neuro-memory-architecture - SKILL.md Agent Skill

name: neuro-memory-architecture description: > Neuroscience-inspired memory architecture design for AI agents. Maps biological memory systems (working, short-term, episodic, semantic, procedural, core, cross-context) to AI agent memory layers. Integrates neuroscience models including Hebbian learning, synaptic consolidation, sleep-based replay, and active forgetting. Use when designing agent memory systems, building neuro-inspired AI architectures, or implementing biologically-plausible memory mechanisms. Triggers: neuro memory, brain-inspired memory, zenbrain, cognitive memory architecture, hippocampal memory, memory consolidation AI, agent long-term memory.

Neuroscience-Inspired Memory Architecture

Design AI agent memory systems modeled on biological brain memory mechanisms.

Seven Memory Layers

Based on ZenBrain (arXiv:2604.23878) and AI-Meets-Brain (arXiv:2512.23343):

Layer	Biological Analog	AI Implementation	Retention
Working	Prefrontal cortex	Context window / scratchpad	Seconds
Short-term	Hippocampal CA1	Recent conversation buffer	Minutes-hours
Episodic	Hippocampal CA3	Time-stamped event log	Days-weeks
Semantic	Neocortex	Knowledge graph / embeddings	Months-years
Procedural	Basal ganglia	Learned skills / tool patterns	Permanent
Core	Brainstem	System prompts / identity	Permanent
Cross-context	Default mode	Cross-session synthesis	Permanent

Key Neuroscience Mechanisms

Consolidation (Hours → Days)

Transfer short-term → long-term via replay
Prioritize emotionally salient / frequently accessed memories
Implement during idle periods (analogous to sleep)

Forgetting (Selective)

Decay unused memories exponentially
Remove contradictory / superseded information
Preserve core patterns while losing noise

Reconsolidation (On Recall)

Update memories each time they're retrieved
Merge new context into existing memory traces
Strengthen frequently accessed paths

Hebbian Learning

"Neurons that fire together wire together"
Strengthen co-occurring memory connections
Build associative knowledge graphs

Implementation Patterns

Pattern 1: Multi-layer Memory Buffer

agent.memory = {
    working: [],          # current context
    short_term: deque(maxlen=100),  # recent events
    episodic: SQLite,     # timestamped logs
    semantic: vector_db,  # knowledge embeddings
    procedural: skills,   # learned patterns
    core: system_prompt,  # identity
    cross_context: graph  # cross-session links
}

Pattern 2: Consolidation Cycle

on_idle():
    replay = select_salient(short_term)
    for memory in replay:
        consolidate_to(episodic, memory)
        update_semantic_graph(memory)
    prune_expired(short_term)

Pattern 3: Retrieval with Reconsolidation

retrieve(query):
    results = vector_search(semantic, query)
    for r in results:
        r.strength += 1  # Hebbian reinforcement
        r.last_accessed = now()
    return merge_and_rank(results)

Design Principles

Separation of concerns — Each layer has distinct access patterns
Graceful degradation — Upper layers can fail without breaking core
Energy efficiency — Only consolidate high-value memories
Anti-catastrophic — New learning doesn't overwrite old knowledge
Contextual binding — Memories tagged with situation/emotion metadata

Validation

Memory retrieval latency < 100ms for working, < 1s for semantic
Consolidation doesn't block active operations
Forgetting rate balances storage cost vs recall accuracy
Cross-context links enable transfer learning across domains

Related Research

ZenBrain: 7-layer architecture with 15 neuroscience models (arXiv:2604.23878)
AI Meets Brain: cognitive neuroscience → LLM agents survey (arXiv:2512.23343)
Hippocampal replay credit assignment for deep learning
Sleep-inspired homeostatic regularization for continual learning