Unified Proto Self-Object: A Spiking Neural Architecture for Minimal Self-Representation

We present a generative, recursively structured spiking neural architecture that implements a self-representation as an internal object rather than as a persistent subject or control center. The model distinguishes between observer mechanisms, responsible for integrating sensory and internally generated signals, and a self-object that is dynamically reconstructed from the system’s current configuration. Reflexive dynamics arise when this self-object itself becomes an object of observation, yielding a recursive organization without introducing an explicit subject-level entity.