Complete Connectomic Reconstruction of Olfactory Projection Neurons in the Fly Brain

A competitive disinhibitory network for robust optic flow processing in Drosophila

Many animals navigate using optic flow, detecting rotational image velocity differences between their eyes to adjust direction. Forward locomotion produces strong symmetric translational optic flow that can mask these differences, yet the brain efficiently extracts these binocular asymmetries for course control. In Drosophila melanogaster, monocular horizontal system neurons facilitate detection of binocular asymmetries and contribute to steering. To understand these functions, we reconstructed horizontal system cells' central network using electron microscopy datasets, revealing convergent visual inputs, a recurrent inhibitory middle layer and a divergent output layer projecting to the ventral nerve cord and deeper brain regions. Two-photon imaging, GABA receptor manipulations and modeling, showed that lateral disinhibition reduces the output's translational sensitivity while enhancing its rotational selectivity. Unilateral manipulations confirmed the role of interneurons and descending outputs in steering. These findings establish competitive disinhibition as a key circuit mechanism for detecting rotational motion during translation, supporting navigation in dynamic environments.

Olfactory projection neuron rewiring in the brain of an ecological specialist

Animal behaviors can differ greatly between closely related species. These behavioral changes are frequently linked to sensory system modifications, but central brain cell-type alterations might also be involved. Here, we develop advanced genetic tools to compare homologous central neurons in Drosophila sechellia, an ecological specialist, with the generalist Drosophila melanogaster. Through systematic morphological analysis of olfactory projection neurons (PNs), we reveal that the global anatomy of these second-order neurons is conserved. However, high-resolution, quantitative comparisons identify a striking case of convergent rewiring of PNs in two olfactory pathways critical for D. sechellia's host location. Calcium imaging and labeling of pre-synaptic sites in these evolved D. sechellia PNs indicate that species-specific connections with third-order partners are formed. This work demonstrates that peripheral sensory evolution is accompanied by selective wiring changes in the central brain to facilitate ecological specialization and paves the way to compare other cell types throughout the nervous system.

Functional connectomics spanning multiple areas of mouse visual cortex

Understanding the brain requires understanding neurons' functional responses to the circuit architecture shaping them. Here we introduce the MICrONS functional connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher visual areas (VISrl, VISal and VISlm) in an awake mouse that is viewing natural and synthetic stimuli. These data are co-registered with an electron microscopy reconstruction containing more than 200,000 cells and 0.5 billion synapses. Proofreading of a subset of neurons yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Released as an open-access resource, this dataset includes the tools for data retrieval and analysis1,2. Accompanying studies describe its use for comprehensive characterization of cell types3-6, a synaptic level connectivity diagram of a cortical column4, and uncovering cell-type-specific inhibitory connectivity that can be linked to gene expression data4,7. Functionally, we identify new computational principles of how information is integrated across visual space8, characterize novel types of neuronal invariances9 and bring structure and function together to uncover a general principle for connectivity between excitatory neurons within and across areas10,11.

Neurological confirmation of periplanone-D exploitation as a primary sex pheromone and counteractions of other components in the smoky brown cockroach Periplaneta fuliginosa

The smoky brown cockroach, Periplaneta fuliginosa, is a peridomestic pest inhabiting broad regions of the world from temperate to subtropical zones. In common with other related species such as the American cockroach, Periplaneta americana, female-emitted sex pheromone components, named periplanones, are known to be key volatiles that elicit long-range attraction and courtship rituals in males. How periplanones are processed in the nervous system has been entirely unexplored in P. fuliginosa. By using pheromone compounds, periplanones A, B, C, and D, as stimulants to the antenna, we identified four distinct types of interneurons (projection neurons) that relay pheromonal signals from a single olfactory glomerulus of the first-order olfactory center (antennal lobe) to higher-order centers in the ipsilateral hemibrain. All glomeruli innervated by pheromone-responsive projection neurons clustered near the antennal nerve entrance of the antennal lobe. The projection neuron with dendrites in the largest glomerulus was tuned specifically to periplanone-D, and adding other components to periplanone-D counteracted the excitation elicited by periplanone-D alone. Likewise, the projection neuron with dendrites in the second largest glomerulus and that with dendrites in a medium-sized glomerulus were tuned to periplanone-A and periplanone-B, respectively. Our results are, therefore, consistent with behavioral findings that periplanone-D alone acts as a primary sex attractant and that other components act as potential behavioral antagonists. Moreover, a comparison of the glomeruli in P. fuliginosa and P. americana suggested that there are differences in the sizes of homologous glomeruli, as well as in the ligands they process.

Olfactory coding in the mosquito antennal lobe: labeled lines or combinatorial code?

Odors serve as important cues for many behaviors in mosquitoes, including host-seeking, foraging, and oviposition. They are detected by olfactory receptor neurons present in the sensory organs, whose axons take this signal to the antennal lobe, the first olfactory processing center in the insect brain. We review the organization and the functioning of the antennal lobe in mosquitoes, focusing on two populations of interneurons present there: the local neurons (LNs) and the projection neurons (PNs). LNs enable information processing in the antennal lobe by providing lateral inhibition and excitation. PNs carry the processed output to downstream neurons in the lateral horn and the mushroom body. We compare the ideas of labeled lines and combinatorial codes, and argue that the PN population encodes odors combinatorially. Throughout this review, we discuss the observations from Aedes, Anopheles, and Culex mosquitoes in the context of previous findings from Drosophila and other insects.

Temporal resolution of spike coding in feedforward networks with signal convergence and divergence

Convergent and divergent structures in the networks that make up biological brains are found across many species and brain regions at various spatial scales. Neurons in these networks fire action potentials, or "spikes," whose precise timing is becoming increasingly appreciated as large sources of information about both sensory input and motor output. In this work, we investigate the extent to which feedforward convergent/divergent network structure is related to the gain in information of spike timing representations over spike count representations. While previous theories on coding in convergent and divergent networks have largely neglected the role of precise spike timing, our model and analyses place this aspect at the forefront. For a suite of stimuli with different timescales, we demonstrate that structural bottlenecks-small groups of neurons post-synaptic to network convergence-have a stronger preference for spike timing codes than expansion layers created by structural divergence. We further show that this relationship can be generalized across different spike-generating models and measures of coding capacity, implying a potentially fundamental link between network structure and coding strategy using spikes. Additionally, we found that a simple network model based on convergence and divergence ratios of a hawkmoth (Manduca sexta) nervous system can reproduce the relative contribution of spike timing information in its motor output, providing testable predictions on optimal temporal resolutions of spike coding across the moth sensory-motor pathway at both the single-neuron and population levels.

Correlative light and electron microscopy reveals the fine circuit structure underlying evidence accumulation in larval zebrafish

Accumulating information is a critical component of most circuit computations in the brain across species, yet its precise implementation at the synaptic level remains poorly understood. Dissecting such neural circuits in vertebrates requires precise knowledge of functional neural properties and the ability to directly correlate neural dynamics with the underlying wiring diagram in the same animal. Here we combine functional calcium imaging with ultrastructural circuit reconstruction, using a visual motion accumulation paradigm in larval zebrafish. Using connectomic analyses of functionally identified cells and computational modeling, we show that bilateral inhibition, disinhibition, and recurrent connectivity are prominent motifs for sensory accumulation within the anterior hindbrain. We also demonstrate that similar insights about the structure-function relationship within this circuit can be obtained through complementary methods involving cell-specific morphological labeling via photo-conversion of functionally identified neuronal response types. We used our unique ground truth datasets to train and test a novel classifier algorithm, allowing us to assign functional labels to neurons from morphological libraries where functional information is lacking. The resulting feature-rich library of neuronal identities and connectomes enabled us to constrain a biophysically realistic network model of the anterior hindbrain that can reproduce observed neuronal dynamics and make testable predictions for future experiments. Our work exemplifies the power of hypothesis-driven electron microscopy paired with functional recordings to gain mechanistic insights into signal processing and provides a framework for dissecting neural computations across vertebrates.

A neural correlate of individual odor preference in Drosophila

Behavior varies even among genetically identical animals raised in the same environment. However, little is known about the circuit or anatomical origins of this individuality. Here, we demonstrate a neural correlate of Drosophila odor preference behavior in the olfactory sensory periphery. Namely, idiosyncratic calcium responses in projection neuron (PN) dendrites and densities of the presynaptic protein Bruchpilot in olfactory receptor neuron (ORN) axon terminals correlate with individual preferences in a choice between two aversive odorants. The ORN-PN synapse appears to be a locus of individuality where microscale variation gives rise to idiosyncratic behavior. Simulating microscale stochasticity in ORN-PN synapses of a 3062 neuron model of the antennal lobe recapitulates patterns of variation in PN calcium responses matching experiments. Conversely, stochasticity in other compartments of this circuit does not recapitulate those patterns. Our results demonstrate how physiological and microscale structural circuit variations can give rise to individual behavior, even when genetics and environment are held constant.

Driver lines for studying associative learning in Drosophila

The mushroom body (MB) is the center for associative learning in insects. In Drosophila, intersectional split-GAL4 drivers and electron microscopy (EM) connectomes have laid the foundation for precise interrogation of the MB neural circuits. However, investigation of many cell types upstream and downstream of the MB has been hindered due to lack of specific driver lines. Here we describe a new collection of over 800 split-GAL4 and split-LexA drivers that cover approximately 300 cell types, including sugar sensory neurons, putative nociceptive ascending neurons, olfactory and thermo-/hygro-sensory projection neurons, interneurons connected with the MB-extrinsic neurons, and various other cell types. We characterized activation phenotypes for a subset of these lines and identified a sugar sensory neuron line most suitable for reward substitution. Leveraging the thousands of confocal microscopy images associated with the collection, we analyzed neuronal morphological stereotypy and discovered that one set of mushroom body output neurons, MBON08/MBON09, exhibits striking individuality and asymmetry across animals. In conjunction with the EM connectome maps, the driver lines reported here offer a powerful resource for functional dissection of neural circuits for associative learning in adult Drosophila.

Impact of developmental temperature on neural growth, connectivity, and function

Environmental temperature dictates the developmental pace of poikilothermic animals. In Drosophila, slower development at lower temperatures results in higher brain connectivity, but the generality of such scaling across temperatures and brain regions and its impact on function are unclear. Here, we show that brain connectivity scales continuously across temperatures, in agreement with a first-principle model that postulates different metabolic constraints for the growth of the brain and the organism. The model predicts brain wiring under temperature cycles and the nonuniform temporal scaling of neural development across temperatures. Developmental temperature has notable effects on odor-driven behavior. Dissecting the circuit architecture and function of neurons in the olfactory pathway, we demonstrate that developmental temperature does not alter odor encoding in first- and second-order neurons, but it shifts the specificity of connections onto third-order neurons that mediate innate behaviors. We conclude that while some circuit computations are robust to the effects of developmental temperature on wiring, others exhibit phenotypic plasticity with possible adaptive advantages.

An integrated anatomical, functional and evolutionary view of the Drosophila olfactory system

The Drosophila melanogaster olfactory system is one of the most intensively studied parts of the nervous system in any animal. Composed of ~60 independent olfactory neuron classes, with several associated hygrosensory and thermosensory pathways, it has been subject to diverse types of experimental analyses. However, synthesizing the available data is limited by the incompleteness and inconsistent nomenclature found in the literature. In this work, we first "complete" the peripheral sensory map through the identification of a previously uncharacterized antennal sensory neuron population expressing Or46aB, and the definition of an exceptional "hybrid" olfactory neuron class comprising functional Or and Ir receptors. Second, we survey developmental, anatomical, connectomic, functional and evolutionary studies to generate an integrated dataset of these sensory neuron pathways - and associated visualizations - creating an unprecedented comprehensive resource. Third, we illustrate the utility of the dataset to reveal relationships between different organizational properties of this sensory system, and the new questions these stimulate. These examples emphasize the power of this resource to promote further understanding of the construction, function and evolution of these neural circuits.

Temporal resolution of spike coding in feedforward networks with signal convergence and divergence

Convergent and divergent structures in the networks that make up biological brains are found across many species and brain regions at various spatial scales. Neurons in these networks fire action potentials, or "spikes", whose precise timing is becoming increasingly appreciated as large sources of information about both sensory input and motor output. While previous theories on coding in convergent and divergent networks have largely neglected the role of precise spike timing, our model and analyses place this aspect at the forefront. For a suite of stimuli with different timescales, we demonstrate that structural bottlenecks- small groups of neurons post-synaptic to network convergence - have a stronger preference for spike timing codes than expansion layers created by structural divergence. Additionally, we found that a simple network model based on convergence and divergence ratios of a hawkmoth (Manduca sexta) nervous system can reproduce the relative contribution of spike timing information in its motor output, providing testable predictions on optimal temporal resolutions of spike coding across the moth sensory-motor pathway at both the single-neuron and population levels. Our simulations and analyses suggest a relationship between the level of convergent/divergent structure present in a feedforward network and the loss of stimulus information encoded by its population spike trains as their temporal resolution decreases, which could be tested experimentally across diverse neural systems in future studies. We further show that this relationship can be generalized across different spike-generating models and measures of coding capacity, implying a potentially fundamental link between network structure and coding strategy using spikes.

Convergent olfactory circuits for courtship in Drosophila revealed by ds-Tango

Animals exhibit sex-specific behaviors that are governed by sexually dimorphic circuits. One such behavior in male Drosophila melanogaster, courtship, is regulated by various sensory modalities, including olfaction. Here, we reveal how sexually dimorphic olfactory pathways in male flies converge at the third-order, onto lateral horn output neurons, to regulate courtship. To achieve this, we developed ds-Tango, a modified version of the monosynaptic tracing and manipulation tool trans-Tango. In ds-Tango, two distinct configurations of trans-Tango are positioned in series, thus providing selective genetic access not only to the monosynaptic partners of starter neurons but also to their disynaptic connections. Using ds-Tango, we identified a node of convergence for three sexually dimorphic olfactory pathways. Silencing this node results in deficits in sex recognition of potential partners. Our results identify lateral horn output neurons required for proper courtship behavior in male flies and establish ds-Tango as a tool for disynaptic circuit tracing.

A Drosophila computational brain model reveals sensorimotor processing

The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain1,2. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity3, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation4. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing5-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes6-10. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.

Whole-brain annotation and multi-connectome cell typing of Drosophila

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

The Budding Neuroscience of Ant Social Behavior

Ant physiology has been fashioned by 100 million years of social evolution. Ants perform many sophisticated social and collective behaviors yet possess nervous systems similar in schematic and scale to that of the fruit fly Drosophila melanogaster, a popular solitary model organism. Ants are thus attractive complementary subjects to investigate adaptations pertaining to complex social behaviors that are absent in flies. Despite research interest in ant behavior and the neurobiological foundations of sociality more broadly, our understanding of the ant nervous system is incomplete. Recent technical advances have enabled cutting-edge investigations of the nervous system in a fashion that is less dependent on model choice, opening the door for mechanistic social insect neuroscience. In this review, we revisit important aspects of what is known about the ant nervous system and behavior, and we look forward to how functional circuit neuroscience in ants will help us understand what distinguishes solitary animals from highly social ones.

Ionotropic receptors mediate olfactory learning and memory in Drosophila

Phenylacetaldehyde (PAH), an aromatic compound, is present in a diverse range of fruits including overripe bananas and prickly pear cactus, the two major host fruits for Drosophila melanogaster. PAH acts as a potent ligand for the ionotropic receptor 84a (IR84a) in the adult fruit fly and it is detected by the IR84a/IR8a heterotetrameric complex. Its role in the male courtship behavior through IR84a as an environmental aphrodisiac is of additional importance. In D. melanogaster, two distinct kinds of olfactory receptors, that is, odorant receptors (ORs) and ionotropic receptors (IRs), perceive the odorant stimuli. They display unique structural, molecular, and functional characteristics in addition to having different evolutionary origins. Traditionally, olfactory cues detected by the ORs such as ethyl acetate, 1-butanol, isoamyl acetate, 1-octanol, 4-methylcyclohexanol, etc. classified as aliphatic esters and alcohols have been employed in olfactory classical conditioning using fruit flies. This underlines the participation of OR-activated olfactory pathways in learning and memory formation. Our study elucidates that likewise ethyl acetate (EA) (an OR-responsive odorant), PAH (an IR-responsive aromatic compound) too can form learning and memory when associated with an appetitive gustatory reinforcer. The association of PAH with sucrose (PAH/SUC) led to learning and formation of the long-term memory (LTM). Additionally, the Orco1, Ir84aMI00501, and Ir8a1 mutant flies were used to confirm the exclusive participation of the IR84a/IR8a complex in PAH/SUC olfactory associative conditioning. These results highlight the involvement of IRs via an IR-activated pathway in facilitating robust olfactory behavior.

Automated Analysis of Neuronal Morphology in 2D Fluorescence Micrographs through an Unsupervised Semantic Segmentation of Neurons

Brain function emerges from a highly complex network of specialized cells that are interlinked by billions of synapses. The synaptic connectivity between neurons is established between the elongated processes of their axons and dendrites or, together, neurites. To establish these connections, cellular neurites have to grow in highly specialized, cell-type dependent patterns covering extensive distances and connecting with thousands of other neurons. The outgrowth and branching of neurites are tightly controlled during development and are a commonly used functional readout of imaging in the neurosciences. Manual analysis of neuronal morphology from microscopy images, however, is very time intensive and prone to bias. Most automated analyses of neurons rely on reconstruction of the neuron as a whole without a semantic analysis of each neurite. A fully-automated classification of all neurites still remains unavailable in open-source software. Here we present a standalone, GUI-based software for batch-quantification of neuronal morphology in two-dimensional fluorescence micrographs of cultured neurons with minimal requirements for user interaction. Single neurons are first reconstructed into binarized images using a Hessian-based segmentation algorithm to detect thin neurite structures combined with intensity- and shape-based reconstruction of the cell body. Neurites are then classified into axon, dendrites and their branches of increasing order using a geodesic distance transform of the cell skeleton. The software was benchmarked against a published dataset and reproduced the phenotype observed after manual annotation. Our tool promises accelerated and improved morphometric studies of neuronal morphology by allowing for consistent and automated analysis of large datasets.

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.

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.

Common principles for odour coding across vertebrates and invertebrates

The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.

Plasticity of the olfactory behaviors in Bactrocera dorsalis under various physiological states and environmental conditions

Insects rely heavily on their olfactory system for various behaviors, including foraging, mating, and oviposition. Numerous studies have demonstrated that insects can adjust their olfactory behaviors in response to different physiological states and environmental conditions. This flexibility allows them to perceive and process odorants according to different conditions. The Oriental fruit fly, Bactrocera dorsalis, is a highly destructive and invasive pest causing significant economic losses to fruit and vegetable crops worldwide. The olfactory behavior of B. dorsalis exhibits strong plasticity, resulting in its successful invasion. To enhance our understanding of B. dorsalis' olfactory behavior and explore potential strategies for behavior control, we have reviewed recent literature on its olfactory plasticity and potential molecular mechanisms.

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.

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.

Cognitive limits of larval Drosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning

Drosophila larvae are an established model system for studying the mechanisms of innate and simple forms of learned behavior. They have about 10 times fewer neurons than adult flies, and it was the low total number of their neurons that allowed for an electron microscopic reconstruction of their brain at synaptic resolution. Regarding the mushroom body, a central brain structure for many forms of associative learning in insects, it turned out that more than half of the classes of synaptic connection had previously escaped attention. Understanding the function of these circuit motifs, subsequently confirmed in adult flies, is an important current research topic. In this context, we test larval Drosophila for their cognitive abilities in three tasks that are characteristically more complex than those previously studied. Our data provide evidence for (i) conditioned inhibition, as has previously been reported for adult flies and honeybees. Unlike what is described for adult flies and honeybees, however, our data do not provide evidence for (ii) sensory preconditioning or (iii) second-order conditioning in Drosophila larvae. We discuss the methodological features of our experiments as well as four specific aspects of the organization of the larval brain that may explain why these two forms of learning are observed in adult flies and honeybees, but not in larval Drosophila.

Nested neural circuits generate distinct acoustic signals during Drosophila courtship

Many motor control systems generate multiple movements using a common set of muscles. How are premotor circuits able to flexibly generate diverse movement patterns? Here, we characterize the neuronal circuits that drive the distinct courtship songs of Drosophila melanogaster. Male flies vibrate their wings toward females to produce two different song modes-pulse and sine song-which signal species identity and male quality. Using cell-type-specific genetic reagents and the connectome, we provide a cellular and synaptic map of the circuits in the male ventral nerve cord that generate these songs and examine how activating or inhibiting each cell type within these circuits affects the song. Our data reveal that the song circuit is organized into two nested feedforward pathways with extensive reciprocal and feedback connections. The larger network produces pulse song, the more complex and ancestral song form. A subset of this network produces sine song, the simpler and more recent form. Such nested organization may be a common feature of motor control circuits in which evolution has layered increasing flexibility onto a basic movement pattern.

Multi-layered maps of neuropil with segmentation-guided contrastive learning

Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.

Dopaminergic systems create reward seeking despite adverse consequences

Resource-seeking behaviours are ordinarily constrained by physiological needs and threats of danger, and the loss of these controls is associated with pathological reward seeking1. Although dysfunction of the dopaminergic valuation system of the brain is known to contribute towards unconstrained reward seeking2,3, the underlying reasons for this behaviour are unclear. Here we describe dopaminergic neural mechanisms that produce reward seeking despite adverse consequences in Drosophila melanogaster. Odours paired with optogenetic activation of a defined subset of reward-encoding dopaminergic neurons become cues that starved flies seek while neglecting food and enduring electric shock punishment. Unconstrained seeking of reward is not observed after learning with sugar or synthetic engagement of other dopaminergic neuron populations. Antagonism between reward-encoding and punishment-encoding dopaminergic neurons accounts for the perseverance of reward seeking despite punishment, whereas synthetic engagement of the reward-encoding dopaminergic neurons also impairs the ordinary need-dependent dopaminergic valuation of available food. Connectome analyses reveal that the population of reward-encoding dopaminergic neurons receives highly heterogeneous input, consistent with parallel representation of diverse rewards, and recordings demonstrate state-specific gating and satiety-related signals. We propose that a similar dopaminergic valuation system dysfunction is likely to contribute to maladaptive seeking of rewards by mammals.

Sensorimotor transformation underlying odor-modulated locomotion in walking Drosophila

Most real-world behaviors - such as odor-guided locomotion - are performed with incomplete information. Activity in olfactory receptor neuron (ORN) classes provides information about odor identity but not the location of its source. In this study, we investigate the sensorimotor transformation that relates ORN activation to locomotion changes in Drosophila by optogenetically activating different combinations of ORN classes and measuring the resulting changes in locomotion. Three features describe this sensorimotor transformation: First, locomotion depends on both the instantaneous firing frequency (f) and its change (df); the two together serve as a short-term memory that allows the fly to adapt its motor program to sensory context automatically. Second, the mapping between (f, df) and locomotor parameters such as speed or curvature is distinct for each pattern of activated ORNs. Finally, the sensorimotor mapping changes with time after odor exposure, allowing information integration over a longer timescale.

Optimal routing to cerebellum-like structures

The vast expansion from mossy fibers to cerebellar granule cells (GrC) produces a neural representation that supports functions including associative and internal model learning. This motif is shared by other cerebellum-like structures and has inspired numerous theoretical models. Less attention has been paid to structures immediately presynaptic to GrC layers, whose architecture can be described as a 'bottleneck' and whose function is not understood. We therefore develop a theory of cerebellum-like structures in conjunction with their afferent pathways that predicts the role of the pontine relay to cerebellum and the glomerular organization of the insect antennal lobe. We highlight a new computational distinction between clustered and distributed neuronal representations that is reflected in the anatomy of these two brain structures. Our theory also reconciles recent observations of correlated GrC activity with theories of nonlinear mixing. More generally, it shows that structured compression followed by random expansion is an efficient architecture for flexible computation.

Heterogeneous receptor expression underlies non-uniform peptidergic modulation of olfaction in Drosophila

Sensory systems are dynamically adjusted according to the animal's ongoing needs by neuromodulators, such as neuropeptides. Neuropeptides are often widely-distributed throughout sensory networks, but it is unclear whether such neuropeptides uniformly modulate network activity. Here, we leverage the Drosophila antennal lobe (AL) to resolve whether myoinhibitory peptide (MIP) uniformly modulates AL processing. Despite being uniformly distributed across the AL, MIP decreases olfactory input to some glomeruli, while increasing olfactory input to other glomeruli. We reveal that a heterogeneous ensemble of local interneurons (LNs) are the sole source of AL MIP, and show that differential expression of the inhibitory MIP receptor across glomeruli allows MIP to act on distinct intraglomerular substrates. Our findings demonstrate how even a seemingly simple case of modulation can have complex consequences on network processing by acting non-uniformly within different components of the overall network.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body

The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.

Olfactory navigation in arthropods

Using odors to find food and mates is one of the most ancient and highly conserved behaviors. Arthropods from flies to moths to crabs use broadly similar strategies to navigate toward odor sources-such as integrating flow information with odor information, comparing odor concentration across sensors, and integrating odor information over time. Because arthropods share many homologous brain structures-antennal lobes for processing olfactory information, mechanosensors for processing flow, mushroom bodies (or hemi-ellipsoid bodies) for associative learning, and central complexes for navigation, it is likely that these closely related behaviors are mediated by conserved neural circuits. However, differences in the types of odors they seek, the physics of odor dispersal, and the physics of locomotion in water, air, and on substrates mean that these circuits must have adapted to generate a wide diversity of odor-seeking behaviors. In this review, we discuss common strategies and specializations observed in olfactory navigation behavior across arthropods, and review our current knowledge about the neural circuits subserving this behavior. We propose that a comparative study of arthropod nervous systems may provide insight into how a set of basic circuit structures has diversified to generate behavior adapted to different environments.

Combinatorial encoding of odors in the mosquito antennal lobe

Among the cues that a mosquito uses to find a host for blood-feeding, the smell of the host plays an important role. Previous studies have shown that host odors contain hundreds of chemical odorants, which are detected by different receptors on the peripheral sensory organs of mosquitoes. But how individual odorants are encoded by downstream neurons in the mosquito brain is not known. We developed an in vivo preparation for patch-clamp electrophysiology to record from projection neurons and local neurons in the antennal lobe of Aedes aegypti. Combining intracellular recordings with dye-fills, morphological reconstructions, and immunohistochemistry, we identify different sub-classes of antennal lobe neurons and their putative interactions. Our recordings show that an odorant can activate multiple neurons innervating different glomeruli, and that the stimulus identity and its behavioral preference are represented in the population activity of the projection neurons. Our results provide a detailed description of the second-order olfactory neurons in the central nervous system of mosquitoes and lay a foundation for understanding the neural basis of their olfactory behaviors.

Generating parallel representations of position and identity in the olfactory system

In Drosophila, a dedicated olfactory channel senses a male pheromone, cis-vaccenyl acetate (cVA), promoting female courtship while repelling males. Here, we show that separate cVA-processing streams extract qualitative and positional information. cVA sensory neurons respond to concentration differences in a 5-mm range around a male. Second-order projection neurons encode the angular position of a male by detecting inter-antennal differences in cVA concentration, which are amplified through contralateral inhibition. At the third circuit layer, we identify 47 cell types with diverse input-output connectivity. One population responds tonically to male flies, a second is tuned to olfactory looming, while a third integrates cVA and taste to coincidentally promote female mating. The separation of olfactory features resembles the mammalian what and where visual streams; together with multisensory integration, this enables behavioral responses appropriate to specific ethological contexts.

Temporal control of neuronal wiring

Wiring an animal brain is a complex process involving a staggering number of cell-types born at different times and locations in the developing brain. Incorporation of these cells into precise circuits with high fidelity is critical for animal survival and behavior. Assembly of neuronal circuits is heavily dependent upon proper timing of wiring programs, requiring neurons to express specific sets of genes (sometimes transiently) at the right time in development. While cell-type specificity of genetic programs regulating wiring has been studied in detail, mechanisms regulating proper timing and coordination of these programs across cell-types are only just beginning to emerge. In this review, we discuss some temporal regulators of wiring programs and how their activity is controlled over time and space. A common feature emerges from these temporal regulators - they are induced by cell-extrinsic cues and control transcription factors capable of regulating a highly cell-type specific set of target genes. Target specificity in these contexts comes from cell-type specific transcription factors. We propose that the spatiotemporal specificity of wiring programs is controlled by the combinatorial activity of temporal programs and cell-type specific transcription factors. Going forward, a better understanding of temporal regulators will be key to understanding the mechanisms underlying brain wiring, and will be critical for the development of in vitro models like brain organoids.

retro-Tango enables versatile retrograde circuit tracing in Drosophila

Transsynaptic tracing methods are crucial tools in studying neural circuits. Although a couple of anterograde tracing methods and a targeted retrograde tool have been developed in Drosophila melanogaster, there is still need for an unbiased, user-friendly, and flexible retrograde tracing system. Here, we describe retro-Tango, a method for transsynaptic, retrograde circuit tracing and manipulation in Drosophila. In this genetically encoded system, a ligand-receptor interaction at the synapse triggers an intracellular signaling cascade that results in reporter gene expression in presynaptic neurons. Importantly, panneuronal expression of the elements of the cascade renders this method versatile, enabling its use not only to test hypotheses but also to generate them. We validate retro-Tango in various circuits and benchmark it by comparing our findings with the electron microscopy reconstruction of the Drosophila hemibrain. Our experiments establish retro-Tango as a key method for circuit tracing in neuroscience research.

A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome

Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensively applied to nanoscale connectomes. Here, we investigate this problem via a case study on the bilateral symmetry of a larval Drosophila brain connectome. We translate notions of 'bilateral symmetry' to generative models of the network structure of the left and right hemispheres, allowing us to test and refine our understanding of symmetry. We find significant differences in connection probabilities both across the entire left and right networks and between specific cell types. By rescaling connection probabilities or removing certain edges based on weight, we also present adjusted definitions of bilateral symmetry exhibited by this connectome. This work shows how statistical inferences from networks can inform the study of connectomes, facilitating future comparisons of neural structures.

The connectome of an insect brain

Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

Experience-dependent plasticity in the olfactory system of Drosophila melanogaster and other insects

It is long known that the nervous system of vertebrates can be shaped by internal and external factors. On the other hand, the nervous system of insects was long assumed to be stereotypic, although evidence for plasticity effects accumulated for several decades. To cover the topic comprehensively, this review recapitulates the establishment of the term "plasticity" in neuroscience and introduces its original meaning. We describe the basic composition of the insect olfactory system using Drosophila melanogaster as a representative example and outline experience-dependent plasticity effects observed in this part of the brain in a variety of insects, including hymenopterans, lepidopterans, locusts, and flies. In particular, we highlight recent advances in the study of experience-dependent plasticity effects in the olfactory system of D. melanogaster, as it is the most accessible olfactory system of all insect species due to the genetic tools available. The partly contradictory results demonstrate that morphological, physiological and behavioral changes in response to long-term olfactory stimulation are more complex than previously thought. Different molecular mechanisms leading to these changes were unveiled in the past and are likely responsible for this complexity. We discuss common problems in the study of experience-dependent plasticity, ways to overcome them, and future directions in this area of research. In addition, we critically examine the transferability of laboratory data to natural systems to address the topic as holistically as possible. As a mechanism that allows organisms to adapt to new environmental conditions, experience-dependent plasticity contributes to an animal's resilience and is therefore a crucial topic for future research, especially in an era of rapid environmental changes.

Virtual Fly Brain-An interactive atlas of the Drosophila nervous system

As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.

Selective integration of diverse taste inputs within a single taste modality

A fundamental question in sensory processing is how different channels of sensory input are processed to regulate behavior. Different input channels may converge onto common downstream pathways to drive the same behaviors, or they may activate separate pathways to regulate distinct behaviors. We investigated this question in the Drosophila bitter taste system, which contains diverse bitter-sensing cells residing in different taste organs. First, we optogenetically activated subsets of bitter neurons within each organ. These subsets elicited broad and highly overlapping behavioral effects, suggesting that they converge onto common downstream pathways, but we also observed behavioral differences that argue for biased convergence. Consistent with these results, transsynaptic tracing revealed that bitter neurons in different organs connect to overlapping downstream pathways with biased connectivity. We investigated taste processing in one type of downstream bitter neuron that projects to the higher brain. These neurons integrate input from multiple organs and regulate specific taste-related behaviors. We then traced downstream circuits, providing the first glimpse into taste processing in the higher brain. Together, these results reveal that different bitter inputs are selectively integrated early in the circuit, enabling the pooling of information, while the circuit then diverges into multiple pathways that may have different roles.

Local shape descriptors for neuron segmentation

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

Behavioral algorithms and neural mechanisms underlying odor-modulated locomotion in insects

Odors released from mates and resources such as a host and food are often the first sensory signals that an animal can detect. Changes in locomotion in response to odors are an important mechanism by which animals access resources important to their survival. Odor-modulated changes in locomotion in insects constitute a whole suite of flexible behaviors that allow insects to close in on these resources from long distances and perform local searches to locate and subsequently assess them. Here, we review changes in odor-mediated locomotion across many insect species. We emphasize that changes in locomotion induced by odors are diverse. In particular, the olfactory stimulus is sporadic at long distances and becomes more continuous at short distances. This distance-dependent change in temporal profile produces a corresponding change in an insect's locomotory strategy. We also discuss the neural circuits underlying odor modulation of locomotion.

Polymer Physics-Based Classification of Neurons

Recognizing that diverse morphologies of neurons are reminiscent of structures of branched polymers, we put forward a principled and systematic way of classifying neurons that employs the ideas of polymer physics. In particular, we use 3D coordinates of individual neurons, which are accessible in recent neuron reconstruction datasets from electron microscope images. We numerically calculate the form factor, F(q), a Fourier transform of the distance distribution of particles comprising an object of interest, which is routinely measured in scattering experiments to quantitatively characterize the structure of materials. For a polymer-like object consisting of n monomers spanning over a length scale of r, F(q) scales with the wavenumber [Formula: see text] as [Formula: see text] at an intermediate range of q, where [Formula: see text] is the fractal dimension or the inverse scaling exponent ([Formula: see text]) characterizing the geometrical feature ([Formula: see text]) of the object. F(q) can be used to describe a neuron morphology in terms of its size ([Formula: see text]) and the extent of branching quantified by [Formula: see text]. By defining the distance between F(q)s as a measure of similarity between two neuronal morphologies, we tackle the neuron classification problem. In comparison with other existing classification methods for neuronal morphologies, our F(q)-based classification rests solely on 3D coordinates of neurons with no prior knowledge of morphological features. When applied to publicly available neuron datasets from three different organisms, our method not only complements other methods but also offers a physical picture of how the dendritic and axonal branches of an individual neuron fill the space of dense neural networks inside the brain.

Drosophila olfaction: past, present and future

Among the many wonders of nature, the sense of smell of the fly Drosophila melanogaster might seem, at first glance, of esoteric interest. Nevertheless, for over a century, the 'nose' of this insect has been an extraordinary system to explore questions in animal behaviour, ecology and evolution, neuroscience, physiology and molecular genetics. The insights gained are relevant for our understanding of the sensory biology of vertebrates, including humans, and other insect species, encompassing those detrimental to human health. Here, I present an overview of our current knowledge of D. melanogaster olfaction, from molecules to behaviours, with an emphasis on the historical motivations of studies and illustration of how technical innovations have enabled advances. I also highlight some of the pressing and long-term questions.