Jordan Matelsky

Connectome quality converges predictably to reveal optimal stopping points during proofreading

The paper introduces a method to estimate proofreading progress in connectomic datasets using global graph invariants, enabling optimal stopping points for reconstruction quality.

Physical contact reveals a hidden layer of cortical architecture

The paper maps physical cell-cell contacts in mouse visual cortex, revealing a dense contactome that exceeds synaptic connectivity and highlights the role of astrocytes in shaping the neural microenvironment.

SmartEM: machine-learning guided electron microscopy

SmartEM accelerates electron microscopy image acquisition by using machine learning to intelligently allocate imaging time, achieving up to 7-fold speedup in connectomic samples.

Comparative Connectomics Highlights Conserved Architectural Synaptic Motifs in the Drosophila Mushroom Body

The paper maps convergent synaptic motifs in the Drosophila Mushroom Body using connectome data, revealing their conserved architecture and potential role in associative learning.

PrimateFace: A Machine Learning Resource for Automated Face Analysis in Human and Non-human Primates

260,000+ primate images from 60+ genera with face boxes and 48/68-point landmarks; detectors and landmark models trained on them work across primates and approach human-trained baselines on human faces.

Functional connectomics spanning multiple areas of mouse visual cortex

The paper presents a functional connectomics dataset of mouse visual cortex, combining calcium imaging with EM reconstruction to study neural circuits and their connectivity.

Automated segmentation of synchrotron-scanned fossils

This paper introduces a free, browser-based tool for automated segmentation of synchrotron-scanned fossils, significantly reducing time and cost compared to manual methods.

Local graph-motif features improve gene interaction network prediction

This paper introduces LAMP features to enhance VGAEs for gene interaction network prediction, showing improved performance on missing-link prediction tasks.

Vimo: Visual Analysis of Neuronal Connectivity Motifs

Vimo enables interactive visualization of neuronal connectivity motifs in large brain networks, allowing experts to sketch and analyze motif instances and chains in 3D.

A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron Extension

This paper introduces a semi-automated pipeline for detecting mesh errors in neuronal EM data, improving the accuracy of connectomic reconstructions by highlighting potential inaccuracies near neuronal tips.

Data-driven motif discovery in biological neural networks

This paper introduces a motif discovery method for biological networks that identifies statistically significant subgraphs from single network examples, addressing challenges in computational cost and statistical significance testing.

Mitotic lineage adds predictive information beyond cell type in the C. elegans connectome

The paper shows that mitotic lineage improves predictions of neural connections in C. elegans beyond cell type alone.

In search of a brain microbiome: A machine learning search pipeline for electron microscopy images of brain tissue

This paper develops a machine learning pipeline for detecting bacteria in electron microscopy images of brain tissue, demonstrating its potential for large-scale analysis and quality control.

Scalable graph analysis tools for the connectomics community

The paper introduces a software framework for comparative connectomics to analyze large-scale, diverse brain connectome datasets efficiently.

Circuit motifs and graph properties of connectome development in C. elegans

The paper analyzes C. elegans connectomes across developmental stages using graph and motif analysis to identify evolving network properties and stable patterns.

DotMotif: An open-source tool for connectome subgraph isomorphism search and graph queries

DotMotif is an open-source tool that enables efficient subgraph isomorphism search and graph queries in connectomics data, demonstrated on simulated and real datasets.

intern: Integrated Toolkit for Extensible and Reproducible Neuroscience

The paper introduces a toolkit called intern to unify computational backends and abstract computer science challenges in neuroscience data analysis.

A substrate for modular, extensible data-visualization

The paper introduces a data-visualization framework and Python library to simplify collaboration and visualization of complex datasets across research teams.

Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity

This paper introduces a novel method using virtual agents and path-finding to efficiently identify neuron connectivity in electron microscopy images, achieving performance comparable to state-of-the-art methods at lower computational cost.

The Block Object Storage Service (bossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery

The paper introduces a cloud-native service for managing large-scale neuroscience datasets, enabling scalable storage, visualization, and sharing through an API and web interface.

Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets

The paper develops a scalable, reproducible framework for processing large neuroimaging datasets using open-source tools and standardized pipelines.