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compressive-vasomotion

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.

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compressive-vasomotion

The Compressive Vasomotion Hypothesis (CVH) (Johnson 2023): the vasomotion reflex — the undulation of VSMCs wrapping every blood vessel — functions as a compression sweep on nearby neural resonances, collapsing and merging fragile, ambivalent patterns (the "Bayesian blur") into a more durable, definite state. This is the +1 / generate leg of the vasocomputation timescale triad: the fast initial "grab" that creates specificity.

Use When

  • Modeling the ~25–100 ms taṇhā "fast grabby thing" as a physical compression event
  • Reframing precision-weighting as two terms: sensory clarity available vs. compression forcefulness applied (the KL divergence between them ≈ a number for taṇhā)
  • Representing the generative collapse step where a superposition of interpretations is jostled into one
  • Coupling local electromagnetic-field dissonance → vasomotion trigger → resonance collapse

Core Concepts

  • Compression sweep: motifs of vasomotion, reflexive reactions to uncertainty, and patterns of taṇhā are equivalent. The brain pushes "what is" toward stable, satisfactory, controllable — the three marks inverted.
  • Bayesian blur: an ambivalent SOHM superposition that has not yet committed; CVH collapses it (cf. collapsing a probability distribution / pinching a critical network into a definite circuit).
  • Trigger: VSMC contractions are expected to be triggered by local dissonance in the electromagnetic field and to act back on neurons via ephaptic coupling, reduced blood flow, and altered local resonance.
  • Right amount ≠ zero: a finite brain must compress away patterns or drown in sensory chaos; the cost is metabolic + epistemic. CVH is unskillful only when over-applied.

GF(3) Balanced Triad

compressive-vasomotion (+1) ⊗ vascular-clamp (0) ⊗ latched-hyperprior (−1) = 0 (mod 3)

Skill Trit: +1 (Play / generate — the sweep produces a candidate collapse; cf. match-fires in the PAM RETE).

Concomitant Skills

SkillTritInterface
vascular-clamp0downstream: freezes what the sweep collapsed
latched-hyperprior−1downstream: cements a sustained collapse
vasocomputation+1umbrella / vascular substrate
kolmogorov-compression+1compression progress as a drive (Schmidhuber)
fokker-planck-analyzer+1stationary collapse of a resonance landscape
information-geometry0precision-weighting / KL of clarity vs. grab

Current literature (2024–2026)

  • van Veluw et al. (2020), Neuron — spontaneous ~0.1 Hz vasomotion drives paravascular clearance; amplitude is tunable.
  • Hauglund / Nedergaard et al. (2025), Cell — locus-coeruleus NE infraslow oscillations drive slow vasomotion → glymphatic clearance in NREM; zolpidem suppresses it.
  • Kedarasetti & Drew (2024), Neuron — vasomotion travels as long-wavelength waves; resting modulation exceeds evoked. Recasts the "compression sweep" as a literal traveling wave, not a global clock.
  • Atasoy et al. (2016), Nat Commun — connectome harmonics; with Safron's SOHMs gives the "out-of-tune harmonic → contraction" claim a formal substrate.
  • Pinotsis & Miller (2023), Prog Neurobiol — cytoelectric coupling: endogenous fields sculpt activity (grounds the ephaptic loop; VSMC→field causation still unestablished).
  • Sharpening: specify which ~0.1 Hz band (myogenic vs LC/NE vs Mayer) drives compression — the 2025 literature warns these are conflated.
  • Hook: vasomotion phase should predict bistable-percept switching; optogenetic VSMC constriction should narrow local neural dynamic range / lower LFP entropy.
  • Grounded: vasomotion exists + glymphatic role + ephaptic fields. Speculative: vasomotion as a Bayesian-collapse mechanism.

References

  • Johnson, M.E. (2023). Principles of Vasocomputation, Part I. opentheory.net (§V, CVH).
  • Schmidhuber, J. (2008). Driven by Compression Progress. arXiv.
  • Safron, A. (2020). IWMT. Frontiers in AI 3. (SOHMs as autoencoders/symmetry detectors.)
  • Carhart-Harris & Friston (2019). REBUS and the Anarchic Brain. Pharmacol. Rev. 71(3).

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