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Optimizing Neural Source Extraction Algorithms: A Performance Measure  Based on Neuronal Network Properties
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, NA.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Optimering av extraktionsalgoritmer för neuronala datakällor: Ett prestandamått baserat på neuronala nätverksegenskaper (English)
Abstract [en]

Extracting neural activity from electrophysiological and calcium All existing automated algorithms for this purpose, however, rely heavily on manual intervention and parameter tuning.

In this thesis, we introduce a novel performance measure based on well-founded notions of neuronal network organization. This enables us to systematically tune parameters, using techniques from statistical design of experiments and response surface methods. We implement this framework on an algorithm used to extract neural activity from microendoscopic calcium imaging datasets, and demonstrate that this greatly reduces manual intervention.

Abstract [sv]

Extraktion av neuronal aktivitet från elektrofysiologiska och kalciumavbildningsmätningar utgör ett viktigt problem inom neurovetenskapen. Alla existerande automatiska algoritmer för detta ändamål beror dock i dagsläget på manuell handpåläggning och parameterinställning.

I detta examensarbete presenterar vi ett nytt prestandamått baserat på välgrundade begrepp rörande organisationen av neuronala nätverk. Detta möjliggör en systematisk parameterinställning genom att använda tekniker från statistisk experimentdesign och response surface-metoder. Vi har implementerat detta ramverk för en algoritm som används för att extrahera neuronal aktivitet från mikroendoskopisk kalciumavbildningsdata och visar att detta förfarande avsevärt minskar behovet av manuell inblandning.

Place, publisher, year, edition, pages
2017.
Series
TRITA-MAT-E ; 2017:50
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-210052OAI: oai:DiVA.org:kth-210052DiVA, id: diva2:1118255
External cooperation
NTNU Kavli Institute for Systems Neuroscience, Trondheim, Norway
Subject / course
Scientific Computing
Educational program
Master of Science - Mathematics
Supervisors
Examiners
Available from: 2017-06-30 Created: 2017-06-30 Last updated: 2017-06-30Bibliographically approved

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Numerical Analysis, NA
Computational Mathematics

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