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Novel Machine Learning Approaches for Neurophysiological Data Analysis

Kirschbaum, Elke

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Abstract

Detecting repeating firing motifs of neuron groups (so-called neuronal assemblies) and cell segmentation in calcium imaging, a microscopy technique enabling the observation of neuronal activity, are two fundamental and challenging tasks in neurophysiological data analysis. In this thesis, three novel approaches are presented, which use machine learning to tackle both problems from different perspectives. First, SCC is presented for the detection of motifs in neuronal spike matrices, which are gained from calcium imaging data by cell segmentation. SCC uses sparse convolutional coding and outperforms established motif detection methods by leveraging sparsity constraints specifically designed for this data type combined with a method to avoid false-positive detections. Second, LeMoNADe is the first method ever to detect spatio-temporal motifs directly in calcium imaging videos, eliminating the cumbersome extraction of individual cells. It is a variational autoencoder framework tailored for the extraction of neuronal assemblies from videos and matches the performance of state-of-the-art detection methods requiring cell extraction. Although LeMoNADe enables the detection of neuronal assemblies without previous cell extraction, this step is still essential for a wide range of downstream analyses. Therefore, the third method, DISCo, combines a deep learning model with an instance segmentation algorithm to address this task from a new perspective and thereby outperforms similarly trained existing models.

Item Type: Dissertation
Supervisor: Hamprecht, Prof. Dr. rer. nat. Fred A.
Place of Publication: Heidelberg
Date of thesis defense: 5 November 2019
Date Deposited: 09 Dec 2019 13:32
Date: 2019
Faculties / Institutes: The Faculty of Physics and Astronomy > Institute of Physics
Subjects: 004 Data processing Computer science
500 Natural sciences and mathematics
530 Physics
Controlled Keywords: Maschinelles Lernen
Uncontrolled Keywords: neuronal assembly detection, calcium imaging analysis, machine learning, motif detection, cell segmentation
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