Metraipolis › archive › dc_ee82ccbbe583

A Spiking Neural Network Model of Elementary Self-Consciousness via Endogenous Default Mode Network Dynamics — arXiv:2609.29984

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.29984
License (as archived): CC0-1.0
Archived: 2026-10-08T07:23:46.399Z
SHA-256 of the archived text: c5430957ccbf79d55cfd76b2781a6f0dfa8ecc3aec8f4112330bcb4cecf3e96a
Stable URL of this page: https://metraipolis.com/documento/dc_ee82ccbbe583

How to cite

A Spiking Neural Network Model of Elementary Self-Consciousness via Endogenous Default Mode Network Dynamics — arXiv:2609.29984 — arXiv (en), https://arxiv.org/abs/2609.29984 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ee82ccbbe583 (Metraipolis archive dc_ee82ccbbe583, sha256 c5430957ccbf79d55cfd76b2781a6f0dfa8ecc3aec8f4112330bcb4cecf3e96a)

BibTeX

@misc{dcee82ccbbe583,
  title = {A Spiking Neural Network Model of Elementary Self-Consciousness via Endogenous Default Mode Network Dynamics — arXiv:2609.29984},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.29984},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ee82ccbbe583; sha256 c5430957ccbf79d55cfd76b2781a6f0dfa8ecc3aec8f4112330bcb4cecf3e96a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.29984. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Understanding the neurobiological mechanisms underlying self-referential cognition and baseline self-consciousness remains a fundamental challenge in computational neuroscience. In this work, we propose a large-scale computational model incorporating a 10,000-neuron spiking neural network (SNN) based on Izhikevich dynamics. The network is structured into two interacting subsystems: a sensory processing layer (5,000 regular-spiking cortical neurons) and an endogenous Default Mode Network (DMN) pacemaker subsystem (5,000 intrinsically bursting neurons). The DMN layer is modulated by continuous tonic currents reflecting ascending brainstem neuromodulation, maintaining intrinsic, autonomous bioelectric rhythms independent of external sensory input. To represent top-down cognitive modulation, synaptic weights are hierarchically structured such that DMN-to-network projections exceed sensory-level connections. Through numerical simulations using a modified two-step Euler integration scheme, we demonstrate how endogenous pacemaker activity interacts with transient external sensory perturbations, providing an elementary mathematical framework for the emergence of a persistent, self-sustaining neural representation of "Self".

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-25 in cs.NE).
Categorie arXiv: Neurons and Cognition (q-bio.NC) ; Neural and Evolutionary Computing (cs.NE).
Pagina del lavoro: https://arxiv.org/abs/2609.29984 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T06:43:56.395Z dalla pagina https://arxiv.org/catchup/cs.NE/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).