Insect neural systems are a promising source of inspiration for new navigation algorithms, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing orientation estimation error. Overall, our results may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments.

While insects like Drosophila are flying, aerodynamic instabilities require that they make millisecond-timescale adjustments to their wing motion to stay aloft and on course. These stabilization reflexes can be modeled as a proportional-integral (PI) controller; however, it is unclear how such control might be instantiated in insects at the level of muscles and neurons. Here, we show that the b1 and b2 motor units—prominent components of the fly's steering muscles system—modulate specific elements of the PI controller: the angular displacement (integral, I) and angular velocity (proportional, P), respectively. Moreover, these effects are observed only during the stabilization of pitch. Our results provide evidence for an organizational principle in which each muscle contributes to a specific functional role in flight control, a finding that highlights the power of using top-down behavioral modeling to guide bottom-up cellular manipulation studies.

A surprising finding of recent studies in mouse is the dominance of widespread movement-related activity throughout the brain, including in early sensory areas. In awake subjects, failing to account for movement risks misattributing movement-related activity to other (e.g., sensory or cognitive) processes. In this article, we 1) review task designs for separating task-related and movement-related activity, 2) review three 'case studies' in which not considering movement would have resulted in critically different interpretations of neuronal function, and 3) discuss functional couplings that may prevent us from ever fully isolating sensory, motor, and cognitive-related activity. Our main thesis is that neural signals related to movement are ubiquitous, and therefore ought to be considered first and foremost when attempting to correlate neuronal activity with task-related processes.

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits. Expected final online publication date for the , Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Expansion microscopy (ExM) is a powerful technique to overcome the diffraction limit of light microscopy that can be applied in both tissues and cells. In ExM, samples are embedded in a swellable polymer gel to physically expand the sample and isotropically increase resolution in x, y and z. The maximum resolution increase is limited by the expansion factor of the polymer gel, which is four-fold for the original ExM protocol. Variations on the original ExM method have been reported that allow for greater expansion factors, for example using iterative expansion, but at the cost of ease of adoption or versatility. Here, we systematically explore the ExM recipe space and present a novel method termed Ten-fold Robust Expansion Microscopy (TREx) that, like the original ExM method, requires no specialized equipment or procedures to carry out. We demonstrate that TREx gels expand ten-fold, can be handled easily, and can be applied to both thick tissue sections and cells enabling high-resolution subcellular imaging in a single expansion step. We show that applying TREx on antibody-stained samples can be combined with off-the-shelf small molecule stains for both total protein and membranes to provide ultrastructural context to subcellular protein localization.

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parametrized distributions in two sensory modalities, using data from insect mechanosensors and neurons of mammalian primary visual cortex. We show that these random feature neurons perform a randomized wavelet transform on inputs which removes high frequency noise and boosts the signal. Our result makes a significant theoretical connection between the foundational concepts of receptive fields in neuroscience and random features in artificial neural networks. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

The endoplasmic reticulum (ER) has a complex morphology comprised of stacked sheets, tubules, and three-way junctions, which together function as a platform for protein synthesis of membrane and secretory proteins. Specific ER subdomains are thought to be spatially organized to enable protein synthesis activity, but precisely where these domains are localized is unclear, especially relative to the plethora of organelle interactions taking place on the ER. Here, we use single-molecule tracking of ribosomes and mRNA in combination with simultaneous imaging of ER to assess the sites of membrane protein synthesis on the ER. We found that ribosomes were widely distributed throughout different ER morphologies, but the synthesis of membrane proteins (including Type I, II, and multi-spanning) and an ER luminal protein (Calreticulin) occurred primarily at three-way junctions. Lunapark played a key role in stabilizing transmembrane protein mRNA at three-way junctions. We additionally found that translating mRNAs coding for transmembrane proteins are in the vicinity of lysosomes and translate through a cap-independent but eIF2-dependent mechanism. These results support the idea that discrete ER subdomains co-exist with lysosomes to support specific types of protein synthesis activities, with ER-lysosome interactions playing an important role in the translation of secretome mRNAs.

Perhaps the most valuable single set of resources for genetic studies of Drosophila melanogaster is the collection of multiply-inverted chromosomes commonly known as balancer chromosomes. Balancers prevent the recovery of recombination exchange products within genomic regions included in inversions and allow perpetual maintenance of deleterious alleles in living stocks and the execution of complex genetic crosses. Balancer chromosomes have been generated traditionally by exposing animals to ionizing radiation and screening for altered chromosome structure or for unusual marker segregation patterns. These approaches are tedious and unpredictable, and have failed to produce the desired products in some species. Here I describe transgenic tools that allow targeted chromosome rearrangements in Drosophila species. The key new resources are engineered reporter genes containing introns with yeast recombination sites and enhancers that drive fluorescent reporter genes in multiple body regions. These tools were used to generate a doubly-inverted chromosome 3R in D. simulans that serves as an effective balancer chromosome.

Neurotransmitter release is mediated by proteins that drive synaptic vesicle fusion with the presynaptic plasma membrane. While soluble N-ethylmaleimide sensitive factor attachment protein receptors (SNAREs) form the core of the fusion apparatus, additional proteins play key roles in the fusion pathway. Here, we report that the C-terminal amphipathic helix of the mammalian accessory protein, complexin (Cpx), exerts profound effects on membranes, including the formation of pores and the efficient budding and fission of vesicles. Using nanodisc-black lipid membrane electrophysiology, we demonstrate that the membrane remodeling activity of Cpx modulates the structure and stability of recombinant exocytic fusion pores. Cpx had particularly strong effects on pores formed by small numbers of SNAREs. Under these conditions, Cpx increased the current through individual pores 3.5-fold, and increased the open time fraction from roughly 0.1 to 1.0. We propose that the membrane sculpting activity of Cpx contributes to the phospholipid rearrangements that underlie fusion by stabilizing highly curved membrane fusion intermediates.

Animals retain some but not all experiences in long-term memory (LTM). Sleep supports LTM retention across animal species. It is well established that learning experiences enhance post-learning sleep. However, the underlying mechanisms of how learning mediates sleep for memory retention are not clear. Drosophila males display increased amounts of sleep after courtship learning. Courtship learning depends on Mushroom Body (MB) neurons, and post-learning sleep is mediated by the sleep-promoting ventral Fan-Shaped Body neurons (vFBs). We show that post-learning sleep is regulated by two opposing output neurons (MBONs) from the MB, which encode a measure of learning. Excitatory MBONs-γ2α'1 becomes increasingly active upon increasing time of learning, whereas inhibitory MBONs-β'2mp is activated only by a short learning experience. These MB outputs are integrated by SFS neurons, which excite vFBs to promote sleep after prolonged but not short training. This circuit may ensure that only longer or more intense learning experiences induce sleep and are thereby consolidated into LTM.