From Gamma Synchrony to Informative Difference: A Shannon-Theoretic Model of Tonic Context and Phase-Wave Differentials

Abstract

# Provider Description Gamma synchronization can coordinate distributed neural populations, but coordination alone does not specify what changed in a neural state. This paper develops a Self Aware Networks model in which a learned receiver-specific tonic sequence supplies context, while structured departures produced by excitation, inhibition, altered timing, omissions, bursts, duration changes, spatial phase gradients, or route changes carry candidate information about change. The proposal evaluates those departures with Shannon conditional information and follows them through receiver-specific consequences. The framework distinguishes synchrony, sequence predictability, event surprisal, variability, and biological efficacy. It relates coefficient of variation to one measurable component without equating variability with information, separates structured departures from rare noise, and states clear falsification conditions. A prospective mouse experiment compares the full model with rate, power, phase, phase-rate, prediction-error, and matched multivariate alternatives, followed by a phase-specific perturbation and rescue test. The article reports no new animal or human data.