Feedforward Ordering in Neural Connectomes via Feedback Arc Minimization
This work addresses the challenge of analyzing neural connectivity data for neuroscience researchers, but it is incremental as it builds on existing methods with algorithmic refinements.
The authors tackled the problem of revealing feedforward structure in neural connectomes by minimizing feedback arcs in large-scale weighted directed graphs, achieving an improvement in forward edge weight over previous top-performing methods using the FlyWire Connectome Challenge dataset.
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+.