.2009.06.026

    • Google Scholar
66. 1. McGuire SE
    2. Le PT
    3. Davis RL

    (2001) The role of Drosophila mushroom body signaling in olfactory memory

    Science 293:1330–1333.

    https://doi.org/10.1126/science.1062622

    • Google Scholar
67. 1. Murthy M
    2. Fiete I
    3. Laurent G

    (2008) Testing odor response stereotypy in the Drosophila mushroom body

    Neuron 59:1009–1023.

    https://doi.org/10.1016/j.neuron.2008.07.040

    • Google Scholar
68. 1. Nassel DR
    2. Elekes K

    (1992) Aminergic neurons in the brain of blowflies and Drosophila: dopamine- and tyrosine hydroxylase-immunoreactive neurons and their relationship with putative histaminergic neurons

    Cell and Tissue Research 267:147–167.

    https://doi.org/10.1007/BF00318701

    • Google Scholar
69. 1. Ofstad TA
    2. Zuker CS
    3. Reiser MB

    (2011) Visual place learning in Drosophila melanogaster

    Nature 474:204–207.

    https://doi.org/10.1038/nature10131

    • Google Scholar
70. 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

    Proceedings of the National Academy of Sciences of USA 110:7898–7903.

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

    • Google Scholar
71. 1. Patterson GH
    2. Lippincott-Schwartz J

    (2002) A photoactivatable GFP for selective photolabeling of proteins and cells

    Science 297:1873–1877.

    https://doi.org/10.1126/science.1074952

    • Google Scholar
72. 1. Pech U
    2. Pooryasin A
    3. Birman S
    4. Fiala A

    (2013) Localization of the contacts between Kenyon cells and aminergic neurons in the Drosophila melanogaster brain using SplitGFP reconstitution

    The Journal of Comparative Neurology 521:3992–4026.

    https://doi.org/10.1002/cne.23388

    • Google Scholar
73. 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

    • Google Scholar
74. 1. Perisse E
    2. Yin Y
    3. Lin AC
    4. Lin S
    5. Huetteroth W
    6. Waddell S

    (2013) Different kenyon cell populations drive learned approach and avoidance in Drosophila

    Neuron 79:945–956.

    https://doi.org/10.1016/j.neuron.2013.07.045

    • Google Scholar
75. 1. Pfeiffer BD
    2. Jenett A
    3. Hammonds AS
    4. Ngo TT
    5. Misra S
    6. Murphy C
    7. Scully A
    8. Carlson JW
    9. Wan KH
    10. Laverty TR
    11. Mungall C
    12. Svirskas R
    13. Kadonaga JT
    14. Doe CQ
    15. Eisen MB
    16. Celniker SE
    17. Rubin GM

    (2008) Tools for neuroanatomy and neurogenetics in Drosophila

    Proceedings of the National Academy of Sciences of USA 105:9715–9720.

    https://doi.org/10.1073/pnas.0803697105

    • Google Scholar
76. 1. Pfeiffer BD
    2. Ngo TT
    3. Hibbard KL
    4. Murphy C
    5. Jenett A
    6. Truman JW
    7. Rubin GM

    (2010) Refinement of tools for targeted gene expression in Drosophila

    Genetics 186:735–755.

    https://doi.org/10.1534/genetics.110.119917

    • Google Scholar
77. 1. Placais 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

    • Google Scholar
78. 1. Potter CJ
    2. Tasic B
    3. Russler EV
    4. Liang L
    5. Luo LQ

    (2010) The Q system: a repressible binary system for transgene expression, lineage tracing, and Mosaic analysis

    Cell 141:536–548.

    https://doi.org/10.1016/j.cell.2010.02.025

    • Google Scholar
79. 1. Quinn WG
    2. Harris WA
    3. Benzer S

    (1974) Conditioned behavior in Drosophila melanogaster

    Proceedings of the National Academy of Sciences of USA 71:708–712.

    https://doi.org/10.1073/pnas.71.3.708

    • Google Scholar
80. 1. Riemensperger T
    2. Voller T
    3. Stock P
    4. Buchner E
    5. Fiala A

    (2005) Punishment prediction by dopaminergic neurons in Drosophila

    Current Biology 15:1953–1960.

    https://doi.org/10.1016/j.cub.2005.09.042

    • Google Scholar
81. 1. Ruta V
    2. Datta SR
    3. Vasconcelos ML
    4. Freeland J
    5. Looger LL
    6. Axel R

    (2010) A dimorphic pheromone circuit in Drosophila from sensory input to descending output

    Nature 468:686–690.

    https://doi.org/10.1038/nature09554

    • Google Scholar
82. 1. Schnaitmann C
    2. Vogt K
    3. Triphan T
    4. Tanimoto H

    (2010) Appetitive and aversive visual learning in freely moving Drosophila

    Frontiers in Behavioral Neuroscience 4:10.

    https://doi.org/10.3389/fnbeh.2010.00010

    • Google Scholar
83. 1. Schroll C
    2. Riemensperger T
    3. Bucher D
    4. Ehmer J
    5. Voller T
    6. Erbguth K
    7. Gerber B
    8. Hendel T
    9. Nagel G
    10. Buchner E
    11. Fiala A

    (2006) Light-induced activation of distinct modulatory neurons triggers appetitive or aversive learning in Drosophila larvae

    Current Biology 16:1741–1747.

    https://doi.org/10.1016/j.cub.2006.07.023

    • Google Scholar
84. 1. Schurmann FW

    (1974) On the functional anatomy of the corpora pedunculata in insects (author's transl)

    Experimental Brain Research 19:406–432.

    https://doi.org/10.1007/BF00234464

    • Google Scholar
85. 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

    Journal of Neuroscience 23:10495–10502.

    • Google Scholar
86. 1. Sejourne J
    2. Placais PY
    3. Aso Y
    4. Siwanowicz I
    5. Trannoy S
    6. Thoma V
    7. Tedjakumala SR
    8. Rubin GM
    9. Tchenio 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

    • Google Scholar
87. 1. Siegel RW
    2. Hall JC

    (1979) Conditioned responses in courtship behavior of normal and mutant Drosophila

    Proceedings of the National Academy of Sciences of USA 76:3430–3434.

    https://doi.org/10.1073/pnas.76.7.3430

    • Google Scholar
88. 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

    • Google Scholar
89. Book

    1. Strausfeld NJ

    (2012)

    Arthropod brains: evolution, functional elegance, and historical significance

    Cambridge, MA: Harvard University Press.

    • Google Scholar
90. 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

    • Google Scholar
91. 1. Strauss R

    (2002) The central complex and the genetic dissection of locomotor behaviour

    Current Opinion in Neurobiology 12:633–638.

    https://doi.org/10.1016/S0959-4388(02)00385-9

    • Google Scholar
92. 1. Struhl G
    2. Basler K

    (1993) Organizing activity of wingless protein in Drosophila

    Cell 72:527–540.

    https://doi.org/10.1016/0092-8674(93)90072-X

    • Google Scholar
93. Book

    1. Sutton RS
    2. Barto AG

    (1998)

    Reinforcement learning: an introduction

    Cambridge, Mass: MIT Press.

    • Google Scholar
94. 1. Takagawa K
    2. Salvaterra P

    (1996) Analysis of choline acetyltransferase protein in temperature sensitive mutant flies using newly generated monoclonal antibody

    Neuroscience Research 24:237–243.

    https://doi.org/10.1016/0168-0102(95)00999-X

    • Google Scholar
95. 1. Tanaka NK
    2. Awasaki T
    3. Shimada T
    4. Ito K

    (2004) Integration of chemosensory pathways in the Drosophila second-order olfactory centers

    Current Biology 14:449–457.

    https://doi.org/10.1016/j.cub.2004.03.006

    • Google Scholar
96. 1. Tanaka NK
    2. Endo K
    3. Ito K

    (2012) Organization of antennal lobe-associated neurons in adult Drosophila melanogaster brain

    The Journal of Comparative Neurology 520:4067–4130.

    https://doi.org/10.1002/cne.23142

    • Google Scholar
97. 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

    • Google Scholar
98. 1. Tempel BL
    2. Bonini N
    3. Dawson DR
    4. Quinn WG

    (1983) Reward learning in normal and mutant Drosophila

    Proceedings of the National Academy of Sciences of USA 80:1482–1486.

    https://doi.org/10.1073/pnas.80.5.1482

    • Google Scholar
99. 1. Tomchik SM

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

    The Journal of Neuroscience 33:2166–2176a.

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

    • Google Scholar
100. 1. Tomer R
     2. Denes AS
     3. Tessmar-Raible K
     4. Arendt D

     (2010) Profiling by image registration reveals common origin of annelid mushroom bodies and vertebrate pallium

     Cell 142:800–809.

     https://doi.org/10.1016/j.cell.2010.07.043

     • Google Scholar
101. 1. Tully T
     2. Quinn WG

     (1985) Classical conditioning and retention in normal and mutant Drosophila melanogaster

     Journal of Comparative Physiology. A, Sensory, Neural, and Behavioral Physiology 157:263–277.

     https://doi.org/10.1007/BF01350033

     • Google Scholar
102. 1. Turner GC
     2. Bazhenov M
     3. Laurent G

     (2008) Olfactory representations by Drosophila mushroom body neurons

     Journal of Neurophysiology 99:734–746.

     https://doi.org/10.1152/jn.01283.2007

     • Google Scholar
103. 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

     • Google Scholar
104. 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

     • Google Scholar
105. 1. Vosshall LB
     2. Stocker RF

     (2007) Molecular architecture of smell and taste in Drosophila

     Annual Review of Neuroscience 30:505–533.

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

     • Google Scholar
106. 1. Waddell S

     (2013) Reinforcement signalling in Drosophila; dopamine does it all after all

     Current Opinion in Neurobiology 23:324–329.

     https://doi.org/10.1016/j.conb.2013.01.005

     • Google Scholar
107. 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

     • Google Scholar
108. 1. Wan Y
     2. Otsuna H
     3. Chien CB
     4. Hansen C

     (2009) An interactive visualization tool for multi-channel confocal microscopy data in neurobiology research

     IEEE Transactions on Visualization and Computer Graphics 15:1489–1496.

     https://doi.org/10.1109/TVCG.2009.118

     • Google Scholar
109. 1. Wan Y
     2. Otsuna H
     3. Chien CB
     4. Hansen C

     (2012)

     FluoRender: an application of 2D image space methods for 3D and 4D confocal microscopy data visualization in neurobiology research

     IEEE Pacific Visualization Symposium pp. 201–208.

     • Google Scholar
110. 1. Wong AM
     2. Wang JW
     3. Axel R

     (2002) Spatial representation of the glomerular map in the Drosophila protocerebrum

     Cell 109:229–241.

     https://doi.org/10.1016/S0092-8674(02)00707-9

     • Google Scholar
111. 1. Young JM
     2. Armstrong JD

     (2010) Building the central complex in Drosophila: the generation and development of distinct neural subsets

     The Journal of Comparative Neurology 518:1525–1541.

     https://doi.org/10.1002/cne.22285

     • Google Scholar
112. 1. Yu HH
     2. Awasaki T
     3. Schroeder MD
     4. Long F
     5. Yang JS
     6. He Y
     7. Ding P
     8. Kao JC
     9. Wu GY
     10. Peng H
     11. Myers G
     12. Lee T

     (2013) Clonal development and organization of the adult Drosophila central brain

     Current Biology 23:633–643.

     https://doi.org/10.1016/j.cub.2013.02.057

     • Google Scholar
113. 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:.

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

     • Google Scholar
114. 1. Yu Y
     2. Peng HC

     (2011)

     Automated high speed stitching of large 3d microscopic images

     2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 238–241.

     • Google Scholar
115. 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

     • Google Scholar

Author details

1. Yoshinori Aso

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

   Contribution

   YA, Conceived and designed the study, conducted split-GAL4 screening, acquired confocal images, analyzed and interpreted data and wrote the article

   For correspondence

   asoy@janelia.hhmi.org

   Competing interests

   The authors declare that no competing interests exist.

2. Daisuke Hattori

   Howard Hughes Medical Institute, Columbia University, New York, United States

   Contribution

   DH, Designed and carried out the PA-GFP experiments, analyzed data and wrote the article

   Competing interests

   The authors declare that no competing interests exist.

3. Yang Yu

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

   Contribution

   YY, Developed image alignment software

   Competing interests

   The authors declare that no competing interests exist.

4. Rebecca M Johnston

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

   Contribution

   RMJ, Prepared samples and acquired confocal images

   Competing interests

   The authors declare that no competing interests exist.

5. Nirmala A Iyer

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

   Contribution

   NAI, Prepared samples and acquired confocal images

   Competing interests

   The authors declare that no competing interests exist.

6. Teri-TB Ngo

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

   Contribution

   T-TBN, Conducted split-GAL4 screening and acquired confocal images

   Competing interests

   The authors declare that no competing interests exist.

7. Heather Dionne

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

   Contribution

   HD, Made molecular constructs

   Competing interests

   The authors declare that no competing interests exist.

8. LF Abbott

   1. Department of Neuroscience, College of Physicians and Surgeons, Columbia University, New York, United States
   2. Department of Physiology and Cellular Biophysics, College of Physicians and Surgeons, Columbia University, New York, United States

   Contribution

   LFA, Analyzed and interpreted data and wrote the article

   Competing interests

   The authors declare that no competing interests exist.

9. Richard Axel

   1. Howard Hughes Medical Institute, Columbia University, New York, United States
   2. Department of Neuroscience, College of Physicians and Surgeons, Columbia University, New York, United States
   3. Department of Biochemistry and Molecular Biophysics, College of Physicians and Surgeons, Columbia University, New York, United States

   Contribution

   RA, Analyzed and interpreted data and wrote the article

   Competing interests

   The authors declare that no competing interests exist.

10. Hiromu Tanimoto

    1. Tohuku University Graduate School of Life Sciences, Sendai, Japan
    2. Max-Planck Institute of Neurobiology, Martinsried, Germany

    Contribution

    HT, Conceived and designed the study, analysis and interpretation of data, drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Gerald M Rubin

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

    Contribution

    GMR, Conceived and designed the study, analyzed and interpreted data and wrote the article

    For correspondence

    rubing@janelia.hhmi.org

    Competing interests

    The authors declare that no competing interests exist.

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