arXiv (OAI Expanded)
open access
Learning rules for complex-valued patterns in networks of oscillators
In this article, we extend learning rules from real binary to complex-valued spins. This formulation allows for a robust and natural representation of grayscale patterns, where spins behave as multi-state neurons and can be stored in a complex-valued weight matrix. We describe a rule that performs b
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
- Learning rules for complex-valued patterns in networks of oscillators#818846
- Nonlinear dynamics in an artificial feedback spin maser II: dependence on feedback configurations and time crystal effects#985718
- Efficient Representation of multicategorical local Hilbert spaces: nonlinear Restricted Boltzmann Machines to Kolmogorov-Arnold Networks#1069919
- The Motile-Units model: Interacting spins model of cell polarization and motility#1034397
- New Quantum Spin Perspective of Quantum Gravity and Space-Time of Mind-Stuff#724449
Record · ID 808854
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