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

PKCδ is an activator of neuronal mitochondrial metabolism that mediates the spacing effect on memory consolidation

Relevance-based selectivity and high energy cost are two distinct features of long-term memory (LTM) formation that warrant its default inhibition. Spaced repetition of learning is a highly conserved cognitive mechanism that can lift this inhibition. Here, we questioned how the spacing effect integrates experience selection and energy efficiency at the cellular and molecular levels. We showed in Drosophila that spaced training triggers LTM formation by extending over several hours an increased mitochondrial metabolic activity in neurons of the associative memory center, the mushroom bodies (MBs). We found that this effect is mediated by PKCδ, a member of the so-called 'novel PKC' family of enzymes, which uncovers the critical function of PKCδ in neurons as a regulator of mitochondrial metabolism for LTM. Additionally, PKCδ activation and translocation to mitochondria result from LTM-specific dopamine signaling on MB neurons. By bridging experience-dependent neuronal circuit activity with metabolic modulation of memory-encoding neurons, PKCδ signaling binds the cognitive and metabolic constraints underlying LTM formation into a unified gating mechanism.

Brief disruption of activity in a subset of dopaminergic neurons during consolidation impairs long-term memory by fragmenting sleep

Sleep disturbances are associated with poor long-term memory (LTM) formation, yet the underlying cell types and neural circuits involved have not been fully decoded. Dopamine neurons (DANs) are involved in memory processing at multiple stages. Here, using both male and female flies, Drosophila melanogaster , we show that, during the first few hours of memory consolidation, disruption of basal activity of a small subset of protocerebral anterior medial DANs (PAM-DANs), by either brief activation or inhibition of the two dorsal posterior medial (DPM) neurons, impairs 24 h LTM. Interestingly, these brief changes in activity using female flies result in sleep loss and fragmentation, especially at night. Pharmacological rescue of sleep after manipulation restores LTM. A specific subset of PAM-DANs (PAM-α1) that synapse onto DPM neurons specify the microcircuit that links sleep and memory. PAM-DANs, including PAM-α1, form functional synapses onto DPM mainly via multiple dopamine receptor subtypes. This PAM-α1 to DPM microcircuit exhibits a synchronized, transient, post-training increase in activity during the critical memory consolidation window, suggesting an effect of this microcircuit on maintaining the sleep necessary for LTM consolidation. Our results provide a new cellular and circuit basis for the complex relationship between sleep and memory.

Cell type-specific driver lines targeting the Drosophila central complex and their use to investigate neuropeptide expression and sleep regulation

The central complex (CX) plays a key role in many higher-order functions of the insect brain including navigation and activity regulation. Genetic tools for manipulating individual cell types, and knowledge of what neurotransmitters and neuromodulators they express, will be required to gain mechanistic understanding of how these functions are implemented. We generated and characterized split-GAL4 driver lines that express in individual or small subsets of about half of CX cell types. We surveyed neuropeptide and neuropeptide receptor expression in the central brain using fluorescent in situ hybridization. About half of the neuropeptides we examined were expressed in only a few cells, while the rest were expressed in dozens to hundreds of cells. Neuropeptide receptors were expressed more broadly and at lower levels. Using our GAL4 drivers to mark individual cell types, we found that 51 of the 85 CX cell types we examined expressed at least one neuropeptide and 21 expressed multiple neuropeptides. Surprisingly, all co-expressed a small neurotransmitter. Finally, we used our driver lines to identify CX cell types whose activation affects sleep, and identified other central brain cell types that link the circadian clock to the CX. The well-characterized genetic tools and information on neuropeptide and neurotransmitter expression we provide should enhance studies of the CX.

The Neural Correlations of Olfactory Associative Reward Memories in Drosophila

Advancing treatment to resolve human cognitive disorders requires a comprehensive understanding of the molecular signaling pathways underlying learning and memory. While most organ systems evolved to maintain homeostasis, the brain developed the capacity to perceive and adapt to environmental stimuli through the continuous modification of interactions within a gene network functioning within a broader neural network. This distinctive characteristic enables significant neural plasticity, but complicates experimental investigations. A thorough examination of the mechanisms underlying behavioral plasticity must integrate multiple levels of biological organization, encompassing genetic pathways within individual neurons, interactions among neural networks providing feedback on gene expression, and observable phenotypic behaviors. Model organisms, such as Drosophila melanogaster, which possess more simple and manipulable nervous systems and genomes than mammals, facilitate such investigations. The evolutionary conservation of behavioral phenotypes and the associated genetics and neural systems indicates that insights gained from flies are pertinent to understanding human cognition. Rather than providing a comprehensive review of the entire field of Drosophila memory research, we focus on olfactory associative reward memories and their related neural circuitry in fly brains, with the objective of elucidating the underlying neural mechanisms, thereby advancing our understanding of brain mechanisms linked to cognitive systems.

Natural variation in age-related dopamine neuron degeneration is glutathione dependent and linked to life span

Aging is the biggest risk factor for Parkinson's disease (PD), suggesting that age-related changes in the brain promote dopamine neuron vulnerability. It is unclear, however, whether aging alone is sufficient to cause significant dopamine neuron loss, and if so, how this intersects with PD-related neurodegeneration. Here, through examining a large collection of naturally varying Drosophila strains, we find a strong relationship between life span and age-related dopamine neuron loss. Strains with naturally short-lived animals exhibit a loss of dopamine neurons without generalized neurodegeneration, while animals from long-lived strains retain dopamine neurons across age. Metabolomic profiling reveals lower glutathione levels in short-lived strains which is associated with elevated levels of reactive oxygen species (ROS), sensitivity to oxidative stress, and vulnerability to silencing the familial PD gene parkin. Strikingly, boosting neuronal glutathione levels via glutamate-cysteine ligase (Gcl) overexpression is sufficient to normalize ROS levels, extend life span, and block dopamine neurons loss in short-lived backgrounds, demonstrating that glutathione deficiencies are central to neurodegenerative phenotypes associated with short longevity. These findings may be relevant to human PD pathogenesis, where glutathione depletion is reported to occur in the idiopathic PD patient brain through unknown mechanisms. Building on this, we find reduced expression of the Gcl catalytic subunit in both Drosophila strains vulnerable to age-related dopamine neuron loss and in the human brain from familial PD patients harboring the common LRRK2 G2019S mutation. Our study across Drosophila and human PD systems suggests that glutathione synthesis and levels play a conserved role in regulating age-related dopamine neuron health.

Connectomic reconstruction predicts visual features used for navigation

Many animals use visual information to navigate1-4, but how such information is encoded and integrated by the navigation system remains incompletely understood. In Drosophila melanogaster, EPG neurons in the central complex compute the heading direction5 by integrating visual input from ER neurons6-12, which are part of the anterior visual pathway (AVP)10,13-16. Here we densely reconstruct all neurons in the AVP using electron-microscopy data17. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons10,14,15, which connect the medulla in the optic lobe to the small unit of the anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons9,16, which connect the AOTUsu to the bulb neuropil; and ER neurons6-12, which connect the bulb to the EPG neurons. On the basis of morphologies, connectivity between neural classes and the locations of synapses, we identify distinct information channels that originate from four types of MeTu neurons, and we further divide these into ten subtypes according to the presynaptic connections in the medulla and the postsynaptic connections in the AOTUsu. Using the connectivity of the entire AVP and the dendritic fields of the MeTu neurons in the optic lobes, we infer potential visual features and the visual area from which any ER neuron receives input. We confirm some of these predictions physiologically. These results provide a strong foundation for understanding how distinct sensory features can be extracted and transformed across multiple processing stages to construct higher-order cognitive representations.

Reinforcement learning when your life depends on it: A neuro-economic theory of learning

Synaptic plasticity enables animals to adapt to their environment, but memory formation can require a substantial amount of metabolic energy, potentially impairing survival. Hence, a neuro-economic dilemma arises whether learning is a profitable investment or not, and the brain must therefore judiciously regulate learning. Indeed, in experiments it was observed that during starvation, Drosophila suppress formation of energy-intensive aversive memories. Here we include energy considerations in a reinforcement learning framework. Simulated flies learned to avoid noxious stimuli through synaptic plasticity in either the energy expensive long-term memory (LTM) pathway, or the decaying anesthesia-resistant memory (ARM) pathway. The objective of the flies is to maximize their lifespan, which is calculated with a hazard function. We find that strategies that switch between the LTM and ARM pathways, based on energy reserve and reward prediction error, prolong lifespan. Our study highlights the significance of energy-regulation of memory pathways and dopaminergic control for adaptive learning and survival. It might also benefit engineering applications of reinforcement learning under resources constraints.

Dopamine-mediated interactions between short- and long-term memory dynamics

In dynamic environments, animals make behavioural decisions on the basis of the innate valences of sensory cues and information learnt about these cues across multiple timescales1-3. However, it remains unclear how the innate valence of a sensory stimulus affects the acquisition of learnt valence information and subsequent memory dynamics. Here we show that in the Drosophila brain, interconnected short- and long-term memory units of the mushroom body jointly regulate memory through dopamine signals that encode innate and learnt sensory valences. By performing time-lapse in vivo voltage-imaging studies of neural spiking in more than 500 flies undergoing olfactory associative conditioning, we found that protocerebral posterior lateral 1 dopamine neurons (PPL1-DANs)4 heterogeneously and bidirectionally encode innate and learnt valences of punishment, reward and odour cues. During learning, these valence signals regulate memory storage and extinction in mushroom body output neurons (MBONs)5. During initial conditioning bouts, PPL1-γ1pedc and PPL1-γ2α'1 neurons control short-term memory formation, which weakens inhibitory feedback from MBON-γ1pedc>α/β to PPL1-α'2α2 and PPL1-α3. During further conditioning, this diminished feedback allows these two PPL1-DANs to encode the net innate plus learnt valence of the conditioned odour cue, which gates long-term memory formation. A computational model constrained by the fly connectome6,7 and our spiking data explains how dopamine signals mediate the circuit interactions between short- and long-term memory traces, yielding predictions that our experiments confirmed. Overall, the mushroom body achieves flexible learning through the integration of innate and learnt valences in parallel learning units sharing feedback interconnections. This hybrid physiological-anatomical mechanism may be a general means by which dopamine regulates memory dynamics in other species and brain structures, including the vertebrate basal ganglia.

Activity of nested neural circuits drives different courtship songs in Drosophila

Motor systems implement diverse motor programs to pattern behavioral sequences, yet how different motor actions are controlled on a moment-by-moment basis remains unclear. Here, we investigated the neural circuit mechanisms underlying the control of distinct courtship songs in Drosophila. Courting males rapidly alternate between two types of song: pulse and sine. By recording calcium signals in the ventral nerve cord in singing flies, we found that one neural population is active during both songs, whereas an expanded neural population, which includes neurons from the first population, is active during pulse song. Brain recordings showed that this nested activation pattern is present in two descending pathways required for singing. Connectomic analysis reveals that these two descending pathways provide structured input to ventral nerve cord neurons in a manner consistent with their activation patterns. These results suggest that nested premotor circuit activity, directed by distinct descending signals, enables rapid switching between motor actions.

Neuronal lipid droplets play a conserved and sex-biased role in maintaining whole-body energy homeostasis

Lipids are essential for neuron development and physiology. Yet, the central hubs that coordinate lipid supply and demand in neurons remain unclear. Here, we combine invertebrate and vertebrate models to establish the presence and functional significance of neuronal lipid droplets (LD) in vivo. We find that LD are normally present in neurons in a non-uniform distribution across the brain, and demonstrate triglyceride metabolism enzymes and lipid droplet-associated proteins control neuronal LD formation through both canonical and recently-discovered pathways. Appropriate LD regulation in neurons has conserved and male-biased effects on whole-body energy homeostasis across flies and mice, specifically neurons that couple environmental cues with energy homeostasis. Mechanistically, LD-derived lipids support neuron function by providing phospholipids to sustain mitochondrial and endoplasmic reticulum homeostasis. Together, our work identifies a conserved role for LD as the organelle that coordinates lipid management in neurons, with implications for our understanding of mechanisms that preserve neuronal lipid homeostasis and function in health and disease.

Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology

One of the longstanding aims of network neuroscience is to link a connectome's topological properties-i.e., features defined from connectivity alone-with an organism's neurobiology. One approach for doing so is to compare connectome properties with annotational maps. This type of analysis is popular at the meso-/macroscale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of connectivity, these datasets are accompanied by rich annotations. Here, we use a variant of the stochastic blockmodel to detect multilevel communities in the larval Drosophila connectome. We find that communities partition neurons based on function and cell type and that most interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact nonassortatively, forming form a "rich-club" of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between system-level architecture and the biological function and classification of individual neurons. We envision our study as an important step toward bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

Connectomic analysis of taste circuits in Drosophila

Our sense of taste is critical for regulating food consumption. The fruit fly Drosophila represents a highly tractable model to investigate mechanisms of taste processing, but taste circuits beyond sensory neurons are largely unidentified. Here, we use a whole-brain connectome to investigate the organization of Drosophila taste circuits. We trace pathways from four populations of sensory neurons that detect different taste modalities and project to the subesophageal zone (SEZ). We find that second-order taste neurons are primarily located within the SEZ and largely segregated by taste modality, whereas third-order neurons have more projections outside the SEZ and more overlap between modalities. Taste projections out of the SEZ innervate regions implicated in feeding, olfactory processing, and learning. We characterize interconnections between taste pathways, identify modality-dependent differences in taste neuron properties, and use computational simulations to relate connectivity to predicted activity. These studies provide insight into the architecture of Drosophila taste circuits.

A three-dimensional immunofluorescence atlas of the brain of the hackled-orb weaver spider, Uloborus diversus

Spider orb-web building is a captivating, rare example of animal construction, whose neural underpinnings remain undiscovered. An essential step in understanding the basis of this behavior is a foundational mapping of the spider's neuroanatomy, which has thus far been primarily studied using non-web building species. We created a three-dimensional atlas for the hackled orb-weaver, Uloborus diversus, based on immunostaining for the presynaptic component, synapsin, in whole-mounted spider synganglia. Aligned to this volume, we examined the expression patterns of neuronal populations representing many of the classical neurotransmitter and neuromodulators, as well as a subset of neuropeptides - detailing immunoreactivity in an unbiased fashion throughout the synganglion, revealing co-expression in known structures, as well as novel neuropils not evident in prior spider works. This optically-sliced, whole-mount atlas is the first of its kind for spiders, representing a substantive addition to knowledge of brain anatomy and neurotransmitter expression patterns for an orb-weaving species.

Analysis of fast calcium dynamics of honey bee olfactory coding

Odour processing exhibits multiple parallels between vertebrate and invertebrate olfactory systems. Insects, in particular, have emerged as relevant models for olfactory studies because of the tractability of their olfactory circuits. Here, we used fast calcium imaging to track the activity of projection neurons in the honey bee antennal lobe (AL) during olfactory stimulation at high temporal resolution. We observed a heterogeneity of response profiles and an abundance of inhibitory activities, resulting in various response latencies and stimulus-specific post-odour neural signatures. Recorded calcium signals were fed to a mushroom body (MB) model constructed implementing the fundamental features of connectivity between olfactory projection neurons, Kenyon cells (KC), and MB output neurons (MBON). The model accounts for the increase of odorant discrimination in the MB compared to the AL and reveals the recruitment of two distinct KC populations that represent odorants and their aftersmell as two separate but temporally coherent neural objects. Finally, we showed that the learning-induced modulation of KC-to-MBON synapses can explain both the variations in associative learning scores across different conditioning protocols used in bees and the bees' response latency. Thus, it provides a simple explanation of how the time contingency between the stimulus and the reward can be encoded without the need for time tracking. This study broadens our understanding of olfactory coding and learning in honey bees. It demonstrates that a model based on simple MB connectivity rules and fed with real physiological data can explain fundamental aspects of odour processing and associative learning.

Active forgetting and neuropsychiatric diseases

Recent and pioneering animal research has revealed the brain utilizes a variety of molecular, cellular, and network-level mechanisms used to forget memories in a process referred to as "active forgetting". Active forgetting increases behavioral flexibility and removes irrelevant information. Individuals with impaired active forgetting mechanisms can experience intrusive memories, distressing thoughts, and unwanted impulses that occur in neuropsychiatric diseases. The current evidence indicates that active forgetting mechanisms degrade, or mask, molecular and cellular memory traces created in synaptic connections of "engram cells" that are specific for a given memory. Combined molecular genetic/behavioral studies using Drosophila have uncovered a complex system of cellular active-forgetting pathways within engram cells that is regulated by dopamine neurons and involves dopamine-nitric oxide co-transmission and reception, endoplasmic reticulum Ca2+ signaling, and cytoskeletal remodeling machinery regulated by small GTPases. Some of these molecular cellular mechanisms have already been found to be conserved in mammals. Interestingly, some pathways independently regulate forgetting of distinct memory types and temporal phases, suggesting a multi-layering organization of forgetting systems. In mammals, active forgetting also involves modulation of memory trace synaptic strength by altering AMPA receptor trafficking. Furthermore, active-forgetting employs network level mechanisms wherein non-engram neurons, newly born-engram neurons, and glial cells regulate engram synapses in a state and experience dependent manner. Remarkably, there is evidence for potential coordination between the network and cellular level forgetting mechanisms. Finally, subjects with several neuropsychiatric diseases have been tested and shown to be impaired in active forgetting. Insights obtained from research on active forgetting in animal models will continue to enrich our understanding of the brain dysfunctions that occur in neuropsychiatric diseases.

A LAT1-Like Amino Acid Transporter Regulates Neuronal Activity in the Drosophila Mushroom Bodies

The proper functioning of neural circuits that integrate sensory signals is essential for individual adaptation to an ever-changing environment. Many molecules can modulate neuronal activity, including neurotransmitters, receptors, and even amino acids. Here, we ask whether amino acid transporters expressed by neurons can influence neuronal activity. We found that minidiscs (mnd), which encodes a light chain of a heterodimeric amino acid transporter, is expressed in different cell types of the adult Drosophila brain: in mushroom body neurons (MBs) and in glial cells. Using live calcium imaging, we found that MND expressed in α/β MB neurons is essential for sensitivity to the L-amino acids: Leu, Ile, Asp, Glu, Lys, Thr, and Arg. We found that the Target Of Rapamycin (TOR) pathway but not the Glutamate Dehydrogenase (GDH) pathway is involved in the Leucine-dependent response of α/β MB neurons. This study strongly supports the key role of MND in regulating MB activity in response to amino acids.

Serotonergic amplification of odor-evoked neural responses maps onto flexible behavioral outcomes

Behavioral responses to many odorants are not fixed but are flexible, varying based on organismal needs. How such variations arise and the role of various neuromodulators in achieving flexible neural-to-behavioral mapping is not fully understood. In this study, we examined how serotonin modulates the neural and behavioral responses to odorants in locusts (Schistocerca americana). Our results indicated that serotonin can increase or decrease appetitive behavior in an odor-specific manner. On the other hand, in the antennal lobe, serotonergic modulation enhanced odor-evoked response strength but left the temporal features or the combinatorial response profiles unperturbed. This result suggests that serotonin allows for sensitive and robust recognition of odorants. Nevertheless, the uniform neural response amplification appeared to be at odds with the observed stimulus-specific behavioral modulation. We show that a simple linear model with neural ensembles segregated based on behavioral relevance is sufficient to explain the serotonin-mediated flexible mapping between neural and behavioral responses.

Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit

The brain can represent almost limitless objects to "categorize an unlabeled world" (Edelman, 1989). This feat is supported by expansion layer circuit architectures, in which neurons carrying information about discrete sensory channels make combinatorial connections onto much larger postsynaptic populations. Combinatorial connections in expansion layers are modeled as randomized sets. The extent to which randomized wiring exists in vivo is debated, and how combinatorial connectivity patterns are generated during development is not understood. Non-deterministic wiring algorithms could program such connectivity using minimal genomic information. Here, we investigate anatomic and transcriptional patterns and perturb partner availability to ask how Kenyon cells, the expansion layer neurons of the insect mushroom body, obtain combinatorial input from olfactory projection neurons. Olfactory projection neurons form their presynaptic outputs in an orderly, predictable, and biased fashion. We find that Kenyon cells accept spatially co-located but molecularly heterogeneous inputs from this orderly map, and ask how Kenyon cell surface molecule expression impacts partner choice. Cell surface immunoglobulins are broadly depleted in Kenyon cells, and we propose that this allows them to form connections with molecularly heterogeneous partners. This model can explain how developmentally identical neurons acquire diverse wiring identities.

Selective consolidation of learning and memory via recall-gated plasticity

In a variety of species and behavioral contexts, learning and memory formation recruits two neural systems, with initial plasticity in one system being consolidated into the other over time. Moreover, consolidation is known to be selective; that is, some experiences are more likely to be consolidated into long-term memory than others. Here, we propose and analyze a model that captures common computational principles underlying such phenomena. The key component of this model is a mechanism by which a long-term learning and memory system prioritizes the storage of synaptic changes that are consistent with prior updates to the short-term system. This mechanism, which we refer to as recall-gated consolidation, has the effect of shielding long-term memory from spurious synaptic changes, enabling it to focus on reliable signals in the environment. We describe neural circuit implementations of this model for different types of learning problems, including supervised learning, reinforcement learning, and autoassociative memory storage. These implementations involve synaptic plasticity rules modulated by factors such as prediction accuracy, decision confidence, or familiarity. We then develop an analytical theory of the learning and memory performance of the model, in comparison to alternatives relying only on synapse-local consolidation mechanisms. We find that recall-gated consolidation provides significant advantages, substantially amplifying the signal-to-noise ratio with which memories can be stored in noisy environments. We show that recall-gated consolidation gives rise to a number of phenomena that are present in behavioral learning paradigms, including spaced learning effects, task-dependent rates of consolidation, and differing neural representations in short- and long-term pathways.

Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body

The arthropod mushroom body is well-studied as an expansion layer representing olfactory stimuli and linking them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their function remains unclear. Here, we identify inputs to visual Kenyon cells using the FlyWire adult whole-brain connectome. Input repertoires are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual neurons presynaptic to Kenyon cells have large receptive fields, while interneuron inputs receive spatially restricted signals that may be tuned to specific visual features. Individual visual Kenyon cells randomly sample sparse inputs from combinations of visual channels, including multiple optic lobe neuropils. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the specific input repertoire to the smaller population of visual Kenyon cells suggests a constrained encoding of visual stimuli.

A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support

Data-driven spiking neuronal network (SNN) models enable in-silico analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand huge computing facilities and their simulation speed is considerably slower than real-time. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. The model is built using a qualitative modeling approach that can reproduce key dynamics of neuronal activity. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model was successfully implemented on a small entry-level field-programmable gate array and simulated the activity of a network in real-time. In addition, the model reproduced olfactory associative learning, the primary function of the olfactory system, and characteristic spiking activities of different neuron types. In sum, this paper propose a method for building data-driven SNN models from biological data. Our approach reproduces the function and neuronal activities of the nervous system and is lightweight, acceleratable with dedicated hardware, making it scalable to large-scale networks. Therefore, our approach is expected to play an important role in elucidating the brain's information processing at the cellular and synaptic level through an analysis-by-construction approach. In addition, it may be applicable to edge artificial intelligence systems in the future.

Single dopaminergic neuron DAN-c1 in Drosophila larval brain mediates aversive olfactory learning through D2-like receptors

The intricate relationship between the dopaminergic system and olfactory associative learning in Drosophila has been an intense scientific inquiry. Leveraging the formidable genetic tools, we conducted a screening of 57 dopaminergic drivers, leading to the discovery of DAN-c1 driver, uniquely targeting the single dopaminergic neuron (DAN) in each brain hemisphere. While the involvement of excitatory D1-like receptors is well-established, the role of D2-like receptors (D2Rs) remains underexplored. Our investigation reveals the expression of D2Rs in both DANs and the mushroom body (MB) of third instar larval brains. Silencing D2Rs in DAN-c1 via microRNA disrupts aversive learning, further supported by optogenetic activation of DAN-c1 during training, affirming the inhibitory role of D2R autoreceptor. Intriguingly, D2R knockdown in the MB impairs both appetitive and aversive learning. These findings elucidate the distinct contributions of D2Rs in diverse brain structures, providing novel insights into the molecular mechanisms governing associative learning in Drosophila larvae.

PKCδ is an activator of neuronal mitochondrial metabolism that mediates the spacing effect on memory consolidation

Relevance-based selectivity and high energy cost are two distinct features of long-term memory (LTM) formation that warrant its default inhibition. Spaced repetition of learning is a highly conserved cognitive mechanism that can lift this inhibition. Here, we questioned how the spacing effect integrates experience selection and energy efficiency at the cellular and molecular levels. We showed in Drosophila that spaced training triggers LTM formation by extending over several hours an increased mitochondrial metabolic activity in neurons of the associative memory center, the mushroom bodies (MBs). We found that this effect is mediated by PKCδ, a member of the so-called 'novel PKC' family of enzymes, which uncovers the critical function of PKCδ in neurons as a regulator of mitochondrial metabolism for LTM. Additionally, PKCδ activation and translocation to mitochondria result from LTM-specific dopamine signaling on MB neurons. By bridging experience-dependent neuronal circuit activity with metabolic modulation of memory-encoding neurons, PKCδ signaling binds the cognitive and metabolic constraints underlying LTM formation into a unified gating mechanism.

Synapse-Specific Trapping of SNARE Machinery Proteins in the Anesthetized Drosophila Brain

General anesthetics disrupt brain network dynamics through multiple pathways, in part through postsynaptic potentiation of inhibitory ion channels as well as presynaptic inhibition of neuroexocytosis. Common clinical general anesthetic drugs, such as propofol and isoflurane, have been shown to interact and interfere with core components of the exocytic release machinery to cause impaired neurotransmitter release. Recent studies however suggest that these drugs do not affect all synapse subtypes equally. We investigated the role of the presynaptic release machinery in multiple neurotransmitter systems under isoflurane general anesthesia in the adult female Drosophila brain using live-cell super-resolution microscopy and optogenetic readouts of exocytosis and neural excitability. We activated neurotransmitter-specific mushroom body output neurons and imaged presynaptic function under isoflurane anesthesia. We found that isoflurane impaired synaptic release and presynaptic protein dynamics in excitatory cholinergic synapses. In contrast, isoflurane had little to no effect on inhibitory GABAergic or glutamatergic synapses. These results present a distinct inhibitory mechanism for general anesthesia, whereby neuroexocytosis is selectively impaired at excitatory synapses, while inhibitory synapses remain functional. This suggests a presynaptic inhibitory mechanism that complements the other inhibitory effects of these drugs.

Evolution of connectivity architecture in the Drosophila mushroom body

Brain evolution has primarily been studied at the macroscopic level by comparing the relative size of homologous brain centers between species. How neuronal circuits change at the cellular level over evolutionary time remains largely unanswered. Here, using a phylogenetically informed framework, we compare the olfactory circuits of three closely related Drosophila species that differ in their chemical ecology: the generalists Drosophila melanogaster and Drosophila simulans and Drosophila sechellia that specializes on ripe noni fruit. We examine a central part of the olfactory circuit that, to our knowledge, has not been investigated in these species-the connections between projection neurons and the Kenyon cells of the mushroom body-and identify species-specific connectivity patterns. We found that neurons encoding food odors connect more frequently with Kenyon cells, giving rise to species-specific biases in connectivity. These species-specific connectivity differences reflect two distinct neuronal phenotypes: in the number of projection neurons or in the number of presynaptic boutons formed by individual projection neurons. Finally, behavioral analyses suggest that such increased connectivity enhances learning performance in an associative task. Our study shows how fine-grained aspects of connectivity architecture in an associative brain center can change during evolution to reflect the chemical ecology of a species.

Cis inhibition of co-expressed DIPs and Dprs shapes neural development

In Drosophila , two interacting adhesion protein families, Dprs and DIPs, coordinate the assembly of neural networks. While intercellular DIP/Dpr interactions have been well characterized, DIPs and Dprs are often co-expressed within the same cells, raising the question as to whether they also interact in cis . We show, in cultured cells and in vivo, that DIP-α and DIP-δ can interact in cis with their ligands, Dpr6/10 and Dpr12, respectively. When co-expressed in cis with their cognate partners, these Dprs regulate the extent of trans binding, presumably through competitive cis interactions. We demonstrate the neurodevelopmental effects of cis inhibition in fly motor neurons and in the mushroom body. We further show that a long disordered region of DIP-α at the C-terminus is required for cis but not trans interactions, likely because it alleviates geometric constraints on cis binding. Thus, the balance between cis and trans interactions plays a role in controlling neural development.

Functional Imaging of Learning-Induced Plasticity in the Central Nervous System with Genetically Encoded Reporters in Drosophila

Learning and memory allow animals to adjust their behavior based on the predictive value of their past experiences. Memories often exist in complex representations, spread across numerous cells and synapses in the brain. Studying relatively simple forms of memory provides insights into the fundamental processes that underlie multiple forms of memory. Associative learning occurs when an animal learns the relationship between two previously unrelated sensory stimuli, such as when a hungry animal learns that a particular odor is followed by a tasty reward. Drosophila is a particularly powerful model to study how this type of memory works. The fundamental principles are widely shared among animals, and there is a wide range of genetic tools available to study circuit function in flies. In addition, the olfactory structures that mediate associative learning in flies, such as the mushroom body and its associated neurons, are anatomically organized, relatively well-characterized, and readily accessible to imaging. Here, we review the olfactory anatomy and physiology of the olfactory system, describe how plasticity in the olfactory pathway mediates learning and memory, and explain the general principles underlying calcium imaging approaches.

Building and integrating brain-wide maps of nervous system function in invertebrates

The selection and execution of context-appropriate behaviors is controlled by the integrated action of neural circuits throughout the brain. However, how activity is coordinated across brain regions, and how nervous system structure enables these functional interactions, remain open questions. Recent technical advances have made it feasible to build brain-wide maps of nervous system structure and function, such as brain activity maps, connectomes, and cell atlases. Here, we review recent progress in this area, focusing on C. elegans and D. melanogaster, as recent work has produced global maps of these nervous systems. We also describe neural circuit motifs elucidated in studies of specific networks, which highlight the complexities that must be captured to build accurate models of whole-brain function.

Connectome-driven neural inventory of a complete visual system

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain's volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly's visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

Investigating visual navigation using spiking neural network models of the insect mushroom bodies

Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route. Evidence from behavioural experiments, computational studies and brain lesions all support this idea. Here we further investigate the role of mushroom bodies in visual navigation with a spiking neural network model learning complex natural scenes. By implementing these networks in GeNN-a library for building GPU accelerated spiking neural networks-we were able to test these models offline on an image database representing navigation through a complex outdoor natural environment, and also online embodied on a robot. The mushroom body model successfully learnt a large series of visual scenes (400 scenes corresponding to a 27 m route) and used these memories to choose accurate heading directions during route recapitulation in both complex environments. Through analysing our model's Kenyon cell (KC) activity, we were able to demonstrate that KC activity is directly related to the respective novelty of input images. Through conducting a parameter search we found that there is a non-linear dependence between optimal KC to visual projection neuron (VPN) connection sparsity and the length of time the model is presented with an image stimulus. The parameter search also showed training the model on lower proportions of a route generally produced better accuracy when testing on the entire route. We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. On a 6.5 m route, the mushroom body model had a mean distance to training route (error) of 0.144 ± 0.088 m over 5 trials, which was performance comparable to standard visual-only navigation algorithms. Thus, we have demonstrated that a biologically plausible model of the ant mushroom body can navigate complex environments both in simulation and the real world. Understanding the neural basis of this behaviour will provide insight into how neural circuits are tuned to rapidly learn behaviourally relevant information from complex environments and provide inspiration for creating bio-mimetic computer/robotic systems that can learn rapidly with low energy requirements.

Peripheral preprocessing in Drosophila facilitates odor classification

The mammalian brain implements sophisticated sensory processing algorithms along multilayered ("deep") neural networks. Strategies that insects use to meet similar computational demands, while relying on smaller nervous systems with shallow architectures, remain elusive. Using Drosophila as a model, we uncover the algorithmic role of odor preprocessing by a shallow network of compartmentalized olfactory receptor neurons. Each compartment operates as a ratiometric unit for specific odor-mixtures. This computation arises from a simple mechanism: electrical coupling between two differently sized neurons. We demonstrate that downstream synaptic connectivity is shaped to optimally leverage amplification of a hedonic value signal in the periphery. Furthermore, peripheral preprocessing is shown to markedly improve novel odor classification in a higher brain center. Together, our work highlights a far-reaching functional role of the sensory periphery for downstream processing. By elucidating the implementation of powerful computations by a shallow network, we provide insights into general principles of efficient sensory processing algorithms.

Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

Spaced training activates Miro/Milton-dependent mitochondrial dynamics in neuronal axons to sustain long-term memory

Neurons have differential and fluctuating energy needs across distinct cellular compartments, shaped by brain electrochemical activity associated with cognition. In vitro studies show that mitochondria transport from soma to axons is key to maintaining neuronal energy homeostasis. Nevertheless, whether the spatial distribution of neuronal mitochondria is dynamically adjusted in vivo in an experience-dependent manner remains unknown. In Drosophila, associative long-term memory (LTM) formation is initiated by an early and persistent upregulation of mitochondrial pyruvate flux in the axonal compartment of neurons in the mushroom body (MB). Through behavior experiments, super-resolution analysis of mitochondria morphology in the neuronal soma and in vivo mitochondrial fluorescence recovery after photobleaching (FRAP) measurements in the axons, we show that LTM induction, contrary to shorter-lived memories, is sustained by the departure of some mitochondria from MB neuronal soma and increased mitochondrial dynamics in the axonal compartment. Accordingly, impairing mitochondrial dynamics abolished the increased pyruvate consumption, specifically after spaced training and in the MB axonal compartment, thereby preventing LTM formation. Our results thus promote reorganization of the mitochondrial network in neurons as an integral step in elaborating high-order cognitive processes.

Diverse memory paradigms in Drosophila reveal diverse neural mechanisms

In this review, we aggregated the different types of learning and memory paradigms developed in adult Drosophila and attempted to assess the similarities and differences in the neural mechanisms supporting diverse types of memory. The simplest association memory assays are conditioning paradigms (olfactory, visual, and gustatory). A great deal of work has been done on these memories, revealing hundreds of genes and neural circuits supporting this memory. Variations of conditioning assays (reversal learning, trace conditioning, latent inhibition, and extinction) also reveal interesting memory mechanisms, whereas mechanisms supporting spatial memory (thermal maze, orientation memory, and heat box) and the conditioned suppression of innate behaviors (phototaxis, negative geotaxis, anemotaxis, and locomotion) remain largely unexplored. In recent years, there has been an increased interest in multisensory and multicomponent memories (context-dependent and cross-modal memory) and higher-order memory (sensory preconditioning and second-order conditioning). Some of this work has revealed how the intricate mushroom body (MB) neural circuitry can support more complex memories. Finally, the most complex memories are arguably those involving social memory: courtship conditioning and social learning (mate-copying and egg-laying behaviors). Currently, very little is known about the mechanisms supporting social memories. Overall, the MBs are important for association memories of multiple sensory modalities and multisensory integration, whereas the central complex is important for place, orientation, and navigation memories. Interestingly, several different types of memory appear to use similar or variants of the olfactory conditioning neural circuitry, which are repurposed in different ways.

Skewing information flow through pre- and postsynaptic plasticity in the mushroom bodies of Drosophila

Animal brains need to store information to construct a representation of their environment. Knowledge of what happened in the past allows both vertebrates and invertebrates to predict future outcomes by recalling previous experience. Although invertebrate and vertebrate brains share common principles at the molecular, cellular, and circuit-architectural levels, there are also obvious differences as exemplified by the use of acetylcholine versus glutamate as the considered main excitatory neurotransmitters in the respective central nervous systems. Nonetheless, across central nervous systems, synaptic plasticity is thought to be a main substrate for memory storage. Therefore, how brain circuits and synaptic contacts change following learning is of fundamental interest for understanding brain computations tied to behavior in any animal. Recent progress has been made in understanding such plastic changes following olfactory associative learning in the mushroom bodies (MBs) of Drosophila A current framework of memory-guided behavioral selection is based on the MB skew model, in which antagonistic synaptic pathways are selectively changed in strength. Here, we review insights into plasticity at dedicated Drosophila MB output pathways and update what is known about the plasticity of both pre- and postsynaptic compartments of Drosophila MB neurons.

The caloric value of food intake structurally adjusts a neuronal mushroom body circuit mediating olfactory learning in Drosophila

Associative learning enables the adaptive adjustment of behavioral decisions based on acquired, predicted outcomes. The valence of what is learned is influenced not only by the learned stimuli and their temporal relations, but also by prior experiences and internal states. In this study, we used the fruit fly Drosophila melanogaster to demonstrate that neuronal circuits involved in associative olfactory learning undergo restructuring during extended periods of low-caloric food intake. Specifically, we observed a decrease in the connections between specific dopaminergic neurons (DANs) and Kenyon cells at distinct compartments of the mushroom body. This structural synaptic plasticity was contingent upon the presence of allatostatin A receptors in specific DANs and could be mimicked optogenetically by expressing a light-activated adenylate cyclase in exactly these DANs. Importantly, we found that this rearrangement in synaptic connections influenced aversive, punishment-induced olfactory learning but did not impact appetitive, reward-based learning. Whether induced by prolonged low-caloric conditions or optogenetic manipulation of cAMP levels, this synaptic rearrangement resulted in a reduction of aversive associative learning. Consequently, the balance between positive and negative reinforcing signals shifted, diminishing the ability to learn to avoid odor cues signaling negative outcomes. These results exemplify how a neuronal circuit required for learning and memory undergoes structural plasticity dependent on prior experiences of the nutritional value of food.

Learning with sparse reward in a gap junction network inspired by the insect mushroom body

Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.

Modeling neurodegenerative and neurodevelopmental disorders in the Drosophila mushroom body

The common fruit fly Drosophila melanogaster provides a powerful platform to investigate the genetic, molecular, cellular, and neural circuit mechanisms of behavior. Research in this model system has shed light on multiple aspects of brain physiology and behavior, from fundamental neuronal function to complex behaviors. A major anatomical region that modulates complex behaviors is the mushroom body (MB). The MB integrates multimodal sensory information and is involved in behaviors ranging from sensory processing/responses to learning and memory. Many genes that underlie brain disorders are conserved, from flies to humans, and studies in Drosophila have contributed significantly to our understanding of the mechanisms of brain disorders. Genetic mutations that mimic human diseases-such as Fragile X syndrome, neurofibromatosis type 1, Parkinson's disease, and Alzheimer's disease-affect MB structure and function, altering behavior. Studies dissecting the effects of disease-causing mutations in the MB have identified key pathological mechanisms, and the development of a complete connectome promises to add a comprehensive anatomical framework for disease modeling. Here, we review Drosophila models of human neurodevelopmental and neurodegenerative disorders via the effects of their underlying mutations on MB structure, function, and the resulting behavioral alterations.

An integrative sensor of body states: how the mushroom body modulates behavior depending on physiological context

The brain constantly compares past and present experiences to predict the future, thereby enabling instantaneous and future behavioral adjustments. Integration of external information with the animal's current internal needs and behavioral state represents a key challenge of the nervous system. Recent advancements in dissecting the function of the Drosophila mushroom body (MB) at the single-cell level have uncovered its three-layered logic and parallel systems conveying positive and negative values during associative learning. This review explores a lesser-known role of the MB in detecting and integrating body states such as hunger, thirst, and sleep, ultimately modulating motivation and sensory-driven decisions based on the physiological state of the fly. State-dependent signals predominantly affect the activity of modulatory MB input neurons (dopaminergic, serotoninergic, and octopaminergic), but also induce plastic changes directly at the level of the MB intrinsic and output neurons. Thus, the MB emerges as a tightly regulated relay station in the insect brain, orchestrating neuroadaptations due to current internal and behavioral states leading to short- but also long-lasting changes in behavior. While these adaptations are crucial to ensure fitness and survival, recent findings also underscore how circuit motifs in the MB may reflect fundamental design principles that contribute to maladaptive behaviors such as addiction or depression-like symptoms.

Beyond prediction error: 25 years of modeling the associations formed in the insect mushroom body

The insect mushroom body has gained increasing attention as a system in which the computational basis of neural learning circuits can be unraveled. We now understand in detail the key locations in this circuit where synaptic associations are formed between sensory patterns and values leading to actions. However, the actual learning rule (or rules) implemented by neural activity and leading to synaptic change is still an open question. Here, I survey the diversity of answers that have been offered in computational models of this system over the past decades, including the recurring assumption-in line with top-down theories of associative learning-that the core function is to reduce prediction error. However, I will argue, a more bottom-up approach may ultimately reveal a richer algorithmic capacity in this still enigmatic brain neuropil.

Sensory encoding and memory in the mushroom body: signals, noise, and variability

To survive in changing environments, animals need to learn to associate specific sensory stimuli with positive or negative valence. How do they form stimulus-specific memories to distinguish between positively/negatively associated stimuli and other irrelevant stimuli? Solving this task is one of the functions of the mushroom body, the associative memory center in insect brains. Here we summarize recent work on sensory encoding and memory in the Drosophila mushroom body, highlighting general principles such as pattern separation, sparse coding, noise and variability, coincidence detection, and spatially localized neuromodulation, and placing the mushroom body in comparative perspective with mammalian memory systems.

What do the mushroom bodies do for the insect brain? Twenty-five years of progress

In 1998, a special edition of Learning & Memory was published with a discrete focus of synthesizing the state of the field to provide an overview of the function of the insect mushroom body. While molecular neuroscience and optical imaging of larger brain areas were advancing, understanding the basic functioning of neuronal circuits, particularly in the context of the mushroom body, was rudimentary. In the past 25 years, technological innovations have allowed researchers to map and understand the in vivo function of the neuronal circuits of the mushroom body system, making it an ideal model for investigating the circuit basis of sensory encoding, memory formation, and behavioral decisions. Collaborative efforts within the community have played a crucial role, leading to an interactive connectome of the mushroom body and accessible genetic tools for studying mushroom body circuit function. Looking ahead, continued technological innovation and collaborative efforts are likely to further advance our understanding of the mushroom body and its role in behavior and cognition, providing insights that generalize to other brain structures and species.

Future avenues in Drosophila mushroom body research

How does the brain translate sensory information into complex behaviors? With relatively small neuronal numbers, readable behavioral outputs, and an unparalleled genetic toolkit, the Drosophila mushroom body (MB) offers an excellent model to address this question in the context of associative learning and memory. Recent technological breakthroughs, such as the freshly completed full-brain connectome, multiomics approaches, CRISPR-mediated gene editing, and machine learning techniques, led to major advancements in our understanding of the MB circuit at the molecular, structural, physiological, and functional levels. Despite significant progress in individual MB areas, the field still faces the fundamental challenge of resolving how these different levels combine and interact to ultimately control the behavior of an individual fly. In this review, we discuss various aspects of MB research, with a focus on the current knowledge gaps, and an outlook on the future methodological developments required to reach an overall view of the neurobiological basis of learning and memory.

Octopamine in the mushroom body circuitry for learning and memory

Octopamine, the functional analog of noradrenaline, modulates many different behaviors and physiological processes in invertebrates. In the central nervous system, a few octopaminergic neurons project throughout the brain and innervate almost all neuropils. The center of memory formation in insects, the mushroom bodies, receive octopaminergic innervations in all insects investigated so far. Different octopamine receptors, either increasing or decreasing cAMP or calcium levels in the cell, are localized in Kenyon cells, further supporting the release of octopamine in the mushroom bodies. In addition, different mushroom body (MB) output neurons, projection neurons, and dopaminergic PAM cells are targets of octopaminergic neurons, enabling the modulation of learning circuits at different neural sites. For some years, the theory persisted that octopamine mediates rewarding stimuli, whereas dopamine (DA) represents aversive stimuli. This simple picture has been challenged by the finding that DA is required for both appetitive and aversive learning. Furthermore, octopamine is also involved in aversive learning and a rather complex interaction between these biogenic amines seems to modulate learning and memory. This review summarizes the role of octopamine in MB function, focusing on the anatomical principles and the role of the biogenic amine in learning and memory.

Cyclic nucleotide-induced bidirectional long-term synaptic plasticity in Drosophila mushroom body

Activation of the cAMP pathway is one of the common mechanisms underlying long-term potentiation (LTP). In the Drosophila mushroom body, simultaneous activation of odour-coding Kenyon cells (KCs) and reinforcement-coding dopaminergic neurons activates adenylyl cyclase in KC presynaptic terminals, which is believed to trigger synaptic plasticity underlying olfactory associative learning. However, learning induces long-term depression (LTD) at these synapses, contradicting the universal role of cAMP as a facilitator of transmission. Here, we developed a system to electrophysiologically monitor both short-term and long-term synaptic plasticity at KC output synapses and demonstrated that they are indeed an exception in which activation of the cAMP-protein kinase A pathway induces LTD. Contrary to the prevailing model, our cAMP imaging found no evidence for synergistic action of dopamine and KC activity on cAMP synthesis. Furthermore, we found that forskolin-induced cAMP increase alone was insufficient for plasticity induction; it additionally required simultaneous KC activation to replicate the presynaptic LTD induced by pairing with dopamine. On the other hand, activation of the cGMP pathway paired with KC activation induced slowly developing LTP, proving antagonistic actions of the two second-messenger pathways predicted by behavioural study. Finally, KC subtype-specific interrogation of synapses revealed that different KC subtypes exhibit distinct plasticity duration even among synapses on the same postsynaptic neuron. Thus, our work not only revises the role of cAMP in synaptic plasticity by uncovering the unexpected convergence point of the cAMP pathway and neuronal activity, but also establishes the methods to address physiological mechanisms of synaptic plasticity in this important model. KEY POINTS: Although presynaptic cAMP increase generally facilitates synapses, olfactory associative learning in Drosophila, which depends on dopamine and cAMP signalling genes, induces long-term depression (LTD) at the mushroom body output synapses. By combining electrophysiology, pharmacology and optogenetics, we directly demonstrate that these synapses are an exception where activation of the cAMP-protein kinase A pathway leads to presynaptic LTD. Dopamine- or forskolin-induced cAMP increase alone is not sufficient for LTD induction; neuronal activity, which has been believed to trigger cAMP synthesis in synergy with dopamine input, is required in the downstream pathway of cAMP. In contrast to cAMP, activation of the cGMP pathway paired with neuronal activity induces presynaptic long-term potentiation, which explains behaviourally observed opposing actions of transmitters co-released by dopaminergic neurons. Our work not only revises the role of cAMP in synaptic plasticity, but also provides essential methods to address physiological mechanisms of synaptic plasticity in this important model system.

Glial metabolism versatility regulates mushroom body-driven behavioral output in Drosophila

Providing metabolic support to neurons is now recognized as a major function of glial cells that is conserved from invertebrates to vertebrates. However, research in this field has focused for more than two decades on the relevance of lactate and glial glycolysis for neuronal energy metabolism, while overlooking many other facets of glial metabolism and their impact on neuronal physiology, circuit activity, and behavior. Here, we review recent work that has unveiled new features of glial metabolism, especially in Drosophila, in the modulation of behavioral traits involving the mushroom bodies (MBs). These recent findings reveal that spatially and biochemically distinct modes of glucose-derived neuronal fueling are implemented within the MB in a memory type-specific manner. In addition, cortex glia are endowed with several antioxidant functions, whereas astrocytes can serve as pro-oxidant agents that are beneficial to redox signaling underlying long-term memory. Finally, glial fatty acid oxidation seems to play a dual fail-safe role: first, as a mode of energy production upon glucose shortage, and, second, as a factor underlying the clearance of excessive oxidative load during sleep. Altogether, these integrated studies performed in Drosophila indicate that glial metabolism has a deterministic role on behavior.

Roles of feedback and feed-forward networks of dopamine subsystems: insights from Drosophila studies

Across animal species, dopamine-operated memory systems comprise anatomically segregated, functionally diverse subsystems. Although individual subsystems could operate independently to support distinct types of memory, the logical interplay between subsystems is expected to enable more complex memory processing by allowing existing memory to influence future learning. Recent comprehensive ultrastructural analysis of the Drosophila mushroom body revealed intricate networks interconnecting the dopamine subsystems-the mushroom body compartments. Here, we review the functions of some of these connections that are beginning to be understood. Memory consolidation is mediated by two different forms of network: A recurrent feedback loop within a compartment maintains sustained dopamine activity required for consolidation, whereas feed-forward connections across compartments allow short-term memory formation in one compartment to open the gate for long-term memory formation in another compartment. Extinction and reversal of aversive memory rely on a similar feed-forward circuit motif that signals omission of punishment as a reward, which triggers plasticity that counteracts the original aversive memory trace. Finally, indirect feed-forward connections from a long-term memory compartment to short-term memory compartments mediate higher-order conditioning. Collectively, these emerging studies indicate that feedback control and hierarchical connectivity allow the dopamine subsystems to work cooperatively to support diverse and complex forms of learning.

Kalium channelrhodopsins effectively inhibit neurons

The analysis of neural circuits has been revolutionized by optogenetic methods. Light-gated chloride-conducting anion channelrhodopsins (ACRs)-recently emerged as powerful neuron inhibitors. For cells or sub-neuronal compartments with high intracellular chloride concentrations, however, a chloride conductance can have instead an activating effect. The recently discovered light-gated, potassium-conducting, kalium channelrhodopsins (KCRs) might serve as an alternative in these situations, with potentially broad application. As yet, KCRs have not been shown to confer potent inhibitory effects in small genetically tractable animals. Here, we evaluated the utility of KCRs to suppress behavior and inhibit neural activity in Drosophila, Caenorhabditis elegans, and zebrafish. In direct comparisons with ACR1, a KCR1 variant with enhanced plasma-membrane trafficking displayed comparable potency, but with improved properties that include reduced toxicity and superior efficacy in putative high-chloride cells. This comparative analysis of behavioral inhibition between chloride- and potassium-selective silencing tools establishes KCRs as next-generation optogenetic inhibitors for in vivo circuit analysis in behaving animals.

Iterative assay for transposase-accessible chromatin by sequencing to isolate functionally relevant neuronal subtypes

The Drosophila brain contains tens of thousands of distinct cell types. Thousands of different transgenic lines reproducibly target specific neuron subsets, yet most still express in several cell types. Furthermore, most lines were developed without a priori knowledge of where the transgenes would be expressed. To aid in the development of cell type-specific tools for neuronal identification and manipulation, we developed an iterative assay for transposase-accessible chromatin (ATAC) approach. Open chromatin regions (OCRs) enriched in neurons, compared to whole bodies, drove transgene expression preferentially in subsets of neurons. A second round of ATAC-seq from these specific neuron subsets revealed additional enriched OCR2s that further restricted transgene expression within the chosen neuron subset. This approach allows for continued refinement of transgene expression, and we used it to identify neurons relevant for sleep behavior. Furthermore, this approach is widely applicable to other cell types and to other organisms.

Use-Dependent, Untapped Dual Kinase Signaling Localized in Brain Learning Circuitry

Imaging brain learning and memory circuit kinase signaling is a monumental challenge. The separation of phases-based activity reporter of kinase (SPARK) biosensors allow circuit-localized studies of multiple interactive kinases in vivo, including protein kinase A (PKA) and extracellular signal-regulated kinase (ERK) signaling. In the precisely-mapped Drosophila brain learning/memory circuit, we find PKA and ERK signaling differentially enriched in distinct Kenyon cell connectivity nodes. We discover that potentiating normal circuit activity induces circuit-localized PKA and ERK signaling, expanding kinase function within new presynaptic and postsynaptic domains. Activity-induced PKA signaling shows extensive overlap with previously selective ERK signaling nodes, while activity-induced ERK signaling arises in new connectivity nodes. We find targeted synaptic transmission blockade in Kenyon cells elevates circuit-localized ERK induction in Kenyon cells with normally high baseline ERK signaling, suggesting lateral and feedback inhibition. We discover overexpression of the pathway-linking Meng-Po (human SBK1) serine/threonine kinase to improve learning acquisition and memory consolidation results in dramatically heightened PKA and ERK signaling in separable Kenyon cell circuit connectivity nodes, revealing both synchronized and untapped signaling potential. Finally, we find that a mechanically-induced epileptic seizure model (easily shocked "bang-sensitive" mutants) has strongly elevated, circuit-localized PKA and ERK signaling. Both sexes were used in all experiments, except for the hemizygous male-only seizure model. Hyperexcitable, learning-enhanced, and epileptic seizure models have comparably elevated interactive kinase signaling, suggesting a common basis of use-dependent induction. We conclude that PKA and ERK signaling modulation is locally coordinated in use-dependent spatial circuit dynamics underlying seizure susceptibility linked to learning/memory potential.