Unified Proto Self-Object: A Spiking Neural Architecture for Minimal Self-Representation
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2026
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| _version_ | 1866901558121201664 |
|---|---|
| author | Bistricenko, Andrejs |
| author_facet | Bistricenko, Andrejs |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18731842 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Unified Proto Self-Object: A Spiking Neural Architecture for Minimal Self-Representation Bistricenko, Andrejs Neural Networks, Computer <p>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.</p> |
| title | Unified Proto Self-Object: A Spiking Neural Architecture for Minimal Self-Representation |
| topic | Neural Networks, Computer |
| url | https://doi.org/10.5281/zenodo.18731842 |