Computational Phase Synchronization

Multiscale aperture synthesis imaging (MASI) provides a concrete engineering example of computational phase synchronization: independently operating coded sensors preserve wavefield information, computational procedures estimate relative phase offsets, and coherent synthetic-aperture reconstruction is recovered after capture. This paper uses MASI as an engineering source, not as biological validation. The central pattern is that synchrony can move from a fragile hardware precondition into a computational alignment process when local measurements preserve enough phase-relevant information. The paper translates that pattern into two bounded research directions. In the biological direction, SAN/NAPOT terms such as phase wave differentials, coincidence bits, inhibitory gating, oscillatory integration, and tomographic projection are treated as candidate mappings, not established neuroscience. In the AI direction, the paper proposes artificial phase-synchronization networks: modules emit complex-valued or cyclic state variables, an offset estimator aligns them, and a coherence objective tests whether explicit phase-like alignment improves drift-resistant fusion. The result is not a claim about consciousness. It is an engineering-to-architecture paper: MASI supplies the verified optical mechanism, SAN/NAPOT supplies a bounded biological hypothesis, and AI experiments decide whether phase-synchronization layers outperform compute-matched baselines.