Reconstructing the human brain's wiring diagram from axons up.

Due to harmful emissions from vehicles on fossil fuels, rising prices for petroleum products and natural gas, the use of electric vehicles is on the rise. The rapid growth of electric vehicle production will ultimately satisfy these problems in cities. Outside the city, it is advisable to develop intercity electric transport, primarily rail, which can significantly reduce the cost of passenger and freight transportation. The paper considers the possibility of using switched reluctance motors in the traction drives of railway locomotives to replace less efficient, outdated direct current motors. It was designed in a direct current motor housing to study the static characteristics of the switched reluctance traction motor. A simulation model of a switched reluctance motor was developed, and its static characteristics were calculated at various supply voltages and load torque when operating in traction electric drives of railway transport. A comparison of the traction, mechanical and energy characteristics of a switched reluctance motor and a direct current traction motor at different supply voltages was carried out to assess the efficiency of its application. With this approach, as with direct current motors, the supply voltage of the switched reluctance motors was regulated by changing the wiring diagram. An algorithm for controlling the switched reluctance motor through pulse-width modulation of its phase voltage to form a family of traction characteristics is proposed. The study results showed that the proposed approach to regulating the rotation speed of the switched reluctance motor allows for the formation of the required number of traction characteristics and, if necessary, for performing stepwise or smooth transitions between them to regulate the vehicle speed. The results indicate the efficiency of switched reluctance motors in direct current traction electric locomotives.

The Superior Colliculus (SC) is a multimodal integration midbrain structure receiving input from various sensory systems (visual, auditory, and somatosensory) and using this information to guide rapid, reflexive responses. Research using invasive animal model techniques has extensively characterized the SC connectional fingerprint and subcomponent differentiation. However, the extent to which these findings can be generalized to humans remains largely unknown. Here, we developed a fully data-driven approach to examine the wiring diagram of each voxel in the human SC in a cohort of 200 participants. Using diffusion MRI to infer probabilistic tractography, we identified three subregions within the human SC with distinct topography and unique brain-wide connectivity patterns. A superficial division is primarily linked with visual cortical areas, the amygdala, and the posterior thalamus. An intermediate-lateral division is connected predominantly with the auditory and somatosensory cortices and with sectors of the posterior parietal cortex. A deep subregion exhibits preferential connectivity with the brainstem and cerebellum. These in vivo features highlight the heterogeneity in the extrinsic connectivity of the human SC. Probabilistic coupling partly reflects conserved SC interaction motifs reported in other mammals with direct tracing methods, while also showing potential adaptations of cortical-midbrain interactions compatible with human neocortical expansion.

The industrial revolution has brought significant changes in energy consumption patterns, shifting from biomass to fossil fuels, which in turn triggered a surge in greenhouse gas emissions and contributed to climate change. In the era of Industry 4.0, technologies such as the Internet of Things and Augmented Reality have emerged as solutions for creating more efficient and safer systems. This research develops a smart building prototype based on the integration of Internet of Things and Augmented Reality to enhance energy efficiency and safety in electrical installation management. The system uses an ESP32 microcontroller with HTTP protocol to read data from three types of sensors, namely the MQ2 gas sensor, a five-channel flame sensor, and the PZEM-004T sensor for measuring voltage, current, and power comprehensively. All data are transmitted to a Firestore-based server and visualized in real time through a web dashboard built with React and hosted on the Vercel platform. The Augmented Reality feature is developed using AR.js to display electrical wiring diagrams and components such as Miniature Circuit Breakers directly on the electrical panel via mobile devices such as smartphones or tablets. Technicians simply scan the panel to view passive interactive visualizations, including zoom and rotation features, allowing them to understand the installation layout without physically opening the panel. The system is also equipped with an early warning mechanism and automatic activation features such as buzzers and water pumps in the event of fire detection. Additionally, the component management features facilitate the documentation of maintenance history, including component replacements, technical specifications, and visual records, making it easier for technicians to perform maintenance and system audits. The implementation results show that the system successfully integrates Internet of Things, Augmented Reality, and web technologies, offering a potential solution for smart buildings that are efficient, safe, and well-integrated.

Recent technological advances have enabled measurement of the synaptic wiring diagram, or 'connectome', of large neural circuits or entire brains. However, the extent to which such data constrain models of neural dynamics and function is debated. In this study, we developed a theory of connectome-constrained neural networks in which a 'student' network is trained to reproduce the activity of a ground truth 'teacher', representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, the two networks have the same synaptic weights but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We found that a connectome often does not substantially constrain the dynamics of recurrent networks, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory demonstrates that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and determines when activity in such networks can be well predicted. It can also prioritize which neurons to record to most effectively inform such predictions.

Detecting salient visual objects and orienting toward them are commonplace tasks for animals, yet the underlying neural circuit mechanisms remain poorly understood. The fruit fly is an ideal model for a comprehensive analysis of feature detection mechanisms given its complete synaptic wiring diagrams, robust behavioral assays, and cell-type-specific gene expression datasets. We previously showed that columnar visual neurons T3 are required for saccadic orientation toward landscape features during flight. Here, we examine how signals downstream of T3 are processed in the central brain. We identify the LC17 type of visual projection neurons as key postsynaptic targets: they receive strong excitatory input from T3, project to premotor brain regions, and are thus positioned to support visual approach. Using in vivo optical physiology and virtual reality behavior, we demonstrate that LC17 neurons are indeed necessary for object tracking during flight. Furthermore, we find that electrical synapses in LC17 are also required for tracking behavior. Accordingly, we show that the innexin Shaking B ( shakB ) is highly expressed in LC17 dendrites, and genetic perturbations confirm an essential role for gap junctional coupling in this circuit. Our findings reveal mechanisms underlying visual approach, and highlight the interplay between electrical and chemical neurotransmission for rapid object detection and action selection.

The medial prefrontal cortex (MPF) regulates autonomic and neuroendocrine responses to stress1,2 and coordinates goal-directed behaviours such as attention, decision-making and social interactions3-8. However, the underlying mechanisms remain unclear due to incomplete circuit-level MPF characterization7. Here, using integrated neuroanatomical, physiological and behavioural approaches, we construct a comprehensive wiring diagram of the MPF, focused on the dorsal peduncular area (DP)-a poorly understood prefrontal area. We identify its deep (DPd) and superficial (DPs) layers, along with the infralimbic area, as major components of the visceromotor cortex that directly project to hypothalamic and brainstem structures to govern neuroendocrine, sympathetic and parasympathetic output. Notably, the DP functions as a network hub integrating diverse cortical inputs and modulating goal-directed behaviour through a largely unidirectional cortical information flow. On the basis of the mesoscale MPF connectome, we propose a unified network model in which distinct MPF areas orchestrate physiological and behavioural responses to internal and external stimuli.

Community structure is a key feature omnipresent in real-world network data. Plethora of methods have been proposed to reveal subsets of densely interconnected nodes using criteria such as the modularity index. These approaches have been successful for undirected graphs but directed edge information has not yet been dealt with in a satisfactory way. Here, we revisit the concept of directed communities as a mapping between sending and receiving communities. This translates into a definition that we term bimodularity. Using convex relaxation, bimodularity can be optimized with the singular value decomposition of the directed modularity matrix. Subsequently, we propose an edge-based clustering approach to reveal the directed communities including their mappings. The feasibility of the framework is illustrated on a synthetic model and further applied to the neuronal wiring diagram of the Caenorhabditis elegans, for which it yields meaningful feedforward loops of the head and body motion systems. This framework sets the ground for the understanding and detection of community structures in directed networks.

Connectomics, a subfield of neuroscience, aims to map and analyze synapse-level wiring diagrams of the nervous system. While recent advances in deep learning have accelerated automated neuron and synapse segmentation, reconstructing accurate connectomes still demands extensive human proofreading to correct segmentation errors. We present SynAnno, an interactive tool designed to streamline and enhance the proofreading of synaptic annotations in large-scale connectomics datasets. SynAnno integrates into existing neuroscience workflows by enabling guided, neuron-centric proofreading. To address the challenges posed by the complex spatial branching of neurons, it introduces a structured workflow with an optimized traversal path and a 3D mini-map for tracking progress. In addition, SynAnno incorporates fine-tuned machine learning models to assist with error detection and correction, reducing the manual burden and increasing proofreading efficiency. We evaluate SynAnno through a user and case study involving seven neuroscience experts. Results show that SynAnno significantly accelerates synapse proofreading while reducing cognitive load and annotation errors through structured guidance and visualization support. The source code and interactive demo are available at: https://github.com/PytorchConnectomics/SynAnno.

Single-subject morphological brain networks derived from cross-feature correlation of macroscopic MRI-derived morphological measures provide an important means for studying the brain connectome. However, the validity of this approach remains to be confirmed at the microscopic level. Here, we constructed morphological brain networks at the single-cell level by extending features from macroscopic morphological measures to microscopic descriptions of neuronal morphology. We demonstrated the feasibility and generalizability of the method using neurons in the somatosensory cortex of a rat, neurons over the whole brain of a mouse, and neurons in the middle temporal gyrus (MTG) of a human. We found that interneuron morphological similarity was higher for intra- than interclass connections, depended on cytoarchitectonic, chemoarchitectonic, and laminar classification of neurons (rat), differed between regions with different evolutionary timelines (mouse), and correlated with neuronal axonal projections (mouse). Furthermore, highly connected hub neurons were disproportionately from superficial layers (rat), inhibitory neurons (rat), and subcortical regions (mouse), and exhibited unique morphology. Finally, we demonstrated a more segregated, less integrated, and economic network architecture with worse resistance to targeted attacks for neurons in human MTG than neurons in a mouse’s primary visual cortex. Overall, our method provides an alternative avenue to study neuronal wiring diagrams in brains.

Cellular function is driven by the activity proteins in stable complexes. Protein complex assembly depends on the direct physical association of component proteins. Advances in macromolecular structure prediction with tools like AlphaFold and RoseTTAFold have greatly improved our ability to model these interactions in silico, but an all-by-all analysis of the human proteome’s ~200M possible pairs remains computationally intractable. A comprehensive cellular map of direct protein interactions will therefore be an invaluable resource to direct screening efforts. Here, we present DirectContacts2, a machine learning model that distinguishes direct from indirect protein interactions using features derived from over 25,000 mass spectrometry experiments. Applied to ~26 million human protein pairs, our model outperforms previous resources in identifying direct physical interactions and enriches for accurate structural models including ~2,500 new AlphaFold3 models. Our framework enables structural modeling of disease-relevant complexes (e.g. orofacial digital syndrome (OFDS) complex) offering insights into the molecular consequences of pathogenic mutations (OFD1) and broadly, establishes a highly accurate protein wiring diagram of the cell.

The mossy fiber (MF) connections to pyramidal cells in hippocampal CA3 are hypothesized to participate in pattern separation and memory encoding, yet no large-scale neuronal wiring diagram exists for these connections. We assembled a 3D electron microscopy volume (~1×1×0.1mm3) from mouse hippocampal CA3. By proofreading and automated segmentation, we reconstructed and classified all soma-containing neurons-including 1,815 pyramidal cells and 229 inhibitory cells-and over 55,000 MFs. Pyramidal cells receive more numerous MF inputs along a proximodistal gradient. Some distal cells show surprisingly high convergence via relatively small terminals with fewer vesicles. Pyramidal cells share significantly more MF inputs than networks randomized by degree-preserving swap, and are better approximated by networks randomized by proximity-preserving swap. We identify a feedforward inhibitory circuit from MFs via perisomatic interneurons that selectively target a pyramidal subtype. We demonstrated large-scale mapping across levels in the hippocampus-from circuits to cell types to vesicles. The dataset is shared through Pyr, an online platform for hippocampal connectomics.

Single-neuron projectome reveals organization of somatosensory ascending pathways in the mouse brain.

Single-cell multiomic techniques have sparked immense interest in developing a comprehensive multi-modal map of diverse neuronal cell types and their brain-wide projections. However, investigating the complex wiring diagram, spatial organization, transcriptional, and epigenetic landscapes of brain-wide projection neurons is hampered by the lack of efficient and easily adoptable tools. Here we introduce Projection-TAGs, a retrograde AAV platform that allows multiplex tagging of projection neurons using RNA barcodes. By using Projection-TAGs, we performed multiplex projection tracing of the cortex and high-throughput single-cell profiling of the transcriptional and epigenetic landscapes of the cortical projection neurons in female mice. Projection-TAGs can be leveraged to obtain a snapshot of activity-dependent recruitment of distinct projection neurons and their molecular features in the context of a specific stimulus. Given its flexibility, usability, and compatibility, we envision that Projection-TAGs can be readily applied to build a comprehensive multi-modal map of brain neuronal cell types and their projections.

The choice of control system for a flying electric vehicle/quadcopter is very important, especially for users who have never built or driven a flying electric vehicle. Electrical components which include the flight controller, ESC, battery, BLDC motor and other components have an influence in planning electric flying vehicles. Therefore, a BLDC motor control system is needed so that it can operate according to wishes and needs. The purpose of this research is to find out how the control system wiring diagram works, which includes the flight controller wiring diagram as the main flight control and the Electronic Speed ​​Control (ESC) wiring diagram as the BLDC motor speed controller. The choice of flight controller to operate the electric vehicle flight uses DJI Naza M V2 and electronic speed control uses a flier with a 3s-20s cell count configuration.

This study presents the design and implementation of a prototype wiper system using a servo motor, specifically developed for electric vehicles designed for persons with disabilities. The system is structured through several stages, including the creation of a wiring diagram, the development of a control system based on an Arduino Uno microcontroller, and the integration of key components such as a 12V battery, a three-position switch, a step-down LM2596 module, and an RDS3239 servo motor. The control logic enables two-speed wiping modes low and high regulated by user input via the switch. Electrical testing demonstrated that the current drawn by the system was 0.26 A at low speed and 0.37 A at high speed, with corresponding power consumption of 3.12 W and 4.44 W, respectively. These values fall within safe operating limits, indicating energy efficiency suitable for electric vehicle applications. Motion testing showed that the system achieved 30 wipes per minute at low speed and 60 wipes per minute at high speed, with the high-speed mode meeting the minimum functional criteria set by national standards. Angular deviation analysis further revealed that increased speed slightly impacted sweep precision, though still within acceptable tolerances. The results indicate that the developed system not only performs effectively in varying operational conditions but also offers energy-efficient and responsive functionality. This makes it a viable solution for adaptive and accessible mobility technologies in electric vehicles for persons with disabilities.

Connectomics is a field of neuroscience that maps the brain’s intricate wiring diagram. Accurate neuron segmentation from microscopy volumes is essential for automating connectome reconstruction. However, state-of-the-art algorithms use image-based convolutional neural networks limited to local neuron shape context. Thus, we introduce a new framework that reasons over global neuron shape with a novel point affinity transformer. Our framework embeds a (multi-)neuron point cloud into a fixed-length feature set from which we can decode any point pair affinities, enabling clustering neuron point clouds for automatic proofreading. We also show that the learned feature set can easily be mapped to a contrastive embedding space that enables neuron type classification using a simple classifier. Our approach excels in two demanding connectomics tasks: correcting segmentation errors and classifying neuron types. Evaluated on three benchmark datasets derived from state-of-the-art connectomes, our method outperforms point transformers, graph neural networks, and unsupervised clustering baselines.

Sensorimotor computations for learning and behavior rely on precise patterns of synaptic connectivity. Yet, we typically lack the synaptic wiring diagrams for long-range connections between sensory and motor circuits in the brain. Here, we provide the synaptic wiring diagram for sensorimotor circuits involved in learning and production of male zebra finch song, a natural and ethologically relevant behavior. We examined the functional synaptic connectivity from the 4 main sensory afferent pathways onto the three known classes of projection neurons of the song premotor cortical region HVC. Recordings from hundreds of identified projection neurons reveal rules for monosynaptic connectivity and the existence of polysynaptic ensembles of excitatory and inhibitory neuronal populations in HVC. Circuit tracing further identifies novel connections between HVC's presynaptic partners. Our results indicate a modular organization of ensemble-like networks for integrating long-range input with local circuits, providing important context for information flow and computations for learned vocal behavior.

Sensorimotor computations for learning and behavior rely on precise patterns of synaptic connectivity. Yet, we typically lack the synaptic wiring diagrams for long-range connections between sensory and motor circuits in the brain. Here, we provide the synaptic wiring diagram for sensorimotor circuits involved in learning and production of male zebra finch song, a natural and ethologically relevant behavior. We examined the functional synaptic connectivity from the 4 main sensory afferent pathways onto the three known classes of projection neurons of the song premotor cortical region HVC. Recordings from hundreds of identified projection neurons reveal rules for monosynaptic connectivity and the existence of polysynaptic ensembles of excitatory and inhibitory neuronal populations in HVC. Circuit tracing further identifies novel connections between HVC’s presynaptic partners. Our results indicate a modular organization of ensemble-like networks for integrating long-range input with local circuits, providing important context for information flow and computations for learned vocal behavior.

Sensorimotor computations for learning and behavior rely on precise patterns of synaptic connectivity. Yet, we typically lack the synaptic wiring diagrams for long-range connections between sensory and motor circuits in the brain. Here, we provide the synaptic wiring diagram for sensorimotor circuits involved in learning and production of male zebra finch song, a natural and ethologically relevant behavior. We examined the functional synaptic connectivity from the 4 main sensory afferent pathways onto the three known classes of projection neurons of the song premotor cortical region HVC. Recordings from hundreds of identified projection neurons reveal rules for monosynaptic connectivity and the existence of polysynaptic ensembles of excitatory and inhibitory neuronal populations in HVC. Circuit tracing further identifies novel connections between HVC’s presynaptic partners. Our results indicate a modular organization of ensemble-like networks for integrating long-range input with local circuits, providing important context for information flow and computations for learned vocal behavior.