Gray-Roncal, W. R. | Search bioRxiv

bioRxiv2021

Online learning for orientation estimation during translation in an insect ring attractor network

Insect neural systems are a promising source of inspiration for new algorithms for navigation, 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 of orientation estimation error. Overall, our results here may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments. SummaryAn insect neural connectivity-constrained model performs sensor fusion and online learning for orientation estimation.

neuroscience↗

bioRxiv2020

Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity

As biological imaging datasets continue to grow in size, extracting information from large image volumes presents a computationally intensive challenge. State-of-the-art algorithms are almost entirely dominated by the use of convolutional neural network approaches that may be diffcult to run at scale given schedule, cost, and resource limitations. We demonstrate a novel solution for high-resolution electron microscopy brain image volumes that permits the identification of individual neurons and synapses. Instead of conventional approaches whereby voxels are labelled according to the neuron or neuron segment to which they belong, we instead focus on extracting the underlying brain graph represented by synaptic connections between individual neurons while also identifying key features like skeleton similarity and path length. This graph represents a critical step and scaffold for understanding the structure of neuronal circuitry. Our approach recasts the segmentation problem to one of path-finding between keypoints (i.e., connectivity) in an information sharing framework using virtual agents. We create a family of sensors which follow local decision-making rules that perform computationally cheap operations on potential fields to perform tasks such as avoiding cell membranes and finding synapses. These enable a swarm of virtual agents to effciently and robustly traverse three-dimensional datasets, create a sparse segmentation of pathways, and capture connectivity information. We achieve results that meet or exceed state-of-the-art performance at a substantially lower computational cost. This tool offers a categorically different approach to connectome estimation that can augment how we extract connectivity information at scale. Our method is generalizable and may be extended to biomedical imaging problems such as tracing the bronchial trees in lungs or road networks in natural images.

neuroscience↗