Unified Proto Self-Object: A Spiking Neural Architecture for Minimal Self-Representation
Andrejs Bistricenko
Zenodo (CERN European Organization for Nuclear Research) February 22, 2026 preprint DOI: 10.5281/zenodo.18731842 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Key points | Proposes a spiking neural architecture in which the self is modeled as a dynamically reconstructed internal object rather than a persistent subject or control center. Argues that reflexive dynamics emerge when this self-object becomes an object of observation, yielding recursive organization without an explicit subject-level entity. |
Abstract
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.