William Gray-Roncal

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.

Recurrent connections enable point attractor dynamics and dimensionality reduction in a connectome-constrained model of the insect learning center

The paper investigates the computational role of recurrent connections in the insect mushroom body using a connectome-constrained model, finding they enable point attractor dynamics and dimensionality reduction.

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.

NeuVue: A scalable and customizable framework for electron microscopy proofreading

NeuVue is a framework for managing and prioritizing proofreading tasks in connectomic datasets, enabling scalable and coordinated error correction by over forty proofreaders.

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.

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

The paper develops a neural model constrained by insect connectivity to perform sensor fusion and online learning for orientation estimation during translation, showing its viability in robotic systems.

Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics

This paper introduces a new statistic called discriminability to improve replicability by distinguishing accidental from systematic deviations in data, showing its effectiveness in both simulated and real datasets.

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.