I'm 18 and I Built a Neuron-Like Memory System for AI

Not just RAG over chunks. Not vector search. Real memory — the kind humans have.

That question grabbed me and wouldn't let go. So I read about the hippocampus, Ebbinghaus forgetting curves, complementary learning systems, slow-wave sleep replay. And then I built it.

The Architecture #

┌──────────────────────────────────────────────────────────────┐

│ 5-TIER MEMORY ARCHITECTURE │

├──────────────────────────────────────────────────────────────┤

│ │

│ TIER 1+2 EPISODIC BUFFER Brain: Hippocampus │

│ ═══════════════════════════════ Speed: <1ms │

│ 64 working + 256 episodic items │

│ Ebbinghaus decay: n^0.3 · e^(-λt) · importance │ │ Forget threshold: 0.05 | Promote threshold: 0.65 │

│ Access-based reinforcement on every read │

│ ↓ │

│ TIER 3 SEMANTIC STORE Brain: Neocortex │

│ ═══════════════════════════════ Speed: ~50ms │

│ ChromaDB v2 · all-mpnet-base-v2 (768-dim) │ │ Hybrid search: dense + BM25 → Reciprocal Rank Fusion │

│ ↓ │

│ TIER 4 KNOWLEDGE GRAPH Brain: Association Cortex │

│ ═══════════════════════════════ Speed: ~100ms │

│ spaCy NER + 30 keyword patterns │

│ NetworkX + SQLite · Multi-hop reasoning │

│ Auto-relation inference: uses/works_on/depends_on │

│ ↓ │

│ TIER 5 COLD ARCHIVE Brain: Distributed Cortex │

│ ═══════════════════════════════ Speed: async │

│ Filesystem JSON · YYYY/MM organization │

│ Full-text search · Thaw to active · Compact summaries │

│ │

├──────────────────────────────────────────────────────────────┤

│ CONSOLIDATION PIPELINE (Sleep Analog) │

│ ═══════════════════════════════════ │

│ Decay → Cluster → Merge(LLM) → Rescore → Promote → │

│ FindRelations(LLM) → Archive → Neurogenesis │

│ Quick: 60ms (every 5min) | Full: ~3s (on idle) │ ├──────────────────────────────────────────────────────────────┤

│ STANDBY NEURON AGENTS │

│ ═══════════════════════════════════ │

│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │

│ │ Personal │ │ Tech │ │ Projects │ ...N agents │

│ │ Agent │ │ Agent │ │ Agent │ │

│ │ │ │ │ │ │ │

│ │ DEEP 💤 │ │ LIGHT 🟡 │ │ DEEP 💤 │ │

│ │ 0 RAM │ │ ~3KB RAM │ │ 0 RAM │ │

│ │ 0 tokens │ │ ready │ │ 0 tokens │ │

│ └──────────┘ └──────────┘ └──────────┘ │

│ │

│ Wake: trigger patterns + centroid similarity │

│ Vote: all agents score → top K form consensus panel │

│ Sleep: return to idle after task (zero token consumption) │

│ Spawn: Neurogenesis creates agents from memory clusters │

│ Prune: inactive agents auto-removed after 30 days │

└──────────────────────────────────────────────────────────────┘

The Two Novel Things #

1. Standby Neuron Agents

Here's what hit me: biological neurons don't all fire at once. Only ~2% are active at any moment. The rest wait, silent, consuming almost nothing.

So I built agents that work the same way:

They wake on trigger pattern matching + embedding similarity, vote in consensus panels (all agents score the query, top K get activated), communicate via sparse blackboard, and return to sleep.

2. Neurogenesis

When the system notices a cluster of memories forming around a new topic — say, 6+ memories about Minecraft — it automatically spawns a new specialized agent for that domain.

If an agent hasn't been woken in 30 days? It gets pruned. Nobody else does either of these.

Sleep as a Feature #

The consolidation pipeline runs like biological sleep — reorganizing, merging duplicates, strengthening important memories, letting the rest decay:

Quick mode: 60ms, zero LLM calls, runs every 5 minutes

Full mode: ~3 seconds, LLM-powered merge + relation discovery,

triggers on idle detection (15+ minutes of inactivity)

Does It Work? #

Episodic Buffer 41/41 ✅

Memory Integration 32/32 ✅

Semantic Store 26/26 ✅

Knowledge Graph 37/37 ✅

Consolidation 31/31 ✅

Standby Agents 42/42 ✅

Cold Archive 27/27 ✅

Cross-Tier (E2E) 88/88 ✅ ────────────────────────────

TOTAL 324/324 ✅

Why I'm Sharing This #

I'm 18, from Slovakia. This started as a random vibecoding project — a voice assistant. But the memory problem grabbed me and wouldn't let go.

The long-term thing that drives me: I believe better memory for AI could eventually help with conditions like Alzheimer's. Computational memory prosthesis. That's the direction I want to explore.

**GitHub:** [github.com/FogyXT/JARVIS](https://github.com/FogyXT/JARVIS)

**License:** AGPL-3.0

Thanks for reading.

source & further reading

dev.to — original article The Data Scientist's Guide to AI Summarization in 2026 Hice un centro de búsqueda de empleo local-first — sin nube, sin telemetría Cogs and Sparks: Who Wins When Machines Learn to Speak Human