Arlo Sheridan

The paper uses spatial transcriptomics to analyze cardiomyocyte states in pressure-overloaded mouse hearts, finding pro-fibrotic states linked to microenvironmental changes.

The paper introduces PRISM, a platform that uses combinatorial protein barcodes to enable self-correcting neuron tracing with molecular context, improving tracing accuracy and handling spatial gaps.

This paper introduces a method to generate dense 3D segmentations from sparse 2D annotations, achieving accuracy comparable to dense annotations with significantly reduced human effort.

This paper introduces an auxiliary learning task using Local Shape Descriptors to improve neuron segmentation in electron microscopy volumes, achieving performance comparable to state-of-the-art methods with greater efficiency.

The paper maps cerebellar connectivity in mice and shows how structured, non-random motifs improve pattern separation while maintaining encoding capacity.

The paper develops a method to map synaptic activity in vivo using FIBSEM and machine learning, revealing distribution patterns of vesicles in synapses of awake mice.

This study identifies the neural network structure of the Drosophila heading direction system, confirming it contains ring attractor components and revealing novel structural features.

The paper presents an automatic method to identify synaptic partners in a whole-brain Drosophila EM dataset using convolutional neural networks, achieving high accuracy in predicting synaptic connections.