neural-potentiation

name	neural-potentiation
description	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.
license	MIT
metadata	{"trit":-1,"source":"https://doi.org/10.31234/osf.io/uxmz6"}

neural-potentiation

The synaptic substrate (−1 / coplay) of the three-substrate vasocomputation stack. Long-term potentiation/depression (LTP/LTD) writes priors into synaptic weights — the learning landscape of Deep CANALs (weights = learning landscape; SOHMs + vascular tension = inference landscape). This is the consolidated commit: a neuron prior is a vasocomputational latch that has been annealed to disk.

Use When

• Modeling consolidation of a held prediction (inference landscape → learning landscape)
• "Neuron priors" — long-timescale structural memory in synaptic weights
• The write-path: vascular tension (write-buffer) → neural annealing/remodeling → potentiation (commit-to-disk)
• Distinguishing the fast, reversible held prior (vascular) from the slow, structural one (synaptic)

Core Concepts

• Commit-to-disk: Johnson's own clause — vascular tension is "rendered superfluous by neural remodeling: hold a pattern in place long enough and it becomes the default." A neuron prior is a settled latch.
• Two landscapes (Deep CANALs): the learning landscape (weights) changes slowly and structurally; the inference landscape (SOHMs + vascular tension) holds the active hypothesis. Potentiation moves content from the second into the first.
• Annealing: neural annealing consolidates a held prediction into weights — the −1 validation that the prior is worth keeping.
• Immune gate: cytokines (IL-1β, TNF-α) and microglial pruning (neuroimmune-pruning) gate and prune what consolidates — precision-weighting on the commit.
• Maladaptive commit: trauma/PTSD = a latch annealed into a permanent prior that contradicts new data (a nogood-H¹ that won't repair).

GF(3) Balanced Triad

vasocomputation (+1) ⊗ neuroimmune-pruning (0) ⊗ neural-potentiation (−1) = 0 (mod 3)

Skill Trit: −1 (Coplay / commit — the consolidated long-term store; cf. TMS recompute writing a validated belief).

Honesty markers

Grounded: LTP/LTD as synaptic weight change; consolidation/annealing; cytokine modulation of plasticity; Deep CANALs learning-vs-inference landscape distinction. Structural correspondence: the vascular-latch → synaptic-prior write-path is Johnson's stated program direction plus this synthesis, not a measured mechanism.

Concomitant Skills

Skill	Trit	Interface
vasocomputation	+1	inference-landscape source of held priors
neuroimmune-pruning	0	gates/prunes what consolidates
latched-hyperprior	−1	the latch this commits to weights
waddington-landscape	0	canalization = priors becoming default
information-geometry	0	Fisher metric on the learning landscape
koho-sheafnn	0	sheaf-structured neural priors

Current literature (2024–2026)

• Juliani, Safron & Kanai (2024), Neuroscience of Consciousness niae005 — Deep CANALs splits canalization into Type A (inference landscape, activity attractors) vs Type B (learning landscape, slow weight updates θₜ). The skill's "priors in weights" = Type B; the held vascular latch = a sustained Type-A occupancy; the write-path is A→B.
• Herring & Nicoll (2016) — the molecular write-head: NMDA-Ca²⁺ → CaMKII (necessary + sufficient) → GluA1/AMPAR insertion.
• Redondo & Morris (2011), synaptic tagging & capture — induction sets only a tag (potential); persistence needs PRP capture = the commit-vs-potential distinction (tag = dirty page, capture = fsync).
• Josselyn & Frankland (2018); Tonegawa et al. (2018) — engram allocation by CREB/excitability precedes writing; silent→active engram maturation = systems consolidation.
• Nader & Hardt (reconsolidation) — retrieval destabilizes before a labile window allows rewriting: editing a committed prior is two gates (destabilize, then restabilize), not one.
• BCM metaplasticity — the sliding LTP/LTD threshold = the learning-rate / prior-on-plasticity (precision on the learning timescale).
• Hook / falsifier: latch dwell-time below the protein-synthesis window → no Type-B commit (stays labile/lost); reconsolidation window ~10 min–6 h; canalization depth ↔ rumination/OCD/SUD rigidity & psychedelic response.
• Grounded: LTP/CaMKII/AMPAR, STC, engram allocation, reconsolidation, metaplasticity. Speculative: neural annealing, and the vascular-latch → AMPAR weld (the skill's own refutable hypothesis).

References

• Juliani, A., Safron, A., Kanai, R. (2023). Deep CANALs: A Deep Learning Approach to Refining the Canalization Theory of Psychopathology. doi:10.31234/osf.io/uxmz6.
• Johnson, M.E. (2023). Principles of Vasocomputation, Part I. opentheory.net (consolidation / remodeling routes).
• Yirmiya, R. & Goshen, I. (2011). Immune modulation of learning, memory, neural plasticity. Brain Behav. Immun. 25(2).
• Johnson, M. (2019). Neural Annealing: Toward a Neural Theory of Everything. opentheory.net.