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.

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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

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