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Drosophila male courtship songs were thought to have a fixed structure with song repetition variations introduced unintentionally because of neural noise; this behavioural assay and computational modelling study instead reveals that males use fast changes in sensory information to actively pattern individual song sequences.

Detailed maps, or connectomes, of the fly brain and nervous system reveal how circuit architecture (wiring) shapes both neural activity and behavior. These maps already demonstrate that it is possible to predict function from structure, and provide the essential foundation for biologically realistic models of circuit function.

FlyWire is an online community and a platform for proofreading electron microscopy-based connectome data of the Drosophila brain.

A consensus cell type atlas for the fly brain provides both an intellectual framework and open-source toolchains for brain-scale comparative connectomics.

SLEAP is a versatile deep learning-based multi-animal pose-tracking tool designed to work on videos of diverse animals, including during social behavior.

Analysis of the whole-brain fly connectome reveals high-dimensional dynamics supported by many small independent circuits, motivating a proposal for optogenetic perturbation to efficiently learn a whole-brain causal neural dynamics model.

The network of the fly brain is highly recurrent and displays rich-club organization, with a large population (30%) of preferentially connected neurons.

Three electron microscopy datasets are combined to provide a complete connectomic description of the neural circuitry that makes up the neck connective in Drosophila, including the descending neurons, ascending neurons and sensory ascending neurons.

A deep neural network with ‘knockout training’ is used to model sensorimotor transformations and neural perturbations of male Drosophila melanogaster during visually guided social behaviour and provides predictions and insights into relationships between stimuli, neurons and behaviour.

FlyWire presents a neuronal wiring diagram of the whole fly brain with annotations for cell types, classes, nerves, hemilineages and predicted neurotransmitters, with data products and an open ecosystem to facilitate exploration and browsing.

We create a computational model of the adult Drosophila brain that accurately describes circuit responses upon activation of different gustatory and mechanosensory subtypes and generates experimentally testable hypotheses to describe complete sensorimotor transformations.

An analysis of the Drosophila connectome yields all cell types intrinsic to the optic lobe, and their rules of connectivity.

LEAP is a deep-learning-based approach for the analysis of animal pose. LEAP’s graphical user interface facilitates training of the deep network. The authors illustrate the method by analyzing Drosophila and mouse behavior.

Animals compose behaviors from both sensory cues and internal states. Calhoun et al. develop an unsupervised modeling framework to identify the dynamic internal states that shape social interactions in Drosophila and use the model to identify neurons that modulate the male’s internal state.

Insights into the underlying neuronal circuitry of the Drosophila song production system are provided using song patterning of males near versus far from the female.

Pacheco et al. present new methods for the unbiased recording and cataloging of sensory activity throughout the Drosophila brain and across trials and individuals. They find auditory activity is temporally diverse but present in neurons throughout nearly all central brain regions.

Genetically encoded calcium indicators are commonly used to study cellular activity, but their usefulness is limited by their response kinetics. Here the authors generate indicators with faster responses to calcium events in both Drosophila melanogasterand mammalian neurons.

Complex auditory stimuli such as courtship song are sensed by mechanosensory neurons (JONs) in Drosophila antennae. Here the authors report two forms of adaptation in JONs that correct for antennal position (mean) as well as background sound intensity (variance) to maintain sensitivity to natural sensory stimuli.

Behavioral quantification is changing neuroscience. Pereira et al. provide an overview of the latest advances in motion tracking and behavior prediction and discuss how these methods are used to understand the brain in ways not previously possible.