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.
Immune substrate of biological active inference — microglia + complement (C1q→C3b→CR3) tag and phagocytose synapses, acting as the body's TMS justifier / garbage collector. Immunosurveillance = nogood-repair; autoimmunity = the false nogood. Use when modeling immune editing of neural priors, neuroinflammation gating plasticity, or the immune-vascular latch coupling.
Vascular Clamp Hypothesis (VCH) — vascular contraction freezes local neural patterns and plasticity for its duration, storing a specific active-inference prediction as vascular tension = medium-term memory. Use when modeling predictions-as-tension, the FEP-AI top-down model store, or the witness/hold step of vasocomputation.
Vasocomputation paradigm — vascular smooth-muscle (VSMC) tension as the brain's hidden top-down predictive store, unifying Buddhist taṇhā, active inference, and physical reflex (Johnson 2023). Use when modeling held predictions as somatic tension, locating where FEP top-down models hide, building embodied active-inference agents, or relating suffering/clearing to obligation cohomology H¹.
Orchestrates multiple skills in GF(3)-balanced triplets. Assigns MINUS/ERGODIC/PLUS trits to skills ensuring conservation. Use for multi-skill workflows, parallel skill dispatch, or maintaining GF(3) invariants across skill compositions.
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