Alvankar Golpayegan, Hanieh Phase transitions in stochastic models of neural networks. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Phase transitions in stochastic models of neural networks |
| Autori: | Autore Email Alvankar Golpayegan, Hanieh hani89alvn@gmail.com |
| Numero di pagine: | 101 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Neuroscienze e Scienze Riproduttive ed Odontostomatologichei |
| Dottorato: | Neuroscienze |
| Ciclo di dottorato: | 36 |
| Coordinatore del Corso di dottorato: | nome email Taglialatela, Maurizio maurizio.taglialatela@unina.it |
| Tutor: | nome email De Candia, Antonio [non definito] |
| Numero di pagine: | 101 |
| Parole chiave: | Wilson-Cowan model, Criticality, Hopfield model, |
| Settori scientifico-disciplinari del MIUR: | Area 02 - Scienze fisiche > FIS/07 - Fisica applicata (a beni culturali, ambientali, biologia e medicina) |
| Depositato il: | 18 Mar 2024 09:33 |
| Ultima modifica: | 10 Mar 2026 14:08 |
| URI: | http://www.fedoa.unina.it/id/eprint/15715 |
Abstract
In this research, we have examined the stochastic Wilson-Cowan neural network model to pinpoint the conditions that influence network behavior, focusing on criticality and bistability. Recent magnetoencephalography findings underscore the functional importance of bistable criticality in the dynamics of the brain in a healthy, awake, resting state, suggesting its potential role as a pathophysiological factor in epilepsy. By applying the Wilson-Cowan model, we sought to uncover the roots of bistability, attributed to neurons' superlinear activation function. Beyond its clinical implications for conditions like epilepsy, bistability, and multistability serve as crucial characteristics of networks with associative memory capabilities. Consequently, we explored a variant of the Wilson-Cowan model designed to facilitate associative memory, akin to the approach of the Hopfield model.
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