arXiv (All)
open access
Untrained CNNs Exceed Backpropagation in V1 Alignment at High Evaluation Resolution: A Systematic RSA Comparison of Four Learning Rules Against Human fMRI
Revised version (v4): noise ceiling replaced, analyses restricted to stimulus pairs presented in different fMRI runs, repaired evaluation of all five conditions. We compare four learning rules (backpropagation (BP), feedback alignment (FA), predictive coding (PC), and spike-timing-dependent plastici
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
- Deep Learning in Infant Functional Neuroimaging: Challenges, Advances, and Future Directions#1062575
- Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance#141715
- Learning in Stroke#828417
- Blind Identification of Altered Functional Subnetworks in Alzheimer’s Disease Using Resting-State fMRI.#196064
- FFR: Forward-Forward Learning for Regression#1218368
Record · ID 1199800
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