Deep Learning Methods in Neuroscience: From Modeling Molecular Mechanisms to Classifying States of Consciousness
Бендерская Елена Николаевна, Anastasiia Alifanova, Svetlana Batalova, Vasilisa Zhuk, Анна Ожиганова
arXiv (Cornell University) September 28, 2026 preprint DOI: 10.48550/arxiv.2609.35372 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Review |
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| Key findings | Argues that machine and computational modeling methods are increasingly effective for clustering and classifying brain states, building multilevel models, and identifying connectivity patterns correlated with consciousness levels, but that limited interpretability, missing standardized metrics, and uncertain marker specificity constrain practical use. The authors propose hybrid, physiologically grounded architectures and larger datasets, and contend EEG- and LFP-based models are most promising clinically. |
Abstract
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.