Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/144948
Title: Gesture tracking and neural activity segmentation in head-fixed behaving mice by deep learning methods
Author: Abbas, Waseem
Director: Masip Rodó, David
Keywords: neuroscience
neural activity
behavioral data
3-dimensional convolutional neural network (3D-CNN)
long-term and short-term memory network (LSTM)
Issue Date: 30-Oct-2020
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The typical approach used by neuroscientists is to study the response of laboratory animals to a stimulus while recording their neural activity at the same time. With the advent of calcium imaging technology, researchers can now study neural activity at sub-cellular resolutions in vivo. Similarly, recording the behaviour of laboratory animals is also becoming more affordable. Although it is now easier to record behavioural and neural data, this data comes with its own set of challenges. The biggest challenge, given the sheer volume of the data, is annotation. A traditional approach is to annotate the data manually, frame by frame. With behavioural data, manual annotation is done by looking at each frame and tracing the animals; with neural data, this is carried out by a trained neuroscientist. In this research, we propose automated tools based on deep learning that can aid in the processing of behavioural and neural data. These tools will help neuroscientists annotate and analyse the data they acquire in an automated and reliable way.
Language: English
URI: http://hdl.handle.net/10609/144948
Appears in Collections:Tesis doctorals

Files in This Item:
File Description SizeFormat 
W.Abbas_PhD_Thesis_Manuscript.pdfAbbas_dissertation15,4 MBAdobe PDFThumbnail
View/Open