Neuronal dendrites carry out computations through compartmentalized signaling, while axons have long been considered to carry signals to their terminal fields relatively uniformly following spike initiation. However, anatomical and physiological compartmentalization of axons has been recently documented in neurons from worms through mammals (Boto et al., 2014; Cohn et al., 2015; Hendricks et al., 2012; Rowan et al., 2016). How axonal compartmentalization influences information flow across neuronal circuits and modulates behavioral outcomes is not understood. One functional role for axonal compartmentalization may be to enable localized, presynaptic plasticity to alter output from select axon compartments in a flexible, experience-dependent manner. This would vastly enhance the neuron’s flexibility and computational capabilities. A potential function of such compartmentalization would allow independent modulation of axonal segments and/or synaptic release sites by biologically salient events, such as sensory stimuli that drive learning.

The anatomical organization of the Drosophila mushroom body (MB) makes it an exemplary test bed to study how sensory information is processed during learning and rerouted to alter behavioral outcomes. The MB encodes odor in sparse representations across the intrinsic MB neurons, Kenyon cells (KCs), which are arranged in several parallel sets. They project axons in fasciculated bundles into several anatomically distinct, but spatially adjacent lobes (α/β, α′/β′, and γ) (Crittenden et al., 1998). KC axons are longitudinally subdivided into discrete tiled compartments (Aso et al., 2014a). Each compartment receives afferent neuromodulatory input from unique dopaminergic neurons (Aso et al., 2014a; Mao and Davis, 2009), and innervates unique efferent mushroom body output neurons (MBONs) (Aso et al., 2014a). Each set of dopaminergic neurons plays an individual role in learning, with some modulating aversive learning (Mao and Davis, 2009; Schroll et al., 2006; Schwaerzel et al., 2003), others modulating reward learning (Liu et al., 2012; Yamagata et al., 2015), and a third class modulating memory strength without driving behavioral valence (Boto et al., 2019). Similarly, each MBON has a unique effect on behavioral approach and avoidance, with some biasing the animal to approach, others biasing the animal to avoidance, and some having no known effect (Aso et al., 2014b; Perisse et al., 2016; Plaçais et al., 2013; Séjourné et al., 2011).

A major question in learning and memory is how presynaptic plasticity contributes to reweighting the flow of sensory signals across each of the downstream ‘approach’ or ‘avoidance’ circuits, altering action selection and memory retrieval. In naïve conditions, Drosophila dopaminergic circuits modulate cAMP in a compartmentalized fashion along the MB axons (Boto et al., 2014). This compartmentalized dopaminergic signaling can independently modulate Ca²⁺ responses in each compartment, as well as the responses of the downstream valence-coding MBONs (Cohn et al., 2015). Presynaptic plasticity within each KC compartment likely contributes to the changes in downstream MBON activity that guide learned behavioral responses (Zhang et al., 2019). However, manipulation of the ‘aversive’ protocerebral posterior lateral 1 (PPL1) dopaminergic neurons does not detectably alter Ca²⁺ signals in KCs (Boto et al., 2019; Hige et al., 2015a). Furthermore, Ca²⁺ responses in KCs are uniformly potentiated across compartments with appetitive classical conditioning protocols and unaltered in KCs following aversive protocols (Louis et al., 2018). This raises the question of how local, compartmentalized synaptic plasticity in KCs drives coherent changes across the array of downstream MBONs to modulate action selection during memory retrieval. Learning/dopamine-induced plasticity has been demonstrated in the MBONs (Berry et al., 2018; Hige et al., 2015a; Hige et al., 2015b; Owald et al., 2015), with dopamine also acting directly on them (Takemura et al., 2017) (in addition to KCs). Feedforward inhibition among MBONs that drive opposing behavioral outcomes provides a mechanism explaining how bidirectional valence coding in MBONs could be generated, with or without bidirectional presynaptic plasticity (Perisse et al., 2016). The compartmentalized, dopamine-dependent plasticity in KCs and the necessity for dopamine receptors and downstream signaling molecules in the intrinsic KCs points to a potential presynaptic contribution (Kim et al., 2007; McGuire et al., 2003; Zars et al., 2000). Thus, compartmentalized presynaptic plasticity could contribute to reweighting the flow of olfactory information to downstream circuits.

Here, we describe how learning alters the flow of information through the MB via alteration of synaptic release of the KC neurotransmitter acetylcholine (ACh) (Barnstedt et al., 2016), using a genetically encoded indicator of ACh neurotransmission. The data reveal that appetitive and aversive learning alter compartmentalized acetylcholine release in distinct spatial patterns, with differing molecular mechanisms, coherently reweighting the flow of olfactory information across the ensemble of downstream neurons that mediate action selection.

Compartmentalized plasticity in neurotransmitter release expands the potential computational capacity of learning circuits. It allows a set of odor-coding MB neurons to bifurcate their output to different downstream approach- and avoidance-driving downstream output neurons, independently modulating the synaptic connections to alter action selection based on the conditioned value of olfactory stimuli. The KCs modify the encoded value of olfactory stimuli through bidirectional plasticity in odor responses, which vary in a compartment-specific manner along the length of the axons. These changes were observed following pairing an olfactory CS with gustatory/somatosensory US (sucrose feeding or electric shock) in vivo. The CS+ and CS- drive unique patterns of plasticity in each compartment, demonstrating that olfactory stimuli are reweighted differently across compartments following learning, depending on the temporal associations of the stimuli. Different molecular mechanisms govern the potentiation of trained odor responses (Ca_V2/Cac) and maintenance of responsivity over time (IP₃R). Finally, one set of γ output neurons, γ3/γ3β′1, is important for appetitive short-term memory.

Learning-induced plasticity of ACh release in the MB was bidirectional within the compartment, depending on the valence of the US, and was coherent with the valence of the MBON downstream of the compartment. Notably, the γ2 and γ3 MB compartments, which relay information to approach-promoting MBONs (Aso et al., 2014b), exhibited plasticity that was coherent with promoting behavioral approach following appetitive conditioning and avoidance after aversive conditioning. There was an increase in the relative CS+:CS- ACh responses after appetitive conditioning, and conversely reduced CS+:CS- ACh responses following aversive conditioning. For this study, we focused on the time point 5 min following conditioning, which is consistent with behavioral short-term memory. Aversive conditioning was previously reported to decrease neurotransmitter release from KCs (Zhang and Roman, 2013; Zhang et al., 2019). Indirect evidence, via Ca²⁺ imaging in presynaptic KCs, suggested that increases in presynaptic neurotransmission could also be associated with learning. Pairing odor with stimulation of appetitive PAM dopaminergic neurons potentiates odor-evoked cytosolic Ca²⁺ transients across the KC compartments (Boto et al., 2014). Appetitive conditioning increases odor-evoked Ca²⁺ transients across KC compartments (Louis et al., 2018). Stimulation of dopaminergic circuits associated with reward learning potentiate MB γ4 connections with the respective γ4 MBON (Handler et al., 2019). We did not detect a statistically significant effect in γ4 with appetitive or aversive classical conditioning, though the CS+ and CS- trended in the same direction as the adjacent γ5 compartment following conditioning. Overall, the present data demonstrate that there are bidirectional changes in neurotransmitter (ACh) release from MB compartments following appetitive vs aversive learning and provide a window into the spatial patterns of plasticity across compartments following associative learning.

Behavioral alterations following conditioning involve changes in responses among the MBONs. As the KCs provide presynaptic olfactory input to the MBONs, it was a logical a priori assumption that presynaptic plasticity in the KCs could be altered in a compartmental manner and contribute to the changes in MBON responses after conditioning. Yet data from previous Ca²⁺ imaging experiments have not completely supported this model. Compartmentalized effects have been observed in KCs with non-associative learning protocols (Cohn et al., 2015) and within the γ4 compartment following associative learning (Handler et al., 2019). In contrast, classical conditioning produces no compartmentalized differences in odor-evoked Ca²⁺ responses. Appetitive conditioning with odor + sucrose pairing increases odor-evoked cytosolic Ca²⁺ transients in KCs across the γ lobe compartments (Louis et al., 2018). Aversive conditioning produces no net change across the compartments (Louis et al., 2018), but alters synapse-specific Ca²⁺ responses at the individual bouton level (Bilz et al., 2020). If the compartmental effects of conditioning (observed with Ca²⁺ imaging) in KCs drove a proportional change in neurotransmitter release, both the approach- and avoidance-promoting MBONs would be simultaneously potentiated. Extracellular influx of Ca²⁺ through voltage-gated calcium channels is a primary driver of neurotransmitter release; however, there are multiple sources of Ca²⁺ in the cytosol that could contribute to the GCaMP signals (Grienberger and Konnerth, 2012). A major conclusion of the present study is that learning drives compartmentalized plasticity in neurotransmitter release that is coherent with the behavioral valence of the corresponding MBON.

At least two major molecular mechanisms govern the spatial patterns of plasticity across the MB compartments: a Cac-dependent CS+ potentiation and an IP₃R-dependent maintenance of sensory responses over trials/time. This suggests that different sources of Ca²⁺ play different roles in regulating KC synaptic responses. Cac is the pore-forming subunit of the voltage-sensitive, presynaptic Ca_V2 Ca²⁺ channel in Drosophila. Ca_V2 channels regulate several forms of synaptic plasticity, including paired-pulse facilitation, homeostatic plasticity, and long-term potentiation (Frank et al., 2006; Inchauspe et al., 2004; Nanou et al., 2016). Our data suggest that these channels regulate the spatial patterns of learning-induced plasticity in the MB unidirectionally (from baseline), with Cac underlying potentiation but not depression. Ca_V2 channel activity is modulated by presynaptic calcium and G protein-coupled receptor activity (Zamponi and Currie, 2013), and channel localization in the active zone dynamically regulates synaptic strength (Gratz et al., 2019; Lübbert et al., 2019). Thus, Cac insertion into, or increased clustering within, the active zones may underlie learning-induced potentiation (e.g. in the γ1-γ2 compartments following appetitive conditioning). Conditional knockdown of Cac, which reduced Cac levels by ~29%, impaired this potentiation, likely by decreasing the number of available channels for modulation. Baseline stimulus-evoked neurotransmitter release was maintained during Cac knockdown, mediated either by the significant residual Cac expression or compensation by other intracellular Ca²⁺ channels/sources. In contrast to the Cac effect on potentiation, IP₃R was necessary to maintain normal odor responsivity when odors were presented repeatedly across multiple trials (whether those were pre/post trials in the conditioning protocol or 10× odor presentations in the adaptation protocol). This is broadly consistent with the temporal role of IP₃R in maintenance of presynaptic homeostatic potentiation at the neuromuscular junction (James et al., 2019). In addition, dopaminergic circuits associated with reward learning drive release of Ca²⁺ from the endoplasmic reticulum when activated with KCs in a backward pairing paradigm ex vivo, potentiating MB γ4 connections with the respective γ4 MBON (Handler et al., 2019). This is consistent with a role for ER calcium in positively regulating synaptic strength.

We observed potentiation and depression of ACh release across multiple MB compartments following conditioning, providing a presynaptic mechanism that potentially contributes to shaping conditioned MBON responses. Importantly, by comparing the CS+ and CS- responses to those of untrained odors, we ascribed plasticity to potentiation or depression (accounting for any non-associative olfactory adaptation) within each compartment. This is relevant for modeling efforts, where it has been unclear whether to include potentiation (along with depression) in the learning rule(s) at KC-MBON synapses (Abdelrahman et al., 2021; Bennett et al., 2021; Jiang and Litwin-Kumar, 2021; Springer and Nawrot, 2021). In addition, the experiments revealed an additional layer of spatial regulation in the γ1–γ3 compartments: a gradient of CS+ potentiation to CS- depression following appetitive conditioning. Specifically, the CS+/CS- relationship changed in a linear gradient down the γ1–γ3 compartments following appetitive conditioning. Appetitive conditioning increased CS+ responses in the γ1 compartment, while decreasing the CS- responses in the γ3 compartment. The γ2 compartment yielded a mix of these responses. These patterns of plasticity have the net effect of increasing the relative response to the CS+ odor (↑CS+:CS-). Since the MBONs postsynaptic to these compartments drive behavioral approach (Aso et al., 2014b), this would bias the animal to approach the CS+ if it encountered both odors simultaneously. Such a situation occurs at the choice point of a T-maze during retrieval in a classical conditioning assay. The CS+ and CS- produce different patterns of plasticity at different loci (e.g. γ1 vs γ3), which presumably coordinate to regulate behavior via temporal integration of the odor and US cues (Berry et al., 2018; Handler et al., 2019; Jacob and Waddell, 2020; König et al., 2018; Tanimoto et al., 2004; Tully and Quinn, 1985). The CS+ is temporally contiguous with the US, while the CS- is nonoverlapping. Therefore, the timing of CS/US pairing drives plasticity differently in each compartment. These patterns of plasticity presumably coordinate to regulate memory formation and action selection during retrieval. For instance, while the γ1–γ3 compartments exhibited ↑CS+:CS- following appetitive conditioning, the γ5 compartment exhibited plasticity in the opposite direction: decreasing the relative response to the CS+ odor (↓CS+:CS-). As the γ5 compartment is presynaptic to an avoidance-promoting MBON, this plasticity pattern would coherently contribute to biasing the animal toward CS+ approach (reducing CS+ avoidance). Thus, it would work in concert with the plasticity in γ1–γ3 to bias the animal toward behavioral approach. Overall, plasticity is regulated in each MB compartment individually by the timing of events and the valence of the US, with the changes coordinated across multiple compartments to coherently drive behavior.

Behaviorally, MBONs innervating the γ lobe variably drive behavioral approach or avoidance when stimulated (Aso et al., 2014b). Despite the approach-promoting valence of the γ2α′one and γ3/γ3β′1 MBONs, among them, only the γ3/γ3β′1 MBONs produced a loss-of-function phenotype in behavioral appetitive conditioning. This suggests that redundancy and/or different weighting across approach promoting MBONs renders the system resilient to silencing some of them. A previous study found effects of blocking the γ2α′1 MBONs, though not γ3/γ3β′1 MBONs, when blocking individual steps of memory processing (acquisition, retention, and/or retrieval) with a 1 hr appetitive memory protocol (Ichinose et al., 2021). This suggests that the different MBONs have differing roles across time, with some redundancy in appetitive processing. Blocking synaptic output of γ3/γ3β′1 MBONs reduced appetitive conditioning performance in our experiments immediately following conditioning, suggesting that these neurons play a specific role in appetitive short-term memory.

The present and previous studies suggest that alterations of MBON activity following learning are the product of both presynaptic and postsynaptic plasticity at the KC-MBON synapses, as well as feedforward inhibition (Hige et al., 2015a; Perisse et al., 2016; Pribbenow et al., 2021). Blocking synaptic output from KCs impairs the acquisition of appetitive memories (30–60 min after conditioning), suggesting a role for postsynaptic plasticity (Pribbenow et al., 2021; Schwaerzel et al., 2003). However, this does not rule out presynaptic plasticity, as blocking KC output (with R13F02) leaves signaling from reinforcing dopaminergic neurons partially intact (Cervantes-Sandoval et al., 2017), which likely shapes the presynaptic KC responses via heterosynaptic plasticity. At the circuit level, polysynaptic inhibition can convert depression from select MB compartments into potentiation in MBONs following learning; in one established example, reduction of odor-evoked responses in the GABAergic γ1pedc MBON following aversive conditioning disinhibits the downstream γ5β′2 a MBON (Owald et al., 2015; Perisse et al., 2016).

KC-MBON synapses represent one node of learning-related plasticity, which is distributed across multiple sites during learning. Short-term memory-related plasticity has been observed in multiple olfactory neurons, such as the antennal lobes (Yu et al., 2004) and KCs (Louis et al., 2018; Wang et al., 2008). In addition, connectomics studies have revealed complex connectivity within and beyond the MB, which is a multi-layered network including circuit motifs that influence the propagation of information and generation of plasticity during learning. Such connections include recurrent feedback (Aso et al., 2014a; Ichinose et al., 2015; Li et al., 2020; Otto et al., 2020; Scaplen et al., 2021; Takemura et al., 2017; Zhao et al., 2018). Some of these recurrent connections are from cholinergic MBONs that synapse within the MB, which could have contributed to the ACh signals we observed in this study. For instance, the γ2α′1 MBON is a cholinergic MBON that sends ~6% of its output back to the γ lobe (Scheffer et al., 2020). Some of the recurrent connections are formed by dopaminergic neurons, such as the PAM γ4<γ1/y2 (Aso et al., 2014a; Li et al., 2020). In addition, reciprocal connections between KCs and dopaminergic neurons in the vertical lobes are necessary for memory retrieval (Cervantes-Sandoval et al., 2017). This adds another layer of recurrent circuitry that may participate in reinforcement during associative learning (Aso et al., 2014b; Ichinose et al., 2015; Ichinose et al., 2021). Across these circuits, some neurons corelease several neurotransmitters and act on an array of postsynaptic receptors, which contribute to plasticity distributed across multiple sites (Aso et al., 2019; Barnstedt et al., 2016; Berry et al., 2012; Bielopolski et al., 2019; Cohn et al., 2015; Handler et al., 2019; Haynes et al., 2015; Keene et al., 2004; Kim et al., 2007; Liu and Davis, 2009; Pribbenow et al., 2021; Schroll et al., 2006; Séjourné et al., 2011; Silva et al., 2015; Wu et al., 2013; Zhou et al., 2019).

Overall, plasticity between KCs and MBONs may guide behavior through biasing network activation to alter action selection in a probabilistic manner. Appetitive conditioning drives compartmentalized, presynaptic plasticity in KCs that correlates with postsynaptic changes in MBONs that guide learned behaviors. Prior studies documented only depression at these synapses at short time points following conditioning (Hige et al., 2015a; Zhang and Roman, 2013; Zhang et al., 2019). Here, we observed both potentiation and depression in ACh release in the MB, suggesting that bidirectional presynaptic plasticity modulates learned behaviors. These bidirectional changes likely integrate with plasticity at downstream circuit nodes that also undergo learning-induced plasticity to produce network-level alterations in odor responses across the olfactory pathway following salient events. Thus, plasticity in ACh release from KCs functions to modulate responsivity to olfactory stimuli features across graded plasticity maps down the MB axons.

Raw data have been deposited to Dryad under https://doi.org/10.5061/dryad.dfn2z353h.

1. 1. Tomchik SM

   (2022) Dryad Digital Repository

   Data from: Associative learning drives longitudinally-graded presynaptic plasticity of neurotransmitter release along axonal compartments.

   https://doi.org/10.5061/dryad.dfn2z353h

1. 1. Abdelrahman NY
   2. Vasilaki E
   3. Lin AC

   (2021) Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body

   PNAS 118:e2102158118.

   https://doi.org/10.1073/pnas.2102158118

   • PubMed
   • 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

   • PubMed
   • Google Scholar
3. 1. Aso Y
   2. Hattori D
   3. Yu Y
   4. Johnston RM
   5. Iyer NA
   6. Ngo TT
   7. Dionne H
   8. Abbott L
   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:e1.

   https://doi.org/10.7554/eLife.04577.001

   • Google Scholar
4. 1. Aso Y
   2. Sitaraman D
   3. Ichinose T
   4. Kaun KR
   5. Vogt K
   6. Belliart-Guerin G
   7. Placais PY
   8. Robie AA
   9. Yamagata N
   10. Schnaitmann C

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

   eLife 3:e1.

   https://doi.org/10.7554/eLife.04580.001

   • Google Scholar
5. 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
6. 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
7. 1. Bennett JEM
   2. Philippides A
   3. Nowotny T

   (2021) Learning with reinforcement prediction errors in a model of the Drosophila mushroom body

   Nature Communications 12:2569.

   https://doi.org/10.1038/s41467-021-22592-4

   • PubMed
   • Google Scholar
8. 1. Berry JA
   2. Cervantes-Sandoval I
   3. Nicholas EP
   4. Davis RL

   (2012) Dopamine is required for learning and forgetting in Drosophila

   Neuron 74:530–542.

   https://doi.org/10.1016/j.neuron.2012.04.007

   • PubMed
   • Google Scholar
9. 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
10. 1. Bielopolski N
    2. Amin H
    3. Apostolopoulou AA
    4. Rozenfeld E
    5. Lerner H
    6. Huetteroth W
    7. Lin AC
    8. Parnas M

    (2019) Inhibitory muscarinic acetylcholine receptors enhance aversive olfactory learning in adult Drosophila

    eLife 8:e48264.

    https://doi.org/10.7554/eLife.48264

    • PubMed
    • Google Scholar
11. 1. Bilz F
    2. Geurten BRH
    3. Hancock CE
    4. Widmann A
    5. Fiala A

    (2020) Visualization of a Distributed Synaptic Memory Code in the Drosophila Brain

    Neuron 106:963–976.

    https://doi.org/10.1016/j.neuron.2020.03.010

    • PubMed
    • Google Scholar
12. 1. Boto T
    2. Louis T
    3. Jindachomthong K
    4. Jalink K
    5. Tomchik SM

    (2014) Dopaminergic Modulation of cAMP Drives Nonlinear Plasticity across the Drosophila Mushroom Body Lobes

    Current Biology 24:822–831.

    https://doi.org/10.1016/j.cub.2014.03.021

    • PubMed
    • Google Scholar
13. 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
14. 1. Brusich DJ
    2. Spring AM
    3. Frank CA

    (2015) A single-cross, RNA interference-based genetic tool for examining the long-term maintenance of homeostatic plasticity

    Frontiers in Cellular Neuroscience 9:107.

    https://doi.org/10.3389/fncel.2015.00107

    • PubMed
    • Google Scholar
15. 1. Cervantes-Sandoval I
    2. Phan A
    3. Chakraborty M
    4. Davis RL

    (2017) Reciprocal synapses between mushroom body and dopamine neurons form a positive feedback loop required for learning

    eLife 6:e23789.

    https://doi.org/10.7554/eLife.23789

    • PubMed
    • Google Scholar
16. 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
17. 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 (Cold Spring Harbor, N.Y.) 5:38–51.

    • PubMed
    • Google Scholar
18. 1. Dietzl G
    2. Chen D
    3. Schnorrer F
    4. Su K-C
    5. Barinova Y
    6. Fellner M
    7. Gasser B
    8. Kinsey K
    9. Oppel S
    10. Scheiblauer S
    11. Couto A
    12. Marra V
    13. Keleman K
    14. Dickson BJ

    (2007) A genome-wide transgenic RNAi library for conditional gene inactivation in Drosophila

    Nature 448:151–156.

    https://doi.org/10.1038/nature05954

    • PubMed
    • Google Scholar
19. 1. Frank CA
    2. Kennedy MJ
    3. Goold CP
    4. Marek KW
    5. Davis GW

    (2006) Mechanisms underlying the rapid induction and sustained expression of synaptic homeostasis

    Neuron 52:663–677.

    https://doi.org/10.1016/j.neuron.2006.09.029

    • PubMed
    • Google Scholar
20. 1. Gratz SJ
    2. Goel P
    3. Bruckner JJ
    4. Hernandez RX
    5. Khateeb K
    6. Macleod GT
    7. Dickman D
    8. O’Connor-Giles KM

    (2019) Endogenous Tagging Reveals Differential Regulation of Ca(2+) Channels at Single Active Zones during Presynaptic Homeostatic Potentiation and Depression

    The Journal of Neuroscience 39:2416–2429.

    https://doi.org/10.1523/JNEUROSCI.3068-18.2019

    • PubMed
    • Google Scholar
21. 1. Grienberger C
    2. Konnerth A

    (2012) Imaging calcium in neurons

    Neuron 73:862–885.

    https://doi.org/10.1016/j.neuron.2012.02.011

    • PubMed
    • Google Scholar
22. 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
23. 1. Haynes PR
    2. Christmann BL
    3. Griffith LC

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

    eLife 4:3868.

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

    • PubMed
    • Google Scholar
24. 1. Hendricks M
    2. Ha H
    3. Maffey N
    4. Zhang Y

    (2012) Compartmentalized calcium dynamics in a C. elegans interneuron encode head movement

    Nature 487:99–103.

    https://doi.org/10.1038/nature11081

    • PubMed
    • Google Scholar
25. 1. Hidalgo S
    2. Campusano JM
    3. Hodge JJL

    (2021) The Drosophila ortholog of the schizophrenia-associated CACNA1A and CACNA1B voltage-gated calcium channels regulate memory, sleep and circadian rhythms

    Neurobiology of Disease 155:105394.

    https://doi.org/10.1016/j.nbd.2021.105394

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

    (2015a) 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
27. 1. Hige T
    2. Aso Y
    3. Rubin GM
    4. Turner GC

    (2015b) Plasticity-driven individualization of olfactory coding in mushroom body output neurons

    Nature 526:258–262.

    https://doi.org/10.1038/nature15396

    • PubMed
    • Google Scholar
28. 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
29. 1. Ichinose T
    2. Kanno M
    3. Wu H
    4. Yamagata N
    5. Sun H
    6. Abe A
    7. Tanimoto H

    (2021) Mushroom body output differentiates memory processes and distinct memory-guided behaviors

    Current Biology 31:1294–1302.

    https://doi.org/10.1016/j.cub.2020.12.032

    • PubMed
    • Google Scholar
30. 1. Inchauspe CG
    2. Martini FJ
    3. Forsythe ID
    4. Uchitel OD

    (2004) Functional compensation of P/Q by N-type channels blocks short-term plasticity at the calyx of Held presynaptic terminal

    The Journal of Neuroscience 24:10379–10383.

    https://doi.org/10.1523/JNEUROSCI.2104-04.2004

    • PubMed
    • Google Scholar
31. 1. Ishikawa T
    2. Kaneko M
    3. Shin HS
    4. Takahashi T

    (2005) Presynaptic N-type and P/Q-type Ca2+ channels mediating synaptic transmission at the calyx of Held of mice

    The Journal of Physiology 568:199–209.

    https://doi.org/10.1113/jphysiol.2005.089912

    • PubMed
    • Google Scholar
32. 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
33. 1. James TD
    2. Zwiefelhofer DJ
    3. Frank CA

    (2019) Maintenance of homeostatic plasticity at the Drosophila neuromuscular synapse requires continuous IP3-directed signaling

    eLife 8:e39643.

    https://doi.org/10.7554/eLife.39643

    • PubMed
    • Google Scholar
34. 1. Jenett A
    2. Rubin GM
    3. Ngo T-TB
    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 SCB
    24. Li H-H
    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
35. 1. Jiang L
    2. Litwin-Kumar A

    (2021) Models of heterogeneous dopamine signaling in an insect learning and memory center

    PLOS Computational Biology 17:e1009205.

    https://doi.org/10.1371/journal.pcbi.1009205

    • PubMed
    • Google Scholar
36. 1. Jing M
    2. Zhang P
    3. Wang G
    4. Feng J
    5. Mesik L
    6. Zeng J
    7. Jiang H
    8. Wang S
    9. Looby JC
    10. Guagliardo NA
    11. Langma LW
    12. Lu J
    13. Zuo Y
    14. Talmage DA
    15. Role LW
    16. Barrett PQ
    17. Zhang LI
    18. Luo M
    19. Song Y
    20. Zhu JJ
    21. Li Y

    (2018) A genetically encoded fluorescent acetylcholine indicator for in vitro and in vivo studies

    Nature Biotechnology 36:726–737.

    https://doi.org/10.1038/nbt.4184

    • PubMed
    • Google Scholar
37. 1. Jing M
    2. Li Y
    3. Zeng J
    4. Huang P
    5. Skirzewski M
    6. Kljakic O
    7. Peng W
    8. Qian T
    9. Tan K
    10. Zou J
    11. Trinh S
    12. Wu R
    13. Zhang S
    14. Pan S
    15. Hires SA
    16. Xu M
    17. Li H
    18. Saksida LM
    19. Prado VF
    20. Bussey TJ
    21. Prado MAM
    22. Chen L
    23. Cheng H
    24. Li Y

    (2020) An optimized acetylcholine sensor for monitoring in vivo cholinergic activity

    Nature Methods 17:1139–1146.

    https://doi.org/10.1038/s41592-020-0953-2

    • PubMed
    • Google Scholar
38. 1. Keene AC
    2. Stratmann M
    3. Keller A
    4. Perrat PN
    5. Vosshall LB
    6. Waddell S

    (2004) Diverse odor-conditioned memories require uniquely timed dorsal paired medial neuron output

    Neuron 44:521–533.

    https://doi.org/10.1016/j.neuron.2004.10.006

    • PubMed
    • Google Scholar
39. 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

    • PubMed
    • Google Scholar
40. 1. Kitamoto T

    (2001) Conditional modification of behavior in Drosophila by targeted expression of a temperature-sensitive shibire allele in defined neurons

    Journal of Neurobiology 47:81–92.

    https://doi.org/10.1002/neu.1018

    • PubMed
    • Google Scholar
41. 1. König C
    2. Khalili A
    3. Ganesan M
    4. Nishu AP
    5. Garza AP
    6. Niewalda T
    7. Gerber B
    8. Aso Y
    9. Yarali A

    (2018) Reinforcement signaling of punishment versus relief in fruit flies

    Learning & Memory (Cold Spring Harbor, N.Y.) 25:247–257.

    https://doi.org/10.1101/lm.047308.118

    • PubMed
    • Google Scholar
42. 1. Li F
    2. Lindsey JW
    3. Marin EC
    4. Otto N
    5. Dreher M
    6. Dempsey G
    7. Stark I
    8. Bates AS
    9. Pleijzier MW
    10. Schlegel P
    11. Nern A
    12. Takemura S-Y
    13. Eckstein N
    14. Yang T
    15. Francis A
    16. Braun A
    17. Parekh R
    18. Costa M
    19. Scheffer LK
    20. Aso Y
    21. Jefferis GS
    22. Abbott LF
    23. Litwin-Kumar A
    24. Waddell S
    25. Rubin GM

    (2020) The connectome of the adult Drosophila mushroom body provides insights into function

    eLife 9:e62576.

    https://doi.org/10.7554/eLife.62576

    • PubMed
    • Google Scholar
43. 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
44. 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
45. 1. Louis T
    2. Stahl A
    3. Boto T
    4. Tomchik SM

    (2018) Cyclic AMP-dependent plasticity underlies rapid changes in odor coding associated with reward learning

    PNAS 115:E448–E457.

    https://doi.org/10.1073/pnas.1709037115

    • PubMed
    • Google Scholar
46. 1. Lübbert M
    2. Goral RO
    3. Keine C
    4. Thomas C
    5. Guerrero-Given D
    6. Putzke T
    7. Satterfield R
    8. Kamasawa N
    9. Young SM Jr

    (2019) CaV2.1 alpha1 Subunit Expression Regulates Presynaptic CaV2.1 Abundance and Synaptic Strength at a Central Synapse

    Neuron 101:260–273.

    https://doi.org/10.1016/j.neuron.2018.11.028

    • Google Scholar
47. 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
48. 1. McGuire SE
    2. Le PT
    3. Davis RL

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

    Science (New York, N.Y.) 293:1330–1333.

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

    • PubMed
    • Google Scholar
49. 1. McGuire SE
    2. Le PT
    3. Osborn AJ
    4. Matsumoto K
    5. Davis RL

    (2003) Spatiotemporal rescue of memory dysfunction in Drosophila

    Science (New York, N.Y.) 302:1765–1768.

    https://doi.org/10.1126/science.1089035

    • PubMed
    • Google Scholar
50. 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
51. 1. Müller M
    2. Davis GW

    (2012) Transsynaptic control of presynaptic Ca(2)(+) influx achieves homeostatic potentiation of neurotransmitter release

    Current Biology 22:1102–1108.

    https://doi.org/10.1016/j.cub.2012.04.018

    • PubMed
    • Google Scholar
52. 1. Nanou E
    2. Scheuer T
    3. Catterall WA

    (2016) Calcium sensor regulation of the CaV2.1 Ca2+ channel contributes to long-term potentiation and spatial learning

    PNAS 113:13209–13214.

    https://doi.org/10.1073/pnas.1616206113

    • PubMed
    • Google Scholar
53. 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 GSXE
    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
54. 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
55. 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
56. 1. Perkins LA
    2. Holderbaum L
    3. Tao R
    4. Hu Y
    5. Sopko R
    6. McCall K
    7. Yang-Zhou D
    8. Flockhart I
    9. Binari R
    10. Shim H-S
    11. Miller A
    12. Housden A
    13. Foos M
    14. Randkelv S
    15. Kelley C
    16. Namgyal P
    17. Villalta C
    18. Liu L-P
    19. Jiang X
    20. Huan-Huan Q
    21. Wang X
    22. Fujiyama A
    23. Toyoda A
    24. Ayers K
    25. Blum A
    26. Czech B
    27. Neumuller R
    28. Yan D
    29. Cavallaro A
    30. Hibbard K
    31. Hall D
    32. Cooley L
    33. Hannon GJ
    34. Lehmann R
    35. Parks A
    36. Mohr SE
    37. Ueda R
    38. Kondo S
    39. Ni J-Q
    40. Perrimon N

    (2015) The Transgenic RNAi Project at Harvard Medical School: Resources and Validation

    Genetics 201:843–852.

    https://doi.org/10.1534/genetics.115.180208

    • PubMed
    • Google Scholar
57. 1. Pfeiffer BD
    2. Ngo T-TB
    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

    • PubMed
    • Google Scholar
58. 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 Drososphila

    Cell Reports 5:769–780.

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

    • PubMed
    • Google Scholar
59. 1. Pribbenow C
    2. Chen Y
    3. Heim MM
    4. Laber D
    5. Reubold S
    6. Reynolds E
    7. Balles I
    8. Grimalt RS
    9. Rauch C
    10. Rösner J
    11. Alquicira TFV
    12. Owald D

    (2021) Postsynaptic plasticity of cholinergic synapses underlies the induction and expression of appetitive memories in Drosophila

    Neuroscience 1:e450776.

    https://doi.org/10.1101/2021.07.01.450776

    • Google Scholar
60. 1. Rowan MJM
    2. DelCanto G
    3. Yu JJ
    4. Kamasawa N
    5. Christie JM

    (2016) Synapse-Level Determination of Action Potential Duration by K(+) Channel Clustering in Axons

    Neuron 91:370–383.

    https://doi.org/10.1016/j.neuron.2016.05.035

    • PubMed
    • Google Scholar
61. 1. Sayin S
    2. De Backer J-F
    3. Siju KP
    4. Wosniack ME
    5. Lewis LP
    6. Frisch L-M
    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 GSXE
    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
62. 1. Scaplen KM
    2. Talay M
    3. Fisher JD
    4. Cohn R
    5. Sorkaç A
    6. Aso Y
    7. Barnea G
    8. Kaun KR

    (2021) Transsynaptic mapping of Drosophila mushroom body output neurons

    eLife 10:e63379.

    https://doi.org/10.7554/eLife.63379

    • PubMed
    • Google Scholar
63. 1. Scheffer LK
    2. Xu CS
    3. Januszewski M
    4. Lu Z
    5. Takemura S-Y
    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
64. 1. Schroll C
    2. Riemensperger T
    3. Bucher D
    4. Ehmer J
    5. Völler 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

    • PubMed
    • Google Scholar
65. 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.

    • PubMed
    • Google Scholar
66. 1. Séjourné J
    2. Plaçais P-Y
    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
67. 1. Silva B
    2. Molina-Fernández C
    3. Ugalde MB
    4. Tognarelli EI
    5. Angel C
    6. Campusano JM

    (2015) Muscarinic ACh Receptors Contribute to Aversive Olfactory Learning in Drosophila

    Neural Plasticity 2015:658918.

    https://doi.org/10.1155/2015/658918

    • PubMed
    • Google Scholar
68. 1. Springer M
    2. Nawrot MP

    (2021) A Mechanistic Model for Reward Prediction and Extinction Learning in the Fruit Fly

    ENeuro 8:ENEURO.0549-20.2021.

    https://doi.org/10.1523/ENEURO.0549-20.2021

    • PubMed
    • Google Scholar
69. 1. Takemura S-Y
    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 L-A
    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
70. 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
71. 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
72. 1. Taufiq AM
    2. Fujii S
    3. Yamazaki Y
    4. Sasaki H
    5. Kaneko K
    6. Li J
    7. Kato H
    8. Mikoshiba K

    (2005) Involvement of IP3 receptors in LTP and LTD induction in guinea pig hippocampal CA1 neurons

    Learning & Memory (Cold Spring Harbor, N.Y.) 12:594–600.

    https://doi.org/10.1101/lm.17405

    • PubMed
    • Google Scholar
73. 1. Tomchik SM

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

    The Journal of Neuroscience 33:2166–76a.

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

    • PubMed
    • Google Scholar
74. 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

    • PubMed
    • Google Scholar
75. 1. Wang Y
    2. Mamiya A
    3. Chiang A
    4. Zhong Y

    (2008) Imaging of an early memory trace in the Drosophila mushroom body

    The Journal of Neuroscience 28:4368–4376.

    https://doi.org/10.1523/JNEUROSCI.2958-07.2008

    • PubMed
    • Google Scholar
76. 1. Wu C-L
    2. Shih M-FM
    3. Lee P-T
    4. Chiang A-S

    (2013) An octopamine-mushroom body circuit modulates the formation of anesthesia-resistant memory in Drosophila

    Current Biology 23:2346–2354.

    https://doi.org/10.1016/j.cub.2013.09.056

    • PubMed
    • Google Scholar
77. 1. Wu JK
    2. Tai CY
    3. Feng KL
    4. Chen SL
    5. Chen CC
    6. Chiang AS

    (2017) Long-term memory requires sequential protein synthesis in three subsets of mushroom body output neurons in Drosophila

    Scientific Reports 7:7112.

    https://doi.org/10.1038/s41598-017-07600-2

    • PubMed
    • Google Scholar
78. Preprint

    1. Xu CS
    2. Januszewski M
    3. Lu Z
    4. Takemura SY
    5. Hayworth K
    6. Huang G
    7. Shinomiya K
    8. Maitin-Shepard J
    9. Ackerman D
    10. Berg S

    (2020) A Connectome and Analysis of the Adult Drosophila Central Brain

    bioRxiv.

    https://doi.org/10.1101/2020.04.07.030213

    • Google Scholar
79. 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
80. 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
81. 1. Yu D
    2. Ponomarev A
    3. Davis RL

    (2004) Altered representation of the spatial code for odors after olfactory classical conditioning; memory trace formation by synaptic recruitment

    Neuron 42:437–449.

    https://doi.org/10.1016/s0896-6273(04)00217-x

    • PubMed
    • Google Scholar
82. 1. Zamponi GW
    2. Currie KPM

    (2013) Regulation of Ca(V)2 calcium channels by G protein coupled receptors

    Biochimica et Biophysica Acta 1828:1629–1643.

    https://doi.org/10.1016/j.bbamem.2012.10.004

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

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

    Science (New York, N.Y.) 288:672–675.

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

    • PubMed
    • Google Scholar
84. 1. Zhang S
    2. Roman G

    (2013) Presynaptic inhibition of gamma lobe neurons is required for olfactory learning in Drosophila

    Current Biology 23:2519–2527.

    https://doi.org/10.1016/j.cub.2013.10.043

    • PubMed
    • Google Scholar
85. 1. Zhang X
    2. Noyes NC
    3. Zeng J
    4. Li Y
    5. Davis RL

    (2019) Aversive Training Induces Both Presynaptic and Postsynaptic Suppression in Drosophila

    The Journal of Neuroscience 39:9164–9172.

    https://doi.org/10.1523/JNEUROSCI.1420-19.2019

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

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

    eLife 7:e31425.

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

    • PubMed
    • Google Scholar
87. 1. Zhou M
    2. Chen N
    3. Tian J
    4. Zeng J
    5. Zhang Y
    6. Zhang X
    7. Guo J
    8. Sun J
    9. Li Y
    10. Guo A
    11. Li Y

    (2019) Suppression of GABAergic neurons through D2-like receptor secures efficient conditioning in Drosophila aversive olfactory learning

    PNAS 116:5118–5125.

    https://doi.org/10.1073/pnas.1812342116

    • PubMed
    • Google Scholar

Author details

1. Aaron Stahl

   Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3170-1101

2. Nathaniel C Noyes

   Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

   Contribution

   Data curation, Formal analysis, Investigation

   Competing interests

   No competing interests declared

3. Tamara Boto

   Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9974-3714

4. Valentina Botero

   Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9744-3929

5. Connor N Broyles

   Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2930-7343

6. Miao Jing

   Chinese Institute for Brain Research, Beijing, China

   Contribution

   Methodology, Resources

   Competing interests

   No competing interests declared

7. Jianzhi Zeng

   1. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
   2. State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing, China
   3. PKU IDG/McGovern Institute for Brain Research, Beijing, China

   Contribution

   Methodology, Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5380-6281

8. Lanikea B King

   Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Yulong Li

   1. Chinese Institute for Brain Research, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
   3. State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing, China
   4. PKU IDG/McGovern Institute for Brain Research, Beijing, China

   Contribution

   Funding acquisition, Methodology, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

10. Ronald L Davis

    Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

    Contribution

    Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Seth M Tomchik

    Department of Neuroscience, The Scripps Research Institute, Jupiter, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft

    For correspondence

    STomchik@scripps.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5686-0833

• 2,255

  views

• 338

  downloads

• 23

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.