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Extraction of recurring behavioral motifs from video recordings of natural behavior
KTH, School of Engineering Sciences (SCI).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Extraktion av återkommande beteendemönster från videoinspelningar av naturligt beteende (Swedish)
Abstract [en]

Complex neural activity exhibits itself in various forms, one of which is behavior. Hence a natural way to study neural activity is to analyze behavior. In this thesis, behavior has been studied using a Gaussian hidden Markov model. The data has been gathered from video recordings of free roaming mice in a box. The model has trained on and classified mouse behavior. Classification with 4 and 6 states have been tried, the one with 6 states seems to make a distinction between two different stationary states which is biologically interesting. The conclusion is that the Gaussian hidden Markov model is a reasonable approach to mice behavior classification but it does not solve any fundamental problems. There are also some data gathering techniques that affect the results which need to be improved.

Abstract [sv]

Komplex neural aktivitet utrycks i en mängd olika former, en av dessa är beteende. Det gör att ett naturligt sått att studera neural aktivitet är att analysera beteende. I den här uppsatsen så har beteende blivit studerat genom en dold Markov modell. Data har tagits från filmer av fritt springande möss i en låda. Modellen har framgångsrikt tränats på- och klassificerat mössbeteende. Klassificering med 4 och 6 tillstånd har testats, med 6 tillstånd verkar modellen göra en distinktion mellan två olika stationära tillstånd vilket är biologiskt intressant. Sammanfattningsvis är en gaussisk dold Markov modell ett rimligt sått att klassificera mössbeteende men det löser inga fundamentala problem. Det är också en del datainsamlingstekniker som skapat felaktigheter vilket behöver förbättras.

Place, publisher, year, edition, pages
2018. , p. 24
Series
TRITA-SCI-GRU ; 2018-087
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-230894OAI: oai:DiVA.org:kth-230894DiVA, id: diva2:1220074
Supervisors
Examiners
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18Bibliographically approved

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