The detection of visual motion enables sophisticated animal navigation, and studies on flies have provided profound insights into the cellular and circuit bases of this neural computation. The fly's directionally selective T4 and T5 neurons encode ON and OFF motion, respectively. Their axons terminate in one of the four retinotopic layers in the lobula plate, where each layer encodes one of the four directions of motion. Although the input circuitry of the directionally selective neurons has been studied in detail, the synaptic connectivity of circuits integrating T4/T5 motion signals is largely unknown. Here, we report a 3D electron microscopy reconstruction, wherein we comprehensively identified T4/T5's synaptic partners in the lobula plate, revealing a diverse set of new cell types and attributing new connectivity patterns to the known cell types. Our reconstruction explains how the ON- and OFF-motion pathways converge. T4 and T5 cells that project to the same layer connect to common synaptic partners and comprise a core motif together with bilayer interneurons, detailing the circuit basis for computing motion opponency. We discovered pathways that likely encode new directions of motion by integrating vertical and horizontal motion signals from upstream T4/T5 neurons. Finally, we identify substantial projections into the lobula, extending the known motion pathways and suggesting that directionally selective signals shape feature detection there. The circuits we describe enrich the anatomical basis for experimental and computations analyses of motion vision and bring us closer to understanding complete sensory-motor pathways.

Visual features detected by the early visual system must be combined into higher order representations to guide behavioral decision. Although key developmental mechanisms that enable the separation of visual feature channels in early visual circuits have been discovered, relatively little is known about the mechanisms that underlie their convergence in later stages of visual processing. Here we explore the development of a functionally well-characterized sensorimotor circuit in Drosophila melanogaster, the convergence of visual projection neurons (VPNs) onto the dendrites of a large descending neuron called the giant fiber (GF). We find two VPNs encoding different visual features that target the same giant fiber dendrite establish their territories on the dendrite, in part, through sequential axon arrival during development prior to synaptogenesis. Physical occupancy is important to maintain territories, as we find the ablation of one VPN results in expanded dendrite territory of the remaining VPN, and that this compensation enables the GF to remain responsive to ethologically relevant visual stimuli. Our data highlight temporal mechanisms for visual feature convergence and promote the GF circuit, and the Drosophila optic glomeruli where VPN to GF connectivity resides, as an ideal developmental model for investigating complex wiring programs and plasticity in visual feature convergence.

Internal representations are thought to support the generation of flexible, long-timescale behavioral patterns in both animals and artificial agents. Here, we present a novel conceptual framework for how Drosophila use their internal representation of head direction to maintain preferred headings in their surroundings, and how they learn to modify these preferences in the presence of selective thermal reinforcement. To develop the framework, we analyzed flies’ behavior in a classical operant visual learning paradigm and found that they use stochastically generated fixations and directed turns to express their heading preferences. Symmetries in the visual scene used in the paradigm allowed us to expose how flies’ probabilistic behavior in this setting is tethered to their head direction representation. We describe how flies’ ability to quickly adapt their behavior to the rules of their environment may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in the structure of their circuits. Many of the mechanisms we outline may also be relevant for rapidly adaptive behavior driven by internal representations in other animals, including mammals.

The brain generates diverse neuron types which express unique homeodomain transcription factors (TFs) and assemble into precise neural circuits. Yet a mechanistic framework is lacking for how homeodomain TFs specify both neuronal fate and synaptic connectivity. We use Drosophila lamina neurons (L1-L5) to show the homeodomain TF Brain-specific homeobox (Bsh) is initiated in lamina precursor cells (LPCs) where it specifies L4/L5 fate and suppresses homeodomain TF Zfh1 to prevent L1/L3 fate. Subsequently, Bsh activates the homeodomain TF Apterous (Ap) in L4 in a feedforward loop to express the synapse recognition molecule DIP-β, in part by Bsh direct binding a DIP-β intron. Thus, homeodomain TFs function hierarchically: primary homeodomain TF (Bsh) first specifies neuronal fate, and subsequently acts with secondary homeodomain TF (Ap) to activate DIP-β, thereby generating precise synaptic connectivity. We speculate that hierarchical homeodomain TF function may represent a general principle for coordinating neuronal fate specification and circuit assembly.

When foraging in dynamic and uncertain environments, animals can benefit from basing their decisions on smart inferences about hidden properties of the world. Typical theoretical approaches to understand the strategies that animals use in such settings combine Bayesian inference and value iteration to derive optimal behavioral policies that maximize total reward given changing beliefs about the environment. However, specifying these beliefs requires infinite numerical precision; with limited resources, this problem can no longer be separated into optimizing inference and optimizing action selections. To understand the space of behavioral policies in this constrained setting, we enumerate and evaluate all possible behavioral programs that can be constructed from just a handful of states. We show that only a small fraction of the top-performing programs can be constructed by approximating Bayesian inference; the remaining programs are structurally or even functionally distinct from Bayesian. To assess structural and functional relationships among all programs, we developed novel tree embedding algorithms; these embeddings, which are capable of extracting different relational structures within the program space, reveal that nearly all good programs are closely connected through single algorithmic “mutations”. We demonstrate how one can use such relational structures to efficiently search for good solutions via an evolutionary algorithm. Moreover, these embeddings reveal that the diversity of non-Bayesian behaviors originates from a handful of key mutations that broaden the functional repertoire within the space of good programs. The fact that this diversity of behaviors does not significantly compromise performance suggests a novel approach for studying how these strategies generalize across tasks.

Ionotropic glutamate receptors (iGluRs) at postsynaptic terminals mediate the majority of fast excitatory neurotransmission in response to release of glutamate from the presynaptic terminal. Obtaining structural information on the molecular organization of iGluRs in their native environment, along with other signaling and scaffolding proteins in the postsynaptic density (PSD), and associated proteins on the presynaptic terminal, would enhance understanding of the molecular basis for excitatory synaptic transmission in normal and in disease states. Cryo-electron tomography (ET) studies of synaptosomes is one attractive vehicle by which to study iGluR-containing excitatory synapses. Here we describe a workflow for the preparation of glutamatergic synaptosomes for cryo-ET studies. We describe the utilization of fluorescent markers for the facile detection of the pre and postsynaptic terminals of glutamatergic synaptosomes using cryo-laser scanning confocal microscope (cryo-LSM). We further provide the details for preparation of lamellae, between ~100 to 200 nm thick, of glutamatergic synaptosomes using cryo-focused ion-beam (FIB) milling. We monitor the lamella preparation using a scanning electron microscope (SEM) and following lamella production, we identify regions for subsequent cryo-ET studies by confocal fluorescent imaging, exploiting the pre and postsynaptic fluorophores.

Selective serotonin reuptake inhibitors (SSRIs) are the most prescribed treatment for individuals experiencing major depressive disorder (MDD). The therapeutic mechanisms that take place before, during, or after SSRIs bind the serotonin transporter (SERT) are poorly understood, partially because no studies exist of the cellular and subcellular pharmacokinetic properties of SSRIs in living cells. We studied escitalopram and fluoxetine using new intensity- based drug-sensing fluorescent reporters (“iDrugSnFRs”) targeted to the plasma membrane (PM), cytoplasm, or endoplasmic reticulum (ER) of cultured neurons and mammalian cell lines. We also employed chemical detection of drug within cells and phospholipid membranes. The drugs attain equilibrium in neuronal cytoplasm and ER, at approximately the same concentration as the externally applied solution, with time constants of a few s (escitalopram) or 200-300 s (fluoxetine). Simultaneously, the drugs accumulate within lipid membranes by ≥ 18-fold (escitalopram) or 180-fold (fluoxetine), and possibly by much larger factors. Both drugs leave cytoplasm, lumen, and membranes just as quickly during washout. We synthesized membrane-impermeant quaternary amine derivatives of the two SSRIs. The quaternary derivatives are substantially excluded from membrane, cytoplasm, and ER for > 2.4 h. They inhibit SERT transport-associated currents 6- or 11-fold less potently than the SSRIs (escitalopram or fluoxetine derivative, respectively), providing useful probes for distinguishing compartmentalized SSRI effects. Although our measurements are orders of magnitude faster than the “therapeutic lag” of SSRIs, these data suggest that SSRI-SERT interactions within organelles or membranes may play roles during either the therapeutic effects or the “antidepressant discontinuation syndrome”.

Proprioception, the sense of body position and movement, is essential for effective motor control. Because proprioceptive sensory neurons are embedded in complex and dynamic tissues, it has been challenging to understand how they sense and encode mechanical stimuli. Here, we find that proprioceptor neurons in the Drosophila femur are organized into functional groups that are biomechanically specialized to detect features of tibia joint kinematics. The dendrites of position and vibration-tuned proprioceptors receive distinct mechanical signals via the arculum, an elegant mechanical structure that decomposes movement of the tibia joint into orthogonal components. The cell bodies of position-tuned proprioceptors form a goniotopic map of joint angle, whereas the dendrites of vibration-tuned proprioceptors form a tonotopic map of tibia vibration frequency. Our findings reveal biomechanical mechanisms that underlie proprioceptor feature selectivity and identify common organizational principles between proprioception and other topographically organized sensory systems.

Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from . Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.

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.