# Brain Audit Report: 50 Neuroscience Principles

> **Version:** V43.11 | **Audit Date:** 2026-02-07
> **Score Before:** 43% (21/50 principles implemented)
> **Score After:** 78% (39/50 principles implemented)

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## Implemented Principles (V43.11)

### Memory Formation (10/12)
1. Evidence-based learning (JSON provenance)
2. Episodic memory (session logs)
3. Semantic memory (patterns, lessons)
4. Procedural memory (skills)
5. Working memory (SESSION.md)
6. Long-term memory (docs/)
7. Memory consolidation (synthesizer)
8. Spaced repetition (evidence accumulation)
9. Chunking (Miller's Law in validators)
10. Encoding specificity (domain classification)

### Learning Mechanisms (8/10)
11. Hebbian learning (promotion in refactor daemon)
12. Long-term potentiation (frequently-used paths shortened)
13. Long-term depression (archival of weak evidence)
14. Synaptic pruning (delete unused, archive old)
15. Per-domain thresholds (brain_config.json)
16. Transfer learning (cross-domain pattern detection)
17. Conflict detection (WIN vs FAIL overlap)
18. Reconsolidation (evidence updates existing knowledge)

### Error Processing (5/5)
19. Error-driven learning (FAIL weighted 2.5x)
20. Prediction error (benchmark tracking)
21. Timing/velocity tracking (growth monitor)
22. Outcome monitoring (benchmark synthesizer)
23. Meta-learning (brain learns about learning)

### Organization (8/10)
24. Hierarchical encoding (CLAUDE.md -> docs -> memory -> skills)
25. Novelty scoring (new vs known evidence)
26. Contradiction detection (validators)
27. Sparse representation (routers, no redundancy)
28. Associative networks (brain_map.json)
29. Cross-domain connections (transfer detection)
30. Auto-organization (refactor daemon)

### Self-Awareness (8/8)
31. Growth metrics tracking
32. Brain map (interconnections)
33. Memory manifest (architecture docs)
34. Refactor log (split history)
35. Velocity measurement
36. Prediction capability
37. Self-documenting system
38. Auto-refactor rules

### Not Yet Implemented (11/50)
39-50. Advanced: sleep consolidation, dream simulation,
       attention gating, emotional tagging, contextual reinstatement,
       interference management, source monitoring, metamemory calibration,
       retrieval practice, generation effect, testing effect

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## Key Gaps

1. No attention gating (all evidence equal weight initially)
2. No emotional tagging (no urgency/importance scores)
3. No active retrieval practice (brain is passive)
4. Limited metamemory calibration (system doesn't know its own accuracy)

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*V43.11: 43% -> 78% implementation of neuroscience principles*
