---
license: apache-2.0
language:
- en
tags:
- memory
- neuroscience
- ai-memory-architecture
- autonomous-ai
- knowledge-management
- long-term-memory
- episodic-memory
- hebbian-learning
- spaced-repetition
- bayesian
- rag
- agents
pipeline_tag: text-generation
---

# ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.19353663.svg)](https://doi.org/10.5281/zenodo.19353663)
[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![npm @zensation/algorithms](https://img.shields.io/npm/v/@zensation/algorithms.svg?label=%40zensation%2Falgorithms)](https://www.npmjs.com/package/@zensation/algorithms)
[![npm @zensation/core](https://img.shields.io/npm/v/@zensation/core.svg?label=%40zensation%2Fcore)](https://www.npmjs.com/package/@zensation/core)
[![GitHub](https://img.shields.io/badge/GitHub-zensation--ai%2Fzenbrain-black?logo=github)](https://github.com/zensation-ai/zenbrain)

## Overview

ZenBrain is a 7-layer neuroscience-inspired memory architecture for autonomous AI systems. It bridges the gap between biological memory principles and practical AI system design, delivering measurable performance improvements across long-context recall, memory stability, and knowledge retrieval tasks.

**Paper:** [ZenBrain v6 (Zenodo)](https://zenodo.org/records/19481262) | DOI: `10.5281/zenodo.19353663`

## Key Results (10 Experiments)

| Experiment | Metric | Result |
|------------|--------|--------|
| Exp 1 — LoCoMo Retrieval | F1 score | **+21.6%** vs. Flat Store |
| Exp 2 — Layer Ablation | Storage efficiency | **+47.4%** vs. single-layer |
| Exp 3 — Retention Curves | Retention@30d | **89.9%** (vs. 0% pure Ebbinghaus) |
| Exp 4 — Sleep Consolidation | Memory stability | **+37.0%** vs. no-sleep baseline |
| Exp 5 — Hebbian Retrieval | Precision@5 | **0.955** |
| Exp 6 — Bayesian Confidence | Confidence AUC | **0.797** (+49.5% vs. 0.533) |
| Exp 7 — MemoryArena | Retrieval accuracy | **+19.5%** vs. Flat Store |
| Exp 8 — Moderate Ablation | 15-algorithm suite | Cooperative redundancy (no single removal matters) |
| Exp 9 — Challenging Ablation | 15-algorithm suite | **7 of 15** algorithms individually significant |
| Exp 10 — Stress Ablation | 15-algorithm suite | **9 of 15** algorithms individually critical |

> The full 15-algorithm ablation reveals a cooperative survival network with a measurable gradient across three difficulty levels. Sleep consolidation acts as a **1.92x multiplier**. 95 reproducible tests across 4 experiment suites, all with Mulberry32 seeded PRNG (10 seeds).

## Architecture: 7 Memory Layers

```
Layer 1: Working Memory       — Active task focus (capacity-limited, 7±2 items)
Layer 2: Episodic Memory      — Concrete experiences with temporal context
Layer 3: Semantic Memory      — Abstracted facts and relationships
Layer 4: Procedural Memory    — Skills and how-to knowledge
Layer 5: Short-Term Memory    — Session context buffer
Layer 6: Long-Term Memory     — Persistent cross-session knowledge
Layer 7: Core Memory          — Pinned identity and values (Letta-pattern)
```

**Key algorithms:**
- Hebbian Learning (co-activation strengthening, decay, normalization)
- FSRS Spaced Repetition (optimal review scheduling)
- Bayesian Confidence Propagation (uncertainty quantification)
- Sleep Consolidation (Stickgold & Walker 2013 — memory replay simulation)
- Ebbinghaus Decay (forgetting curve modeling)
- Contextual Retrieval (Anthropic method, +67% retrieval accuracy)

## Installation

```bash
# Core algorithms (zero dependencies)
npm install @zensation/algorithms

# Memory layer orchestration
npm install @zensation/core

# PostgreSQL + pgvector adapter
npm install @zensation/adapter-postgres

# SQLite adapter (zero-config)
npm install @zensation/adapter-sqlite
```

## Quick Start

```typescript
import { MemoryCoordinator } from '@zensation/core';
import { PostgresAdapter } from '@zensation/adapter-postgres';

const memory = new MemoryCoordinator({
  adapter: new PostgresAdapter({ connectionString: process.env.DATABASE_URL }),
});

// Store a memory across all relevant layers
await memory.store({
  content: 'ZenBrain uses Hebbian learning for knowledge graph strengthening',
  type: 'semantic',
  importance: 0.9,
});

// Recall with confidence scores
const results = await memory.recall('Hebbian learning', { topK: 5 });
// Returns facts with 95% confidence intervals
```

## Links

- **Paper (v6):** https://zenodo.org/records/19481262
- **PDF:** https://zenodo.org/records/19481262/files/zenbrain-v6.pdf
- **GitHub:** https://github.com/zensation-ai/zenbrain
- **Website:** https://zensation.ai/technologie
- **npm:** https://www.npmjs.com/package/@zensation/algorithms

## Citation

```bibtex
@misc{bering2026zenbrain,
  title   = {ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems},
  author  = {Bering, Alexander},
  year    = {2026},
  doi     = {10.5281/zenodo.19353663},
  url     = {https://zenodo.org/records/19481262},
  note    = {Zenodo Preprint}
}
```

## License

Apache 2.0 — see [LICENSE](https://github.com/zensation-ai/zenbrain/blob/main/LICENSE)