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Neural response of a Neuron population: A mathematical modelling approach
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
2021 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Matematisk modellering av neuronresponser i en population av neuroner (Swedish)
Abstract [en]

The brain – the organ that allows us to be aware of our surroundings – consists of a complex network of neurons, which seemingly allows the human brain to be able of abstract thinking, emotions, and cognitive function. To learn how the brain is capable of this, the two main branches of neuroscience study either neurons in detail, or how they communicate within neuronal networks. Both these branches often tackle the complexity using a combination of experiments and mathematical modelling. A third and less studied aspect of neuroscience concerns the neurovascular coupling (NVC), for which my research group has previously developed mathematical models. However, these NVC models have still not integrated valuable data from rodents and primates, and the NVC models are also not connected to existing neuronal network models. In this project, I address both of these two shortcomings. First, an existing model for the NVC was connected with a simple model for neuronal networks, establishing a connection between the NVC models and the software NEURON. Second, we established a way to preserved information from NVC data from rodents and mice into NVC models humans. This work thus connects the previously developed NVC model both with data from other species and with other types of models. This brings us one step closer to a more holistic and interconnected understanding of the brain and its many intriguing cognitive and physiological functions.

Place, publisher, year, edition, pages
2021. , p. 46
Keywords [en]
Neurovascular coupling, The brain, Mathematical modelling, Systems Biology, Neural communication
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:liu:diva-177797ISRN: LIU-IMT-TFK-A—21/591--SEOAI: oai:DiVA.org:liu-177797DiVA, id: diva2:1577565
Subject / course
Biotechnology
Presentation
2021-06-10, 16:22 (English)
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Examiners
Available from: 2021-09-01 Created: 2021-07-02 Last updated: 2021-09-01Bibliographically approved

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fulltext(2082 kB)177 downloads
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Division of Biomedical Engineering
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CiteExportLink to record
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  • apa
  • ieee
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