claim

active

claim:vsmcs-are-the-brain-s-compression-prediction-infrastructure-where-top-down-predictive-models-are-physically-stored

VSMCs are the brain's compression/prediction infrastructure — where top-down predictive models are physically stored.

Central thesis linking VSMCs to predictive coding.

Source paper

extracted_from

Johnson Vasocomputation 2023

Neighborhood — ranked by edge-count

Hypotheses (3)

hypothesis

Compressive Vasomotion Hypothesis (CVH): Vasomotion reflex functions as a compression sweep on nearby neural resonances, collapsing ambivalent patterns into durable definite states, with motifs of vasomotion as reflexive reactions to uncertainties (patterns of tanha).
supports
First of the three core vasocomputation hypotheses, linking vasomotion to compression.
Vascular Clamp Hypothesis (VCH): Vascular contractions freeze local neural patterns and plasticity for the duration of contraction, with specific constrictions encoding specific predictions, functioning as medium-term memory.
supports
Second core hypothesis, linking VSMC contraction to active inference predictions and memory.
Latched Hyperprior Hypothesis (LHH): If a vascular contraction is held long enough, it engages the latch-bridge mechanism, durably freezing the nearby circuit and creating a durable commitment to a specific hyperprior isolated from global updating, unlocking only when the corresponding prediction is resolved.
supports
Third core hypothesis, explaining how latched VSMCs instantiate hyperpriors.

Communities (2)

community

Chain-of-Thought reasoning robustness & safety
members_of
CoT effects on generalization, multimodal QA accuracy, and AI safety alignment training.
Sensory integration in predictive cognition
members_of
Framework viewing perception as active inference mechanism that reduces hallucination through multimodal feature integration and predictive model compression.

Artifacts (1)

artifact

Principles of Vasocomputation: A Unification of Buddhist Phenomenology, Active Inference, and Physical Reflex (Part I)
cites
Blog post/research essay that introduces the Vasocomputation framework, linking Buddhist tanha with vascular smooth muscle cell function, active inference, and physical reflexes.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Smooth muscle cell motifs are where the brain's top-down predictive models are hiding.claim0.765
Key claim for the FEP-AI community about the physical location of predictive models.
Performance-optimized hierarchical models predict neural responses in higher visual cortex (Yamins et al., 2014)concept0.753
Demonstrated CNN representations predict neurons in visual cortex; background motivation for neural-network-brain correspondence.
Vascular Smooth Muscle Cells (VSMCs)concept0.747
Proposed as the brain's compression/prediction infrastructure where tanha physically manifests; central mechanism in vasocomputation theory.
The spatial memory pipeline: A model of egocentric to allocentric understanding in mammalian brains (Uria et al., 2020)concept0.739
Alternative neuroscience model analogous to TEM; the mathematical relationship to transformers also holds for this model.
Olah et al. (2020) found that automatically trained computer vision models, regardless of architecture and training procedure, all arrive at similar functional structures organizing similar features into similar compositional hierarchies, closely resembling the primate visual cortex.finding0.737
Empirical finding supporting the Universality Hypothesis; extended by the paper to consciousness
Migraines and cluster headaches are inappropriate VSMC latches.claim0.736
Medical interpretation of certain headaches as latch dysfunction.
The ability to represent relationships (e.g. correlations or associations) among variables, rather than a system of independent variables, is essential for any connectionist model of cognition.claim0.734
Necessary condition for connectionist cognition.
The huge chemical complexity within each synapse suggests that neural models of cognition that refer only to changing weights of synaptic connections and ignore sub-neural chemistry are probably ignoring some of the most important explanatory mechanisms in brains.claim0.733
Sloman's critique of mainstream neural network theories.