Raphael Norman-Tenazas

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.

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.

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

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