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.

Emerging neuroimaging datasets (collected through modalities such as Electron Microscopy, Calcium Imaging, or X-ray Microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational expertise or resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. We developed an ecosystem of neuroimaging data analysis pipelines that utilize open source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, that connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets, but may be applied to similar problems in other domains.

1.AbstractNeuVue is a software platform created for large-scale proofreading of machine segmentation and neural circuit reconstruction in high-resolution electron microscopy connectomics datasets. The NeuVue platform provides a robust web-based interface for proofreaders to collaboratively view, annotate, and edit segmentation and connectivity data. A backend queuing service organizes proofreader tasks into purpose-driven task types and increases proofreader throughput by limiting proofreader actions to simple, atomic operations. A collection of analytical dashboards, data visualization tools, and Application Program Interface (API) capabilities provide stakeholders real-time access to proofreading progress at an individual proofreader level as well as insights on task generation priorities. NeuVue is agnostic to the underlying data being proofread and improves upon the traditional proofreader experience through quality-of-life features that streamline complex editing operations such as splitting and merging objects in dense nanoscale segmentation.NeuVue heavily leverages cloud resources to enable proofreaders to simultaneously access and edit data on the platform. Production-quality features such as load-balancing, auto-scaling, and pre-deployment testing are all integrated into the platform’s cloud architecture. Additionally, NeuVue is powered by well-supported open-source connectomics tools from the community such as Neuroglancer, PyChunkedGraph, and Connectomics Annotation Versioning Engine (CAVE). The modular design of NeuVue facilitates easy integration and adoption of useful community tools to allow proofreaders to take advantage of the latest improvements in data visualization, processing, and analysis.We demonstrate our framework through proofreading of the mouse visual cortex data generated on the IARPA MICrONS Project. This effort has yielded over 40,000 proofreader edits across the 2 petavoxels of “Minnie” neuroimaging data. 44 unique proofreaders of various skill levels have logged a cumulative 3,740 proofreading hours, and we have been able to validate the improved connectivity of thousands of neurons in the volume. With sustained development on the platform, new integrated error detection and error correction capabilities, and continuous improvements to the proofreader model, we believe that the NeuVue framework can enable high-throughput proofreading for large-scale connectomics datasets of the future.

Network science is a powerful tool that can be used to better explore the complex structure of brain networks. Leveraging graph and motif analysis tools, we interrogate C. elegans connectomes across multiple developmental time points and compare the resulting graph characteristics and substructures over time. We show the evolution of the networks and highlight stable invariants and patterns as well as those that grow or decay unexpectedly, providing a substrate for additional analysis.

No abstract