Geometry of phase space and asymptotic behavior for oscillatory neural networks

Autor:	Anatoly T. Vlassov, George G. Krylov, Halina V. Grushevskaja
Rok vydání:	1998
Předmět:	Physics Quantitative Biology::Neurons and Cognition Artificial neural network Computer simulation Ordinary differential equation Phase space Computer Science::Neural and Evolutionary Computation Complex system Geometry Systems modeling Asymptotic expansion Synchronization
Zdroj:	SPIE Proceedings.
ISSN:	0277-786X
Popis:	The asymptotic expansions of the solutions within the framework of been found phase-space geometry for oscillatory neural model are constructed. The oscillatory neural networks consist of non-identical neurons are examined. The phenomenon of mutual neurons' synchronization has been analyzed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::713eeb7dbd52d507b173afeb7c5fdfe5 https://doi.org/10.1117/12.304824 Zobrazit plný text záznamu