Feedforward Ordering in Neural Connectomes via Feedback Arc Minimization

TL;DR

This paper introduces scalable algorithms to identify feedforward structures in neural connectomes by minimizing feedback arcs, improving the understanding of neural wiring patterns.

Contribution

The authors develop and validate new algorithms combining heuristics and structural analysis to better reveal feedforward organization in large neural graphs.

Findings

Improved total weight of forward edges over previous methods

Effective algorithms validated on large-scale neural data

Efficient implementation in Python on cloud platform

Abstract

We present a suite of scalable algorithms for minimizing feedback arcs in large-scale weighted directed graphs, with the goal of revealing biologically meaningful feedforward structure in neural connectomes. Using the FlyWire Connectome Challenge dataset, we demonstrate the effectiveness of our ranking strategies in maximizing the total weight of forward-pointing edges. Our methods integrate greedy heuristics, gain-aware local refinements, and global structural analysis based on strongly connected components. Experiments show that our best solution improves the forward edge weight over previous top-performing methods. All algorithms are implemented efficiently in Python and validated using cloud-based execution on Google Colab Pro+.