| name | weight-geometry-functional-memory |
| description | Universal organizational regularity showing weight geometry governs functional memory in complex systems across biological, ecological, social, and technological domains. Activation: weight geometry, functional memory, complex systems, interaction strength, hierarchical organization. |
Overview
Paper ID: arXiv:2606.25826v1
Title: Weight geometry governs functional memory in complex systems
Authors: Elkaïoum M. Moutuou, Habib Benali
Published: 2026-06-24
Categories: cond-mat.dis-nn, cs.SI, math-ph, q-bio.NC
URL: https://arxiv.org/abs/2606.25826v1
Core Discovery
Universal organizational regularity: In every complex system studied (biological, ecological, social, technological), real interaction strengths organize memory at greater hierarchical depths than topology alone.
Key Insight
Functional memory in complex systems depends on weight geometry (interaction strength distribution), not just topology (connection structure).
Systems Studied
- Gene regulatory networks
- Neural circuits
- Ecological networks
- Social networks
- Transportation infrastructures
Technical Framework
- Weight geometry analysis of interaction strength distributions
- Functional memory quantification
- Hierarchical organization depth measurement
- Cross-domain comparison methodology
Key Concepts
- Functional Memory: System's ability to retain and use information
- Weight Geometry: Spatial distribution of interaction strengths
- Hierarchical Depth: Level of organizational complexity
- Interaction Strength: Magnitude of connections/flows
Applications
- Neural circuit design optimization
- Gene regulatory network engineering
- Infrastructure network planning
- Social network intervention strategies
Activation Keywords
- weight geometry
- functional memory
- complex system memory
- interaction strength distribution
- network weight analysis
- hierarchical memory organization
Related Skills
- synaptic-weight-distributions-plasticity-geometry
- neural-manifolds-crystallized-embeddings
- effective-plasticity
- neural-population-dynamics
Pitfalls
- Weight distribution estimation requires complete network data
- Hierarchical depth measurement may vary by system type
- Real systems vs idealized models discrepancy
References
