Glia → ML Mappings (actionable)

1) Microglia = Data & Structure Pruning

Biology: Microglia tag and remove weak/unused synapses (often via complement proteins like C1q/C3) and clear debris.
ML analogs (implement now):

• Complement-style “tagging” of data:
  • Tag training items by low information value (near-duplicate, boilerplate), toxicity/risk, or staleness.
  • Signals: high predictability (low loss variance), low gradient contribution, high duplication (MinHash/SimHash), low novelty (embedding similarity to corpus centroid).
• Synapse-level pruning:
  • Magnitude/head/neuron pruning with re-growth (dynamic sparse training / RigL).
  • Attention head SNR pruning: drop heads with persistently low gradient × attention mass.
• KV-cache pruning at inference:
  • Prune tokens from context whose attention scores fall below a running threshold; keep a small protected set (named entities, instructions).

What to measure: validation loss stdev; gradient contribution per example/head; coverage vs. compression; latency gains.

2) Astrocytes = Gating, Routing, and Priority Signals

Biology: Astrocytes modulate synaptic transmission (tripartite synapse), set local gain, and coordinate regional activity via calcium waves.
ML analogs:

• Astrocyte controller (small policy net) that emits neuromodulatory scalars per layer/head/batch:
  • Up-/down-weight attention heads, experts, or adapters based on surprise (loss spikes), novelty, or task context.
• Tripartite-synapse gating for context windows:
  • A side-channel gate regulates which tokens are eligible for attention (salience-gated attention mask).
• Curriculum & sampler modulation:
  • Adaptive sampling that boosts rare-but-important exemplars (high Fisher info, high error, or from “key memories”).

What to measure: ablation utility of modulated components, routing entropy, stability (no oscillatory collapse).

3) Oligodendrocytes = Throughput & Reliability (Myelination)

Biology: Oligodendrocytes myelinate axons, increasing conduction speed and reliability.
ML analogs:

• Implicit “myelination” via compilation/caching:
  • Cache stable subgraphs and common reasoning templates; integrate retrieval for canonical facts (RAG index = myelin sheath around knowledge paths).
• Quantization/distillation as efficiency myelin:
  • Distill frequently-used competencies into smaller adapters; quantize hot paths to reduce latency.
• Latency-aware routing:
  • “Speed limits” drive gates to prefer cheaper paths when accuracy loss is marginal.

What to measure: tokens/sec, energy/token, accuracy degradation under quantization/distillation.

4) Glymphatic System = Waste Clearance & Normalization

Biology: Sleep-driven clearance of metabolites; synaptic homeostasis (global downscaling).
ML analogs:

• Nightly corpus hygiene: dedupe, remove drifted spam, rebalance long-tail classes.
• Homeostatic downscaling: periodic weight norm resets, activation norm targets, and weight decay pulses to prevent runaway amplification.
• Optimizer “washout”: occasional EMA-only consolidation checkpoints; zeroing momentum buffers.

What to measure: exploding/vanishing activation incidents, norm drift, training stability after “sleep”.

5) REM/NREM Cycles = Consolidation Schedules

Biology: NREM slow waves (downscaling + replay), REM (high ACh, associative integration).
ML analogs:

• Two-phase training loop:
  • NREM phase: low learning rate, replay + homeostatic scaling, dedupe and prune (microglia sweep).
  • REM phase: higher plasticity on salient mini-batches, allow larger step sizes or relaxed regularization for associative integration.
• Targeted Memory Reactivation (TMR):
  • During “sleep,” upsample tagged experiences (rare errors, safety-critical cases) for consolidation.

What to measure: retention on key memories, catastrophic forgetting (∆ on “protected” eval sets), post-sleep generalization gains.

Signals to Drive the System (the “neurochemistry”)

• Acetylcholine analog (plasticity on): raise LR/allow head growth during REM-like phases or high-surprise segments.
• Norepinephrine/serotonin analog (stability/precision): lower LR, stronger regularization during NREM-like consolidation.
• Dopamine analog (salience/reward): tag batches with engagement/importance (e.g., RLHF advantages, human feedback confidence, safety criticality) to bias replay.

Minimal Implementation Plan (weekend lab)

1. Data complement tags

Compute per-sample tags:
- novelty = 1 - max cosine(sim(x, sample_bank))
- surprise = recent rolling loss_zscore(x)
- utility = gradient_norm(x) or Fisher diag proxy
- risk = toxicity/bias score (classifier)
- dup = MinHash Jaccard > τ
Keep if: (novelty or utility high) and dup low; else queue for prune.

2. Astrocyte gate (tiny policy net)

• Inputs: batch-level {mean loss, loss var, novelty avg, risk avg, latency budget}.
• Outputs: per-layer scalars {attention_gain, dropout_scale, head_mask_probs}.
• Train with auxiliary objective: improve validation while meeting a latency/energy constraint.

3. Night cycle (NREM→REM)

• NREM: run replay of tagged data, apply weight decay ↑, norm clamps, prune low-SNR heads, dedupe set maintenance.
• REM: higher LR on salient batches; allow temporary head/adaptor growth; commit useful growth via sparsity regularizers.

4. Structural plasticity

• Use DST (RigL) or lottery-ticket style periodic prune–regrow with gates guided by astrocyte policy.
• Protect crucial weights via EWC (Fisher penalty) to avoid catastrophic forgetting.

5. Myelination

• Distill frequently-invoked chains-of-thought into compact adapters; quantize those adapters.
• Add a RAG index for facts; log which retrievals recur → pre-warm cache.

What to Focus on (to make this a theory with teeth)

1. Clear, local signals → local rules.
   Define exactly which per-sample / per-head signals cause prune, regrow, or gate changes. Keep it local and cheap.
2. Sleep schedule + phases.
   Prove that alternating consolidation modes yields better stability–plasticity trade-offs than continuous training.
3. Protected memory sets.
   Maintain a small, diverse “do-not-forget” eval + rehearsal set; measure forgetting explicitly.
4. Energy/latency as first-class metrics.
   Tie astrocyte gating to a compute budget; show accuracy per Joule/token improves post-“myelination.”
5. Causality & ablations.
   For each glial mechanism, run on/off ablations with identical seeds; report gains (accuracy, robustness, calibration, bias metrics, and cost).

Pitfalls & Guards

• Over-pruning → brittle models. Use shadow copies & rollback; prune gradually with regrowth.
• Routing collapse (one-head-to-rule-them-all). Add entropy bonuses or load balancing loss.
• Bias amplification if pruning removes minority/rare cases. Use rarity-aware tags and protected strata.
• Compute creep from controllers. Enforce a tight FLOPs budget for astrocyte/microglia modules.

• ASTRO-PRUNE: Astrocyte-gated, microglial-pruned consolidation.
• MYELIN-RAG: Retrieval myelination via cached facts and distilled adapters.

• GliaLoop: A sleep–glia training regime for stable-plastic LLMs.