From Cellular Difference to Population Reinstatement: Oscillatory Scaling, Inhibitory Waves, and Neural Projection in Self Aware Networks

This paper asks how a relation stored in local cellular and synaptic organization can become recoverable across a larger neural population without requiring a literal copy at every stage. It proposes transformed relation preservation: a compatible input engages a learned cellular transform, produces a typed local difference, and changes connected receivers whose anatomy, timing, gain, inhibition, recurrence, and local fields re-express the relation across successive arrays. The paper distinguishes synaptic, axonal, interneuronal, refractory, neuromodulatory, and ephaptic routes; treats delayed, suppressed, or omitted events relative to a declared expectation; and reserves the term soliton for patterns that pass demanding propagation and perturbation tests. A deterministic example follows a signed relation through two receiver-dependent transforms and compares it with rate-only, topology-shuffled, and negative-channel-ablated controls. The values are proof-of-concept results from constructed data, not biological estimates. The paper provides a staged experimental and falsification program for testing the proposed scale bridge.