Justin Joyce

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.

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.

The paper introduces a computational model of generative replay in fruit fly mushroom bodies to improve continual learning, showing a 20% performance increase on CIFAR-100.

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

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.

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.