| name | partial-annealing-pattern-decorrelation |
| description | Partial annealing framework for associative neural networks. Core idea: Coupling neural dynamics with slowly evolving patterns via two-temperature-two-timescale framework introduces real parameter n (replica-like) that tunes fast-slow separation. Negative n induces pattern decorrelation, reducing interference and promoting orthogonality, achieving maximal storage capacity αc=1. Outperforms standard decorrelation on biased patterns. Activation: partial annealing, pattern decorrelation, associative memory, Hopfield model, two-temperature framework, replica trick, memory capacity, pattern interference, synaptic plasticity timescale, neural memory organization. |
Partial Annealing and Pattern Decorrelation in Associative Neural Networks
Paper
- Title: Partial annealing and pattern decorrelation in associative neural networks
- Authors: Linda Albanese, Andrea Alessandrelli, Adriano Barra, Silvio Franz, Federico Ricci-Tersenghi
- arXiv: 2605.10304v1 (2026-05-11)
- Category: cond-mat.dis-nn
Core Methodology
Problem
Standard associative networks (Hopfield model) suffer from pattern interference when storing many or biased patterns, limiting storage capacity.
Key Framework: Two-Temperature-Two-Timescale
Couple fast degrees of freedom (neurons) with slow degrees of freedom (synapses/patterns):
- Neurons evolve at temperature T₁, fast timescale
- Patterns evolve at temperature T₂, slow timescale
- Separation parameter n (replica-like, real-valued) tunes the fast-slow gap
Key Findings
- Negative n → Pattern Decorrelation: Induces progressive decorrelation of stored patterns, reducing interference
- Maximal Capacity: Achieves theoretical maximum storage αc = 1
- Biased Patterns: Restores retrieval where standard methods fail
- Adaptive Mechanism: Partial annealing serves as self-organizing memory optimization
Derivation Technique
- Adapts Guerra's interpolation to the partial annealing case
- Derives free energy without analytical continuation
- Validated via mean-field Monte Carlo simulations
Implications
- Offers principled approach to memory organization in complex systems
- Suggests biological systems may use timescale separation for memory optimization
- Relevant for continual learning: slow synaptic consolidation + fast neural dynamics
- Outperforms explicit decorrelation methods on non-trivial pattern distributions
Usage Triggers
- Analyzing associative memory capacity limits
- Studying pattern interference in Hopfield-type networks
- Designing continual learning systems with timescale separation
- Understanding biological memory consolidation mechanisms
- Optimizing storage in associative neural networks
Related Skills
- attractor-metadynamics-neural: Neural attractor landscape dynamics
- kernel-hopfield-attractor-geometry: Hopfield network attractor analysis
- snn-working-memory-heterogeneous-delays: Working memory in spiking networks
