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arXiv Machine Learning By Daniel Szelogowski

DyssomniaNN: Multi-Scale Spiking Neural Modeling of Human Sleep Disorders and Perceived Restedness

Read the original on arXiv Machine Learning →

DyssomniaNN is a multi‑scale computational model that integrates Borbely’s two‑process sleep regulation, Saper’s hypothalamic flip‑flop circuit, and a corticothalamic spiking neural network to simulate human sleep disorders. The model reproduces empirical EEG spectra across five clinical cohorts—healthy control, delayed‑phase circadian rhythm disorder, chronic insomnia, paradoxical insomnia, and narcolepsy type 1—within a 72‑hour photoperiod. Introducing the Well‑Rested Index, the simulations show that total sleep time does not reliably predict restorative recovery, as exemplified by paradoxical insomnia’s normal sleep duration but low restoration and narcolepsy’s severe nocturnal fragmentation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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