Outer-synchronization criterions for asymmetric recurrent time-varying neural networks described by differential-algebraic system via data-sampling principles

Asymmetric recurrent time-varying neural networks (ARTNNs) can enable realistic brain-like models to help scholars explore the mechanisms of the human brain and thus realize the applications of artificial intelligence, whose dynamical behaviors such as synchronization has attracted extensive researc...

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Detalles Bibliográficos
Autores principales: Li, Ping, Liu, Qing, Liu, Zhibing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714567/
https://www.ncbi.nlm.nih.gov/pubmed/36465965
http://dx.doi.org/10.3389/fncom.2022.1029235

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714567/
https://www.ncbi.nlm.nih.gov/pubmed/36465965
http://dx.doi.org/10.3389/fncom.2022.1029235

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