Tracking a rat in an open field experiment with a deep learning-based model
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2021Copyright
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New artificial neural network methods have changed the way animals are tracked in neuroscience and psychology experiments. The purpose of this thesis is to test the state-of-the-art method of animal tracking DeepLabCut and to develop a usable model for tracking rats in an open field type experiment, and to use tracking information to extract researchrelated key figures via self-tailored analysis software. The model trained with the DeepLabCut and 825 labeled images was accurate and suitable to be used in a research experiment. With the help of tracked body parts, it was possible to extract meaningful key figures for further analysis in a research experiment. Uudet keinotekoisiin hermoverkkoihin perustuvat menetelmät ovat muuttaneet sitä, miten eläimiä seurataan neurotieteen ja psykologian koeasetelmissa. Tämän tutkimuksen tarkoituksena on ollut testata neuroverkkoihin perustuvaa DeepLabCutmenetelmää rottien seurantaan avoimen kentän testin tyyppisessä koeasetelmassa ja tuottaa seurantadatasta tutkimusten kannalta merkityksellisiä avainlukuja itse kehitetyn analyysiohjelman avulla. DeepLabCutilla ja 825 kuvalla opetettu malli oli tarkka ja soveltuva tutkimuskäyttöön. Seurattujen eläimen kehopisteiden avulla pystyttiin tuottamaan tunnuslukuja tutkimusten analyysejä varten.
Keywords
animal tracking artificial neural networks convolutional neural networks DeepLabCut machine vision open field test jyrsijät rotta (laji) neurotieteet syväoppiminen koneoppiminen neuroverkot tekoäly ohjelmistokehitys bonsai videokamerat ruhot oppiminen rodents Rattus norvegicus neurosciences deep learning machine learning neural networks (information technology) artificial intelligence software development video cameras animal bodies learning
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- Pro gradu -tutkielmat [29344]
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