arXiv (All)
open access
Hodge-Decomposition of Brain Networks
We analyze brain networks by decomposing them into three orthogonal components: gradient, curl, and harmonic flows, through the Hodge decomposition, a technique advantageous for capturing complex topological features. A Wasserstein distance based topological inference is developed to determine the s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- RW-Flow: One-Step Generation on Compact Manifolds via Wasserstein Gradient Flows#1144238
- Structural characteristics of local cortical networks wired by distance dependent connectivity rules.#1039938
- Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis#259481
- Physically Organized Latent Spaces in Unsupervised Autoencoders: Evidence from Aerodynamic Databases#1218186
- Topological Periodicity Test (TopPT) via Confidence Bound of Time-Delay Embeddings#1104923
Record · ID 1446654
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive