Algebro-deterministic hippocampal memory architecture (VaCoAl) built from Galois-field LFSRs. Provides substrate-level alternative to random scaffold-to-hippocampus projections, algebraically tractable model of multi-hop replay-fidelity decay, and STDP-like path selection. Based on Chuma, Otsuka & Sato (arXiv: 2605.15652). Use when designing brain-inspired memory systems, implementing hippocampal replay mechanisms, building hippocampus-silicon bridge architectures, or modeling memory consolid...
Scanned 9/11/2026
Install to Claude Code
npx -y skills add hiyenwong/ai_collection --skill vacoal-algebro-deterministic-memory --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Vacoal Algebro Deterministic Memory?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/hiyenwong-vacoal-algebro-deterministic-memory)More formats (shields.io, HTML) on the badges page.
---
name: vacoal-algebro-deterministic-memory
description: "Algebro-deterministic hippocampal memory architecture (VaCoAl) built from Galois-field LFSRs. Provides substrate-level alternative to random scaffold-to-hippocampus projections, algebraically tractable model of multi-hop replay-fidelity decay, and STDP-like path selection. Based on Chuma, Otsuka & Sato (arXiv: 2605.15652). Use when designing brain-inspired memory systems, implementing hippocampal replay mechanisms, building hippocampus-silicon bridge architectures, or modeling memory consolidation with algebraic methods."
---
# VaCoAl: Algebro-Deterministic Hippocampal Memory
Algebro-deterministic hyperdimensional memory architecture bridging silicon and hippocampal memory. Based on Chuma, Otsuka & Sato (arXiv: 2605.15652).
## Core Architecture
- Built from Galois-field Linear Feedback Shift Registers (LFSRs)
- Connects Vector-HaSH and Tolman-Eichenbaum Machine (TEM) frameworks
- Deterministic Galois-field diffusion replaces random scaffold-to-hippocampus projections
- Path-integral Confidence Ratio: first algebraically tractable model of multi-hop replay-fidelity decay
- STDP-like path selection emerges from architectural demands
## Key Components
### Galois-Field Diffusion
Deterministic alternative to random projections. LFSRs generate structured, reproducible hyperdimensional vectors with provable algebraic properties.
### Confidence Ratio
Path-integral-based metric for multi-hop replay fidelity decay. Algebraically tractable — enables analytical prediction of memory retrieval quality.
### STDP-like Path Selection
Emerges from requirements of similarity preservation and compositional reversibility. No explicit learning rule needed; selection follows from architecture.
## When to Use
- Designing brain-inspired memory systems with hippocampal replay
- Implementing Vector-HaSH or TEM with substrate-level memory
- Building silicon-hippocampus bridge architectures
- Modeling memory consolidation with algebraic/deterministic methods
- Multi-hop memory retrieval with fidelity guarantees
## Related Concepts
- Vector-HaSH (Vector-based Hippocampal Sequence encoding)
- Tolman-Eichenbaum Machine (TEM)
- Hyperdimensional Computing
- Galois Fields / LFSRs
- STDP (Spike-Timing-Dependent Plasticity)
**Activation**: vacoal, algebro-deterministic memory, hippocampal memory, galois-field LFSR, vector-hash, tolman-eichenbaum, replay fidelity, brain-inspired memory, arXiv:2605.15652
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!