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The cerebellum is thought to help detect and correct errors between intended and executed commands(1,2) and is critical for social behaviours, cognition and emotion 3 Computations for motor control must be performed quicklyto correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise(7). Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity ofthe network's first layer(8-)(13). However, maximizing encoding capacity reduces the resilience to noise(7). To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers ofthe circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. Because individual muscles may be used in many different behaviors, MN activity must be flexibly coordinated by dedicated premotor circuitry, the organization of which remains largely unknown. Here, we use comprehensive reconstruction of neuron anatomy and synaptic connectivity from volumetric electron microscopy (i.e., connectomics) to analyze the wiring logic of motor circuits controlling theDrosophilaleg and wing. We find that both leg and wing premotor networks are organized into modules that link MNs innervating muscles with related functions. However, the connectivity patterns within leg and wing motor modules are distinct. Leg premotor neurons exhibit proportional gradients of synaptic input onto MNs within each module, revealing a novel circuit basis for hierarchical MN recruitment. In comparison, wing premotor neurons lack proportional synaptic connectivity, which may allow muscles to be recruited in different combinations or with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

To understand how the cerebellar cortex transforms mossy fiber (MF) inputs into Purkinje cell (PC) outputs, it is vital to delineate the elements of this circuit. Candelabrum cells (CCs) are enigmatic interneurons of the cerebellar cortex that have been identified based on their morphology, but their electrophysiological properties, synaptic connections and function remain unknown. Here, we clarify these properties using electrophysiology, single-nucleus RNA sequencing, in situ hybridization and serial electron microscopy in mice. We find that CCs are the most abundant PC layer interneuron. They are GABAergic, molecularly distinct and present in all cerebellar lobules. Their high resistance renders CC firing highly sensitive to synaptic inputs. CCs are excited by MFs and granule cells and are strongly inhibited by PCs. CCs in turn primarily inhibit molecular layer interneurons, which leads to PC disinhibition. Thus, inputs, outputs and local signals converge onto CCs to allow them to assume a unique role in controlling cerebellar output.

Specialized mechanosensory end organs within mammalian skin—hair follicle-associated lanceolate complexes, Meissner corpuscles, and Pacinian corpuscles—enable our perception of light, dynamic touch1. In each of these end organs, fast-conducting mechanically sensitive neurons, called Aβ low-threshold mechanoreceptors (Aβ LTMRs), associate with resident glial cells, known as terminal Schwann cells (TSCs) or lamellar cells, to form complex axon ending structures. Lanceolate-forming and corpuscle-innervating Aβ LTMRs share a low threshold for mechanical activation, a rapidly adapting (RA) response to force indentation, and high sensitivity to dynamic stimuli1–6. How mechanical stimuli lead to activation of the requisite mechanotransduction channel Piezo27–15and Aβ RA-LTMR excitation across the morphologically dissimilar mechanosensory end organ structures is not understood. Here, we report the precise subcellular distribution of Piezo2 and high-resolution, isotropic 3D reconstructions of all three end organs formed by Aβ RA-LTMRs determined by large volume enhanced Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) imaging. We found that within each end organ, Piezo2 is enriched along the sensory axon membrane and is minimally or not expressed in TSCs and lamellar cells. We also observed a large number of small cytoplasmic protrusions enriched along the Aβ RA-LTMR axon terminals associated with hair follicles, Meissner corpuscles, and Pacinian corpuscles. These axon protrusions reside within close proximity to axonal Piezo2, occasionally contain the channel, and often form adherens junctions with nearby non-neuronal cells. Our findings support a unified model for Aβ RA-LTMR activation in which axon protrusions anchor Aβ RA-LTMR axon terminals to specialized end organ cells, enabling mechanical stimuli to stretch the axon in hundreds to thousands of sites across an individual end organ and leading to activation of proximal Piezo2 channels and excitation of the neuron.

Like the vertebrate spinal cord, the insect ventral nerve cord (VNC) mediates limb sensation and motor control. Here, we applied automated tools for electron microscopy (EM) volume alignment, neuron reconstruction, and synapse prediction to create a draft connectome of theDrosophilaVNC. To interpret the VNC connectome, it is crucial to know its relationship with the rest of the body. We therefore mapped the muscle targets of leg and wing motor neurons in the connectome by comparing their morphology to genetic driver lines, dye fills, and x-ray holographic nano-tomography volumes of the fly leg and wing. Knowing the outputs of the connectome allowed us to identify neural circuits that coordinate the wings with the middle and front legs during escape takeoff. We provide the draft VNC connectome and motor neuron atlas, along with tools for programmatic and interactive access, as community resources.

Our ability to sense and move our bodies relies on proprioceptors, sensory neurons that detect mechanical forces within the body. Proprioceptors are diverse: different subtypes detect different features of joint kinematics, such as position, directional movement, and vibration. However, because they are located within complex and dynamic peripheral tissues, the underlying mechanisms of proprioceptor feature selectivity remain poorly understood. Here, we investigate molecular and biomechanical contributions to proprioceptor diversity in theDrosophilaleg. Using single-nucleus RNA sequencing, we found that different proprioceptor subtypes express similar complements of mechanosensory and other ion channels. However, anatomical reconstruction of the proprioceptive organ and connected tendons revealed major biomechanical differences between proprioceptor subtypes. We constructed a computational model of the proprioceptors and tendons, which identified a putative biomechanical mechanism for joint angle selectivity. The model also predicted the existence of a goniotopic map of joint angle among position-tuned proprioceptors, which we confirmed using calcium imaging. Our findings suggest that biomechanical specialization is a key determinant of proprioceptor feature selectivity inDrosophila. More broadly, our discovery of proprioceptive maps in the fly leg reveals common organizational principles between proprioception and other topographically organized sensory systems.