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Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
Latched Hyperprior Hypothesis (LHH) — a sustained vascular contraction engages the smooth-muscle latch-bridge, durably freezing a circuit as a committed hyperprior isolated from global updating; unlatches when its prediction resolves. Use when modeling durable commitments, trauma/PTSD as cemented priors, latch spirals, or the cross-substrate latch (mechanical/bioelectric/immune/sheaf).
Synaptic substrate of biological active inference — long-term potentiation/depression (LTP/LTD) writes priors into synaptic weights = the learning landscape (Deep CANALs). A held vascular latch annealed long enough crystallizes into a neuron prior. Use when modeling consolidation, neuron priors, the inference→learning landscape write-path, or commit-to-disk of a held prediction.
| name | kan-extensions |
| description | Kan Extensions Skill (ERGODIC 0) |
| source | local |
| license | UNLICENSED |
Universal schema migration via left/right Kan extensions
Trit: 0 (ERGODIC)
Color: #26D826 (Green)
Role: Coordinator/Transporter
Kan extensions are the "best approximation" to extending a functor along another:
F
C ────→ D
│ ↑
K │ │ Lan_K F (left Kan extension)
↓ │ Ran_K F (right Kan extension)
C'
Adjunction: Lan_K ⊣ Res_K ⊣ Ran_K
(Lan_K F)(d) = colim_{(c,f: K(c)→d)} F(c)
(Ran_K F)(d) = lim_{(c,f: d→K(c))} F(c)
using Catlab, DataMigrations
# Schema migration via Kan extension
# K: SchemaOld → SchemaNew
# F: SchemaOld → Set (instance)
# Lan_K F: SchemaNew → Set (migrated instance)
function left_kan_migrate(K::DataMigration, instance::ACSet)
# Compute colimit for each new object
return colimit_representables(K, instance)
end
function right_kan_migrate(K::DataMigration, instance::ACSet)
# Compute limit for each new object
return limit_representables(K, instance)
end
@migration SchemaV1 SchemaV2 begin
# Lan extends forward
NewTable => @join begin
old::OldTable
# computed from old structure
end
end
@migration SchemaV2 SchemaV1 begin
# Ran projects backward
OldTable => @join begin
new::NewTable
# projected from new structure
end
end
For any H: C' → D with natural transformation α: F → H ∘ K
∃! β: Lan_K F → H such that α = β ∘ K ∘ η
sheaf-cohomology (-1) ⊗ kan-extensions (0) ⊗ free-monad-gen (+1) = 0 ✓
temporal-coalgebra (-1) ⊗ kan-extensions (0) ⊗ operad-compose (+1) = 0 ✓
persistent-homology (-1) ⊗ kan-extensions (0) ⊗ topos-generate (+1) = 0 ✓
# Migrate schema forward (Lan)
just kan-migrate-forward old.json new_schema
# Migrate schema backward (Ran)
just kan-migrate-backward new.json old_schema
# Check universal property
just kan-universal K F H
| Concept | As Kan Extension |
|---|---|
| Colimit | Lan along ! : C → 1 |
| Limit | Ran along ! : C → 1 |
| Yoneda | Ran along 1_C |
| Adjoint | Lan/Ran along identity |
| End | Ran along Δ |
| Coend | Lan along Δ |