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Deep Learning Methods in Neuroscience: From Modeling Molecular Mechanisms to Classifying States of Consciousness — arXiv:2609.35372

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35372
License (as archived): CC0-1.0
Archived: 2026-10-07T21:53:42.203Z
SHA-256 of the archived text: 31e6740cc9e8bc72edaa5fb05b2eff458f02cb9843fbe60b23d6f4bd3e820090
Stable URL of this page: https://metraipolis.com/documento/dc_e85fe2a7fd01

How to cite

Deep Learning Methods in Neuroscience: From Modeling Molecular Mechanisms to Classifying States of Consciousness — arXiv:2609.35372 — arXiv (en), https://arxiv.org/abs/2609.35372 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e85fe2a7fd01 (Metraipolis archive dc_e85fe2a7fd01, sha256 31e6740cc9e8bc72edaa5fb05b2eff458f02cb9843fbe60b23d6f4bd3e820090)

BibTeX

@misc{dce85fe2a7fd01,
  title = {Deep Learning Methods in Neuroscience: From Modeling Molecular Mechanisms to Classifying States of Consciousness — arXiv:2609.35372},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35372},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e85fe2a7fd01; sha256 31e6740cc9e8bc72edaa5fb05b2eff458f02cb9843fbe60b23d6f4bd3e820090},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35372. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A critical analysis of contemporary approaches to the study of conscious states. The review focuses on methods of classification, clustering, modeling of brain states under anesthesia and identification of measurable neurobiological characteristics of brain function. A comparative analysis was conducted in the following three major areas: automatic detection of states of consciousness using neural networks based on EEG and fMRI data; modeling of the structural-functional dynamics of the brain under the effects of anesthetics; and detection of neurophysiological indicators which correlate with the level of consciousness. The obtained conclusions demonstrate the growing effectiveness of deep neural models in the classification and prediction of brain states and the analysis of dynamic structural-functional connectivity. Nonetheless, significant limitations were also identified, including the limited interpretability of the models, the lack of standardized metrics, and the problem of the specificity of consciousness markers. Our findings support the need for developing hybrid, generalizible, physiologically grounded architectures. Furthermore, such approaches may improve the translational potential of computational models in clinical neuroscience. Diverse methods of machine and computational modeling have demonstrated their effectiveness in tasks of automatic clustering and classification of brain states, the development of multilevel models and the identification of connectivity patterns correlated with levels of consciousness. A larger-scale analysis and a larger dataset, as well as the implementation of model interpretability approaches are required for the practical application of the analyzed models. The models based on EEG and LFP are the most promising for clinical application due to their availability and the possibility of real-time monitoring.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Neural and Evolutionary Computing (cs.NE).
Pagina del lavoro: https://arxiv.org/abs/2609.35372 — 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 2026-10-07T21:27:08.682Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).