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2 2020

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Modelling effects of consciousness disorders in brainstem computational model - Preliminary findings

Duch Włodzisław, Mikołajewski Dariusz

Bio-Algorithms and Med-Systems

2020

Vol. 16, no. 2

art. no. 20200018

Objectives: Disorders of consciousness are very big medical and social problem. Their variability, problems in precise definition and proper diagnosis make difficult assessing their causes and effectiveness of the therapy. In the paper we present our point of view to a problem of consciousness and its most common disorders. Methods: For this moment scientists do not know exactly, if these disorders can be a result of simple but general mechanism, or a complex set of mechanisms, both on neural, molecular or system level. Presented in the paper simulations using neural network models, including biologically relevant consciousness’ modelling, help assess influence of specified causes. Results: Nonmotoric brain activity can play important role within diagnostic process as a supplementary method for motor capabilities. Simple brain sensory (e.g. visual) processing of both healthy subject and people with consciousness disorders help checking hypotheses in the area of consciousness’ disorders’ mechanisms, including associations between consciousness and its neural correlates. Conclusions: The results are promising. Project announced herein will be developed and its next result will be presented in subsequent articles.

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

Duch Włodzisław, Mikołajewski Dariusz

Bio-Algorithms and Med-Systems

2020

Vol. 16, no. 1

art. no. 20190059

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.