Auditory activity is diverse and widespread throughout the central brain of Drosophila

Neural correlates of individual facial recognition in a social wasp

Individual recognition is critical for social behavior across species. Whether recognition is mediated by circuits specialized for social information processing has been a matter of debate. Here we examine the neurobiological underpinning of individual visual facial recognition in Polistes fuscatus paper wasps. Front-facing images of conspecific wasps broadly increase activity across many brain regions relative to other stimuli. Notably, we identify a localized subpopulation of neurons in the protocerebrum which show specialized selectivity for front-facing wasp images, which we term wasp cells. These wasp cells encode information regarding the facial patterns, with ensemble activity correlating with facial identity. Wasp cells are strikingly analogous to face cells in primates, indicating that specialized circuits are likely an adaptive feature of neural architecture to support visual recognition.

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.

Network Statistics of the Whole-Brain Connectome of Drosophila

Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Mapping the neural dynamics of locomotion across the Drosophila brain

Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.

From connectome to effectome: learning the causal interaction map of the fly brain

A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.

Associative Learning of Quantitative Mechanosensory Stimuli in Honeybees

The proboscis extension response (PER) has been widely used to evaluate honeybees' (Apis mellifera) learning and memory abilities, typically by using odors and visual cues for the conditioned stimuli. Here we asked whether honeybees could learn to distinguish between different magnitudes of the same type of stimulus, given as two speeds of air flux. By taking advantage of a novel automated system for administering PER experiments, we determined that the bees were highly successful when the lower air flux was rewarded and less successful when the higher flux was rewarded. Importantly, since our method includes AI-assisted analysis, we were able to consider subthreshold responses at a high temporal resolution; this analysis revealed patterns of rapid generalization and slowly acquired discrimination between the rewarded and unrewarded stimuli, as well as indications that the high air flux may have been mildly aversive. The learning curve for these mechanosensory stimuli, at least when the lower flux is rewarded, more closely mimics prior data from olfactory PER studies rather than visual ones, possibly in agreement with recent findings that the insect olfactory system is also sensitive to mechanosensory information. This work demonstrates a new modality to be used in PER experiments and lays the foundation for deeper exploration of honeybee cognitive processes when posed with complex learning challenges.

Inferring neural dynamics of memory during naturalistic social communication

Memory processes in complex behaviors like social communication require forming representations of the past that grow with time. The neural mechanisms that support such continually growing memory remain unknown. We address this gap in the context of fly courtship, a natural social behavior involving the production and perception of long, complex song sequences. To study female memory for male song history in unrestrained courtship, we present 'Natural Continuation' (NC)-a general, simulation-based model comparison procedure to evaluate candidate neural codes for complex stimuli using naturalistic behavioral data. Applying NC to fly courtship revealed strong evidence for an adaptive population mechanism for how female auditory neural dynamics could convert long song histories into a rich mnemonic format. Song temporal patterning is continually transformed by heterogeneous nonlinear adaptation dynamics, then integrated into persistent activity, enabling common neural mechanisms to retain continuously unfolding information over long periods and yielding state-of-the-art predictions of female courtship behavior. At a population level this coding model produces multi-dimensional advection-diffusion-like responses that separate songs over a continuum of timescales and can be linearly transformed into flexible output signals, illustrating its potential to create a generic, scalable mnemonic format for extended input signals poised to drive complex behavioral responses. This work thus shows how naturalistic behavior can directly inform neural population coding models, revealing here a novel process for memory formation.

Behavioral neuroscience: Computation in individual neurons

Information coding is generally thought to emerge from fast activity across thousands of neurons. A recent study shows that many features of a sophisticated decision-action sequence are encoded by the slow activity of individual command neurons.

Flexible circuit mechanisms for context-dependent song sequencing

Sequenced behaviours, including locomotion, reaching and vocalization, are patterned differently in different contexts, enabling animals to adjust to their environments. How contextual information shapes neural activity to flexibly alter the patterning of actions is not fully understood. Previous work has indicated that this could be achieved via parallel motor circuits, with differing sensitivities to context1,2. Here we demonstrate that a single pathway operates in two regimes dependent on recent sensory history. We leverage the Drosophila song production system3 to investigate the role of several neuron types4-7 in song patterning near versus far from the female fly. Male flies sing 'simple' trains of only one mode far from the female fly but complex song sequences comprising alternations between modes when near her. We find that ventral nerve cord (VNC) circuits are shaped by mutual inhibition and rebound excitability8 between nodes driving the two song modes. Brief sensory input to a direct brain-to-VNC excitatory pathway drives simple song far from the female, whereas prolonged input enables complex song production via simultaneous recruitment of functional disinhibition of VNC circuitry. Thus, female proximity unlocks motor circuit dynamics in the correct context. We construct a compact circuit model to demonstrate that the identified mechanisms suffice to replicate natural song dynamics. These results highlight how canonical circuit motifs8,9 can be combined to enable circuit flexibility required for dynamic communication.

The spatial and temporal structure of neural activity across the fly brain

What are the spatial and temporal scales of brainwide neuronal activity? We used swept, confocally-aligned planar excitation (SCAPE) microscopy to image all cells in a large volume of the brain of adult Drosophila with high spatiotemporal resolution while flies engaged in a variety of spontaneous behaviors. This revealed neural representations of behavior on multiple spatial and temporal scales. The activity of most neurons correlated (or anticorrelated) with running and flailing over timescales that ranged from seconds to a minute. Grooming elicited a weaker global response. Significant residual activity not directly correlated with behavior was high dimensional and reflected the activity of small clusters of spatially organized neurons that may correspond to genetically defined cell types. These clusters participate in the global dynamics, indicating that neural activity reflects a combination of local and broadly distributed components. This suggests that microcircuits with highly specified functions are provided with knowledge of the larger context in which they operate.

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.

Wide-field fluorescence lifetime imaging of neuron spiking and subthreshold activity in vivo

The development of voltage-sensitive fluorescent probes suggests fluorescence lifetime as a promising readout for electrical activity in biological systems. Existing approaches fail to achieve the speed and sensitivity required for voltage imaging in neuroscience applications. We demonstrated that wide-field electro-optic fluorescence lifetime imaging microscopy (EO-FLIM) allows lifetime imaging at kilohertz frame-acquisition rates, spatially resolving action potential propagation and subthreshold neural activity in live adult Drosophila. Lifetime resolutions of <5 picoseconds at 1 kilohertz were achieved for single-cell voltage recordings. Lifetime readout is limited by photon shot noise, and the method provides strong rejection of motion artifacts and technical noise sources. Recordings revealed local transmembrane depolarizations, two types of spikes with distinct fluorescence lifetimes, and phase locking of spikes to an external mechanical stimulus.

Cell-Surface Targeting of Fluorophores in Drosophila for Rapid Neuroanatomy Visualization

Visualizing neuronal anatomy often requires labor-intensive immunohistochemistry on fixed and dissected brains. To facilitate rapid anatomical staining in live brains, we used genetically targeted membrane tethers that covalently link fluorescent dyes for in vivo neuronal labeling. We generated a series of extracellularly trafficked small-molecule tethering proteins, HaloTag-CD4 (Kirk et al. Front. Neurosci. 2021, 15, 754027) and SNAPf-CD4, which directly label transgene-expressing cells with commercially available ligand-substituted fluorescent dyes. We created stable transgenic Drosophila reporter lines, which express extracellular HaloTag-CD4 and SNAPf-CD4 with LexA and Gal4 drivers. Expressing these enzymes in live Drosophila brains, we labeled the expression patterns of various Gal4 driver lines recapitulating histological staining in live-brain tissues. Pan-neural expression of SNAPf-CD4 enabled the registration of live brains to an existing template for anatomical comparisons. We predict that these extracellular platforms will not only become a valuable complement to existing anatomical methods but will also prove useful for future genetic targeting of other small-molecule probes, drugs, and actuators.

Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila

Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here, we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal, and a visual motion signal associated with rotation of the animal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion.

Complex representation of taste quality by second-order gustatory neurons in Drosophila

Sweet and bitter compounds excite different sensory cells and drive opposing behaviors. However, it remains unclear how sweet and bitter tastes are represented by the neural circuits linking sensation to behavior. To investigate this question in Drosophila, we devised trans-Tango(activity), a strategy for calcium imaging of second-order gustatory projection neurons based on trans-Tango, a genetic transsynaptic tracing technique. We found spatial overlap between the projection neuron populations activated by sweet and bitter tastants. The spatial representation of bitter tastants in the projection neurons was consistent, while that of sweet tastants was heterogeneous. Furthermore, we discovered that bitter tastants evoke responses in the gustatory receptor neurons and projection neurons upon both stimulus onset and offset and that bitter offset and sweet onset excite overlapping second-order projections. These findings demonstrate an unexpected complexity in the representation of sweet and bitter tastants by second-order neurons of the gustatory circuit.

Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning

Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applied to denoise images, their applications to downstream analyses, such as recovering high-SNR calcium traces, have been limited. Further, these methods require temporally-sequential pre-registered data acquired at ultrafast rates. Here, we demonstrate a supervised deep-denoising method to circumvent these tradeoffs for several applications, including whole-brain imaging, large-field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans. Our framework has 30× smaller memory footprint, and is fast in training and inference (50–70 ms); it is highly accurate and generalizable, and further, trained with only small, non-temporally-sequential, independently-acquired training datasets (∼500 pairs of images). We envision that the framework will enable faster and long-term imaging experiments necessary to study neuronal mechanisms of many behaviors.

Volumetric functional imaging is widely used for recording neuron activities in vivo for many experimental organisms. Here the authors report supervised deep-denoising methods for improved whole-brain imaging, large field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans.

Systems neuroscience: Auditory processing at synaptic resolution

Deeper layers of the auditory system have not been fully mapped in any model system. A new study links comprehensive connectomics of Drosophila song perception circuits to physiological response profiles.

Neural network organization for courtship-song feature detection in Drosophila

Animals communicate using sounds in a wide range of contexts, and auditory systems must encode behaviorally relevant acoustic features to drive appropriate reactions. How feature detection emerges along auditory pathways has been difficult to solve due to challenges in mapping the underlying circuits and characterizing responses to behaviorally relevant features. Here, we study auditory activity in the Drosophila melanogaster brain and investigate feature selectivity for the two main modes of fly courtship song, sinusoids and pulse trains. We identify 24 new cell types of the intermediate layers of the auditory pathway, and using a new connectomic resource, FlyWire, we map all synaptic connections between these cell types, in addition to connections to known early and higher-order auditory neurons-this represents the first circuit-level map of the auditory pathway. We additionally determine the sign (excitatory or inhibitory) of most synapses in this auditory connectome. We find that auditory neurons display a continuum of preferences for courtship song modes and that neurons with different song-mode preferences and response timescales are highly interconnected in a network that lacks hierarchical structure. Nonetheless, we find that the response properties of individual cell types within the connectome are predictable from their inputs. Our study thus provides new insights into the organization of auditory coding within the Drosophila brain.

Imaging whole-brain activity to understand behavior

The brain evolved to produce behaviors that help an animal inhabit the natural world. During natural behaviors, the brain is engaged in many levels of activity from the detection of sensory inputs to decision-making to motor planning and execution. To date, most brain studies have focused on small numbers of neurons that interact in limited circuits. This allows analyzing individual computations or steps of neural processing. During behavior, however, brain activity must integrate multiple circuits in different brain regions. The activities of different brain regions are not isolated, but may be contingent on one another. Coordinated and concurrent activity within and across brain areas is organized by (1) sensory information from the environment, (2) the animal's internal behavioral state, and (3) recurrent networks of synaptic and non-synaptic connectivity. Whole-brain recording with cellular resolution provides a new opportunity to dissect the neural basis of behavior, but whole-brain activity is also mutually contingent on behavior itself. This is especially true for natural behaviors like navigation, mating, or hunting, which require dynamic interaction between the animal, its environment, and other animals. In such behaviors, the sensory experience of an unrestrained animal is actively shaped by its movements and decisions. Many of the signaling and feedback pathways that an animal uses to guide behavior only occur in freely moving animals. Recent technological advances have enabled whole-brain recording in small behaving animals including nematodes, flies, and zebrafish. These whole-brain experiments capture neural activity with cellular resolution spanning sensory, decision-making, and motor circuits, and thereby demand new theoretical approaches that integrate brain dynamics with behavioral dynamics. Here, we review the experimental and theoretical methods that are being employed to understand animal behavior and whole-brain activity, and the opportunities for physics to contribute to this emerging field of systems neuroscience.

Differential mechanisms underlie trace and delay conditioning in Drosophila

Two forms of associative learning-delay conditioning and trace conditioning-have been widely investigated in humans and higher-order mammals¹. In delay conditioning, an unconditioned stimulus (for example, an electric shock) is introduced in the final moments of a conditioned stimulus (for example, a tone), with both ending at the same time. In trace conditioning, a 'trace' interval separates the conditioned stimulus and the unconditioned stimulus. Trace conditioning therefore relies on maintaining a neural representation of the conditioned stimulus after its termination (hence making distraction possible²), to learn the conditioned stimulus-unconditioned stimulus contingency³; this makes it more cognitively demanding than delay conditioning⁴. Here, by combining virtual-reality behaviour with neurogenetic manipulations and in vivo two-photon brain imaging, we show that visual trace conditioning and delay conditioning in Drosophila mobilize R2 and R4m ring neurons in the ellipsoid body. In trace conditioning, calcium transients during the trace interval show increased oscillations and slower declines over repeated training, and both of these effects are sensitive to distractions. Dopaminergic activity accompanies signal persistence in ring neurons, and this is decreased by distractions solely during trace conditioning. Finally, dopamine D1-like and D2-like receptor signalling in ring neurons have different roles in delay and trace conditioning; dopamine D1-like receptor 1 mediates both forms of conditioning, whereas the dopamine D2-like receptor is involved exclusively in sustaining ring neuron activity during the trace interval of trace conditioning. These observations are similar to those previously reported in mammals during arousal⁵, prefrontal activation⁶ and high-level cognitive learning^7,8.

Large-scale neural recordings call for new insights to link brain and behavior

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain.

Beyond cognitive templates: re-examining template metaphors used for animal recognition and navigation

The term 'cognitive template' originated from work in human-based cognitive science to describe a literal, stored, neural representation used in recognition tasks. As the study of cognition has expanded to non-human animals, the term has diffused to describe a wider range of animal cognitive tools and strategies that guide action through the recognition of and discrimination between external states. One potential reason for this non-standardized meaning and variable employment is that researchers interested in the broad range of animal recognition tasks enjoy the simplicity of the cognitive template concept and have allowed it to become shorthand for many dissimilar or unknown neural processes without deep scrutiny of how this metaphor might comport with underlying neurophysiology. We review the functional evidence for cognitive templates in fields such as perception, navigation, communication, and learning, highlighting any neural correlates identified by these studies. We find that the concept of cognitive templates has facilitated valuable exploration at the interface between animal behavior and cognition, but the quest for a literal template has failed to attain mechanistic support at the level of neurophysiology. This may be the result of a misled search for a single physical locus for the 'template' itself. We argue that recognition and discrimination processes are best treated as emergent and, as such, may not be physically localized within single structures of the brain. Rather, current evidence suggests that such tasks are accomplished through synergies between multiple distributed processes in animal nervous systems. We thus advocate for researchers to move towards a more ecological, process-oriented conception, especially when discussing the neural underpinnings of recognition-based cognitive tasks.

Molecular and neural mechanisms regulating sexual motivation of virgin female Drosophila

During courtship, multiple information sources are integrated in the brain to reach a final decision, i.e., whether or not to mate. The brain functions for this complex behavior can be investigated by genetically manipulating genes and neurons, and performing anatomical, physiological, and behavioral analyses. Drosophila is a powerful model experimental system for such studies, which need to be integrated from molecular and cellular levels to the behavioral level, and has enabled pioneering research to be conducted. In male flies, which exhibit a variety of characteristic sexual behaviors, we have accumulated knowledge of many genes and neural circuits that control sexual behaviors. On the other hand, despite the importance of the mechanisms of mating decision-making in females from an evolutionary perspective (such as sexual selection), research on the mechanisms that control sexual behavior in females has progressed somewhat slower. In this review, we focus on the pre-mating behavior of female Drosophila melanogaster, and introduce previous key findings on the neuronal and molecular mechanisms that integrate sensory information and selective expression of behaviors toward the courting male.

The neural basis for a persistent internal state in Drosophila females

Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify the neural circuit mechanisms that underlie persistent activity and contribute to long-lasting changes in behavior. Here, we show that a subset of Doublesex+ pC1 neurons in the Drosophila female brain, called pC1d/e, can drive minutes-long changes in female behavior in the presence of males. Using automated reconstruction of a volume electron microscopic (EM) image of the female brain, we map all inputs and outputs to both pC1d and pC1e. This reveals strong recurrent connectivity between, in particular, pC1d/e neurons and a specific subset of Fruitless+ neurons called aIPg. We additionally find that pC1d/e activation drives long-lasting persistent neural activity in brain areas and cells overlapping with the pC1d/e neural network, including both Doublesex+ and Fruitless+ neurons. Our work thus links minutes-long persistent changes in behavior with persistent neural activity and recurrent circuit architecture in the female brain.