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)
- 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.
- 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.
- 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.
- 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.
- 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)
- Clear, local signals → local rules.
Define exactly which per-sample / per-head signals cause prune, regrow, or gate changes. Keep it local and cheap. - Sleep schedule + phases.
Prove that alternating consolidation modes yields better stability–plasticity trade-offs than continuous training. - Protected memory sets.
Maintain a small, diverse “do-not-forget” eval + rehearsal set; measure forgetting explicitly. - Energy/latency as first-class metrics.
Tie astrocyte gating to a compute budget; show accuracy per Joule/token improves post-“myelination.” - 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.
