What can we learn from a connectome? We constructed a simplified model of the first two stages of the fly visual system, the lamina and medulla. The resulting hexagonal lattice convolutional network was trained using backpropagation through time to perform object tracking in natural scene videos. Networks initialized with weights from connectome reconstructions automatically discovered well-known orientation and direction selectivity properties in T4 neurons and their inputs, while networks initialized at random did not. Our work is the first demonstration, that knowledge of the connectome can enable in silico predictions of the functional properties of individual neurons in a circuit, leading to an understanding of circuit function from structure alone.

Understanding the structure and operation of any nervous system has been a subject of research for well over a century. A near-term opportunity in this quest is to understand the brain of a model species, the fruit fly Drosophila melanogaster. This is an enticing target given its relatively small size (roughly 200,000 neurons), coupled with the behavioral richness that this brain supports, and the wide variety of techniques now available to study both brain and behavior. It is clear that within a few years we will possess a connectome for D. melanogaster: an electron-microscopy-level description of all neurons and their chemical synaptic connections. Given what we will soon have, what we already know and the research that is currently underway, what more do we need to know to enable us to understand the fly's brain? Here, we itemize the data we will need to obtain, collate and organize in order to build an integrated model of the brain of D. melanogaster.

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB’s α lobe, using a dataset of isotropic 8-nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only six percent of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC>DAN and DAN>MBON. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall.

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron-microscopy-based connectome of the CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly's head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.

Animal behavior is principally expressed through neural control of muscles. Therefore understanding how the brain controls behavior requires mapping neuronal circuits all the way to motor neurons. We have previously established technology to collect large-volume electron microscopy data sets of neural tissue and fully reconstruct the morphology of the neurons and their chemical synaptic connections throughout the volume. Using these tools we generated a dense wiring diagram, or connectome, for a large portion of the Drosophila central brain. However, in most animals, including the fly, the majority of motor neurons are located outside the brain in a neural center closer to the body, i.e. the mammalian spinal cord or insect ventral nerve cord (VNC). In this paper, we extend our effort to map full neural circuits for behavior by generating a connectome of the VNC of a male fly.

The strong dependence of retroviruses, such as human immunodeficiency virus type 1 (HIV-1), on host cell factors is no more apparent than when the endosomal sorting complex required for transport (ESCRT) machinery is purposely disengaged. The resulting potent inhibition of retrovirus release underscores the importance of understanding fundamental structure-function relationships at the ESCRT-HIV-1 interface. Recent studies utilizing advanced imaging technologies have helped clarify these relationships, overcoming hurdles to provide a range of potential models for ESCRT-mediated virus abscission. Here, we discuss these models in the context of prior work detailing ESCRT machinery and the HIV-1 release process. To provide a template for further refinement, we propose a new working model for ESCRT-mediated HIV-1 release that reconciles disparate and seemingly conflicting studies. Expected final online publication date for the Annual Review of Virology Volume 4 is September 29, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Although undulatory swimming is observed in many organisms, the neuromuscular basis for undulatory movement patterns is not well understood. To better understand the basis for the generation of these movement patterns, we studied muscle activity in the nematode Caenorhabditis elegans. Caenorhabditis elegans exhibits a range of locomotion patterns: in low viscosity fluids the undulation has a wavelength longer than the body and propagates rapidly, while in high viscosity fluids or on agar media the undulatory waves are shorter and slower. Theoretical treatment of observed behaviour has suggested a large change in force-posture relationships at different viscosities, but analysis of bend propagation suggests that short-range proprioceptive feedback is used to control and generate body bends. How muscles could be activated in a way consistent with both these results is unclear. We therefore combined automated worm tracking with calcium imaging to determine muscle activation strategy in a variety of external substrates. Remarkably, we observed that across locomotion patterns spanning a threefold change in wavelength, peak muscle activation occurs approximately 45° (1/8th of a cycle) ahead of peak midline curvature. Although the location of peak force is predicted to vary widely, the activation pattern is consistent with required force in a model incorporating putative length- and velocity-dependence of muscle strength. Furthermore, a linear combination of local curvature and velocity can match the pattern of activation. This suggests that proprioception can enable the worm to swim effectively while working within the limitations of muscle biomechanics and neural control.

This paper proposes a novel agglomerative framework for Electron Microscopy (EM) image (or volume) segmentation. For the overall segmentation methodology, we propose a context-aware algorithm that clusters the over-segmented regions of different sub-classes (representing different biological entities) in different stages. Furthermore, a delayed scheme for agglomerative clustering, which postpones the merge of newly formed bodies, is also proposed to generate a more confident boundary prediction. We report significant improvements in both segmentation accuracy and speed attained by the proposed approaches over existing standard methods on both 2D and 3D datasets.

How adherent and contractile systems coordinate to promote cell shape changes is unclear. Here, we define a counterbalanced adhesion/contraction model for cell shape control. Live-cell microscopy data showed a crucial role for a contractile meshwork at the top of the cell, which is composed of actin arcs and myosin IIA filaments. The contractile actin meshwork is organized like muscle sarcomeres, with repeating myosin II filaments separated by the actin bundling protein α-actinin, and is mechanically coupled to noncontractile dorsal actin fibers that run from top to bottom in the cell. When the meshwork contracts, it pulls the dorsal fibers away from the substrate. This pulling force is counterbalanced by the dorsal fibers' attachment to focal adhesions, causing the fibers to bend downward and flattening the cell. This model is likely to be relevant for understanding how cells configure themselves to complex surfaces, protrude into tight spaces, and generate three-dimensional forces on the growth substrate under both healthy and diseased conditions.

Persistent and ramping neural activity in the frontal cortex anticipates specific movements. Preparatory activity is distributed across several brain regions, but it is unclear which brain areas are involved and how this activity is mediated by multi-regional interactions. The cerebellum is thought to be primarily involved in the short-timescale control of movement; however, roles for this structure in cognitive processes have also been proposed. In humans, cerebellar damage can cause defects in planning and working memory. Here we show that persistent representation of information in the frontal cortex during motor planning is dependent on the cerebellum. Mice performed a sensory discrimination task in which they used short-term memory to plan a future directional movement. A transient perturbation in the medial deep cerebellar nucleus (fastigial nucleus) disrupted subsequent correct responses without hampering movement execution. Preparatory activity was observed in both the frontal cortex and the cerebellar nuclei, seconds before the onset of movement. The silencing of frontal cortex activity abolished preparatory activity in the cerebellar nuclei, and fastigial activity was necessary to maintain cortical preparatory activity. Fastigial output selectively targeted the behaviourally relevant part of the frontal cortex through the thalamus, thus closing a cortico-cerebellar loop. Our results support the view that persistent neural dynamics during motor planning is maintained by neural circuits that span multiple brain regions, and that cerebellar computations extend beyond online motor control.