Physical Processes in Biological Neuronal Networks During Epileptic Seizures: An Overview of Models
DOI:
https://doi.org/10.20535/ibb.2026.10.2.349634Keywords:
electroencephalography, ictal transition, synthetic calibration data, phenomenological dynamics, Epileptor‑2, bifurcation mechanisms, multiscale connectome, feature extraction, classifier calibration, transfer validationAbstract
Abstract: This review summarizes approaches to epileptic seizure detection and forecasting from electroencephalo-graphic and intracranial electroencephalographic signals, with emphasis on physically grounded modelling of neurons and neuronal networks. The topic is relevant because modern machine-learning and deep-learning methods often achieve high classification performance, but their practical use is limited by cross-patient variability, scarcity of la-belled data, dependence on preprocessing, false-alarm control and insufficient interpretability of informative features. The paper systematizes the main model classes used in computational epileptology: low-dimensional phenomenologi-cal models, biophysical models of single neurons and membrane processes, network and multiscale frameworks, ma-chine-learning approaches, and high-order numerical schemes for large-scale simulations. It is shown that biophysical and multiscale models provide deeper insight into ionic, synaptic and network mechanisms of epileptiform activity, but require many parameters and substantial computational resources. In contrast, phenomenological models, espe-cially Epileptor and Epileptor-2, offer a rational compromise between simulation speed, parameter controllability and the ability to reproduce transitions between normal, ictal and post-ictal regimes. We argue that such models should not be considered a replacement for real EEG/iEEG recordings, but rather a controlled source of synthetic signals for fea-ture calibration, classifier robustness testing and analysis of the physical origin of informative signal characteristics. Model-generated signals are therefore positioned as an intermediate layer between experimental recordings and ma-chine-learning algorithms. This approach may improve the reproducibility of studies, increase the interpretability of diagnostic features and provide a basis for further development of compact systems for epileptic seizure detection and forecasting.
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