Brain stem - from general view to computational model based on switchboard rules of operation

• Autorzy: Duch Włodzisław , Mikołajewski Dariusz

Identyfikatory

DOI

10.1515/bams-2019-0059

• Języki publikacji: EN

Abstrakty

EN

Despite great progress in understanding the functions and structures of the central nervous system (CNS) the brain stem remains one of the least understood systems. We know that the brain stem acts as a decision station preparing the organism to act in a specific way, but such functions are rather difficult to model with sufficient precision to replicate experimental data due to the scarcity of data and complexity of large-scale simulations of brain stem structures. The approach proposed in this article retains some ideas of previous models, and provides more precise computational realization that enables qualitative interpretation of the functions played by different network states. Simulations are aimed primarily at the investigation of general switching mechanisms which may be executed in brain stem neural networks, as far as studying how the aforementioned mechanisms depend on basic neural network features: basic ionic channels, accommodation, and the influence of noise.

Słowa kluczowe

EN

activation; brain stem; computational model

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2020
• Tom: Vol. 16, no. 1
• Strony: art. no. 20190059
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Duch Włodzisław

• Department of Informatics, Nicolaus Copernicus University, ul. Grudziądzka 5, Toruń 87-100, Poland

autor

Mikołajewski Dariusz

• Neurocognitive Laboratory, Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, ul. Wileńska 4, Toruń 87-100, Poland
• Institute of Informatics, Kazimierz Wielki University, ul. Kopernika 1, Bydgoszcz 85-074, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).