Neuroscience in Review: Earl K. Miller's MIT Corpus

Title: Neuroscience in Review: Earl K. Miller’s MIT Corpus vs. Micah Blumberg’s Self-Aware Networks (SAN) and Super Information Theory (SIT) Description:This review provides a forensic, concept-by-concept crosswalk between Micah Blumberg’s Self-Aware Networks (SAN) and Super Information Theory (SIT) corpus (2017–2025) and later oscillation-centric findings associated with Earl K. Miller’s group. After translating terms (e.g., “coincidence as a bit,” “phase wave differentials,” “Neural Array Projection Oscillation Tomography (NAPOT),” “volumetric 3D internal screen”) into mainstream neuroscience language, the paper aligns them with traveling-wave dynamics, burst-based working-memory codes, cross-frequency coordination, and large-scale phase-coherence fields reported 2022–2025. Using the Gold-Standard Equivalence Action Plan (GSEAP)—drawing on Gorla’s operational encodability, Baez–Pollard compositional structure, and Wilson–Chiribella higher-order embeddings—the analysis argues these frameworks are operationally and structurally equivalent up to renaming. A Bayesian invariant tally and a Kolmogorov-complexity argument jointly indicate that the observed cluster of seven or more specific conceptual matches is statistically incompatible with independent derivation, supporting priority for the SAN/SIT framework. The work aims to rectify the scholarly record, provide a compact translator between vocabularies, and offer testable predictions for future experiments on phase-structured integration, predictive wave interference, and oscillatory binding across scales. Keywords:neural oscillations; traveling waves; working memory; phase coherence; cross-frequency coupling; predictive coding; binding by synchrony; NAPOT; SAN; Super Information Theory; priority analysis; encodability; category theory Version/Date: v1, October 10, 2025Author: Micah Blumberg (The Self Aware Networks Institute)Related resources: selfawarenetworks (GitHub), prior SAN/SIT essays and reviewsMethod tags: GSEAP; Bayesian invariant analysis; Kolmogorov complexity; operational/categorical equivalenceLicense: Author’s chosen license (specify here)