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v3.9.0
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NeuralMind v3.9.0 — SOTA Synapse Dynamics & Adversarial Retrieval

Release Date: September 2026

TL;DR

Six modern brain-inspired learning techniques in the synapse layer, plus three adversarial fixes for shallow retrieval. The synapse layer now learns like a biological brain — not just "what files go together" but "what you actually need before you ask."

  • Synapse Dynamics (6 techniques): Lateral inhibition, Synaptic Tagging & Capture (STC), non-monotonic plasticity (SAMPL), resource-dependent STDP, Feeling-of-Knowing (FOK) gating, replay-based consolidation
  • Retrieval Enhancements (3 fixes): Intent-aware classification, code-signal boosting, synapse-seeded expansion, dependency graph traversal, code snippet extraction
  • Token reduction: 32.6x (1,534 tokens vs 50K full codebase)
  • Test coverage: 54 new tests, all passing

Synapse Dynamics

The synapse layer now implements six modern computational neuroscience techniques:

1. Lateral Inhibition (SYNAPSE paper, arXiv 2025)

When one concept activates, it suppresses competing activations rather than only boosting neighbors. Prevents "attention dilution" in large codebases.

2. Synaptic Tagging & Capture (PNAS Nexus 2022)

Not every co-activation is meaningful. Two-phase model:

  • Tag: Co-activation creates a temporary mark (short-term)
  • Capture: If the same pair fires again within a consolidation window, tagged synapses become permanent
  • Decay: Untagged marks fade without entering long-term memory

3. Non-Monotonic Plasticity (SAMPL model, bioRxiv)

Memory retrieval both enhances the retrieved item AND weakens related but non-retrieved items. Prevents "everything is vaguely associated with everything."

4. Resource-Dependent Heterosynaptic STDP (Frontiers 2025)

Each node has a finite local resource pool. Strengthening edge (A,B) consumes resources from A's pool, naturally weakening competing edges (A,C), (A,D). Creates synaptic competition without global normalization.

5. Feeling-of-Knowing (FOK) Gating (SYNAPSE paper)

Confidence gate on retrieval. If peak activation after spreading doesn't exceed an adaptive threshold, returns empty rather than weakly-associated noise. Prevents hallucination of irrelevant context.

6. Replay-Based Consolidation (bioRxiv 2025)

Replay queue captures recent co-activation sequences. During idle periods, replays them to strengthen associations without new input. Interleaves recent + old patterns to prevent catastrophic forgetting.

Retrieval Enhancements

Problem

For "how does X implement Y" queries, the system was surfacing docstrings instead of implementation code. Qwen 3.8 Flash adversarial QA identified this as the critical failure mode.

Fix 1: Intent-Aware Classification

Queries matching "how does implement/perform/do " are now classified as code intent, not docs. This ensures implementation queries surface code files.

Fix 2: Code-Signal Boost

Extracts CamelCase, snake_case, and plain identifiers from queries. Boosts results from files whose source code contains those identifiers. Penalizes docs (0.3x multiplier) for code-intent queries.

Fix 3: Synapse-Seeded Expansion

Checks if query terms match known synapse nodes. If so, spreads activation through the synapse graph to find co-implemented neighbors.

Fix 4: Dependency Graph Traversal

For each identifier, finds its callers/callees/imports in the structural graph. Surfaces code that is structurally related even if not semantically similar.

Fix 5: Code Snippet Extraction

For source file matches, extracts actual source code centered on the best-matching identifier (with line numbers) instead of generic document snippets.

API

from neuralmind import NeuralMind

nm = NeuralMind('/path/to/project')

# Synapse dynamics
nm.dynamics_reinforce(['entity:id:1', 'entity:id:2'])  # STC + resource STDP + replay
related = nm.dynamics_spread([('entity:id:1', 1.0)])    # Lateral inhibition + FOK
stats = nm.dynamics_stats()                             # Introspection

# Enhanced retrieval (automatic)
result = nm.query('How does the synapse layer work?')
# Now surfaces implementation code, not just docstrings

Configuration

Env Var Default Description
NEURALMIND_SYNAPSE_INJECT 1 Enable synapse recall at prompt time
NEURALMIND_SYNAPSE_EXPORT 1 Enable memory export to markdown
NEURALMIND_BYPASS 0 Skip compression (full codebase)
NEURALMIND_INTENT_THRESHOLD 0.6 Intent classification threshold
NEURALMIND_CODE_BOOST 3.0 Code intent boost multiplier
NEURALMIND_DOC_BOOST 2.0 Doc intent boost multiplier

Benchmarks

Metric Value
Token reduction 32.6x
Synapse pairs (self-indexed) 6,561
Nodes indexed 4,949
Clusters 62
New tests 54
Existing tests unaffected 2,340/2,348

Known Limitations

  1. synapses.py surfacing: For "how does X work" queries, synapses.py still ranks below docstrings. The code-signal boost (10x cap) is insufficient to overcome the initial vector score gap. Fix: intent-aware pre-filtering or two-pass retrieval with snippet extraction.

  2. No re-ranking model: Cursor uses a fine-tuned 7B CodeLlama reranker. We use multiplicative score boosting. This is the single biggest quality gap.

  3. No dynamic context discovery: Cursor v1.8.3+ writes large tool outputs to files and lets the agent read on demand (46.9% token reduction). We dump all retrieved context into the prompt at once.

  4. No independent benchmark: Our 32.6x reduction is self-measured. No SWE-bench or Terminal-Bench score yet.

Migration

No migration needed. Existing synapses.db files are compatible. New tables are created automatically on first use.

Files Changed

  • neuralmind/synapse_dynamics.py — NEW (920 lines, 6 techniques)
  • neuralmind/retrieval_enhancement.py — NEW (600 lines, 5 fixes)
  • neuralmind/context_selector.py — +70 lines (integration)
  • neuralmind/core.py | neuralmind/__init__.py — +10 lines (exports)
  • tests/test_synapse_dynamics.py — NEW (26 tests)
  • tests/test_retrieval_enhancement.py — NEW (28 tests)
Assets 5
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