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Identifier 000417104
Title Analysis and modeling of spontaneous neural activity using dictionary learning systems
Alternative Title Aνάλυση και μοντελοποίηση της αυθόρμητης νευρωνικής δραστηριότητας με χρήση συστημάτων εκμάθησης λεξικών
Author Τρουλλινού, Ειρήνη Ι.
Thesis advisor Τσακαλίδης, Παναγιώτης
Reviewer Μούχταρης, Αθανάσιος
Παπαδοπούλη, Μαρία
Abstract A central tenet of neuroscience is the remarkable computational abilities of our brains that arise as a result of populations of interconnected neurons. Indeed, we find ourselves at an exciting moment in the history of neuroscience, as the field is experiencing rapid growth in the complexity and quantity of the recorded neural activity, as advances in experimental design, measurement techniques, and computational analysis allow us unprecedented access to the dynamics of neural activity in different brain areas. Thus, one of the goals of neuroscience is to find interpretable descriptions of what the brain represents and computes, and also to explain complex phenomena in simple terms. Considering this task from the perspective of dimensionality reduction provides an entry point into principled mathematical techniques that allows us to discover these representations directly from experimental data, a key step to developing rich yet comprehensible models for brain function. Dimensionality reduction methods produce lowdimensional representations of high-dimensional data, where the representation is chosen to preserve or highlight some feature of interest in the data. In this master thesis, we employ two real binary datasets that refer to the spontaneous neuronal activity of two laboratory mice over time, and we aim to their efficient low-dimensional representation. Real datasets compared to synthetic ones are not so highly structured and background noise is more intense. Noise could be a result of mistakes during the creation or the processing of the datasets or it could also exist due to accidental firings produced by neurons. So, in order to get insights regarding how neurons are connected to each other, we also need to be able to discriminate the true from the noisy activation patterns. In order to address both challenges, namely the low-dimensional representation of the data and the discrimination between true and spurious activation patterns, we combine dimensionality reduction techniques with supervised machine learning. More specifically, we propose a Sequential Adversarial Dictionary Learning Algorithm, which selects sequentially the elements that are included in the dataset and fills the dictionary, namely the new reduced space, only with those elements that contribute to the better representation of the true, rather than the noisy activation patterns, which have been synthetically created. The entry of an element in the dictionary, which is based on the use of true and noisy activation patterns, justifies the name "Adversarial", which is used in the title of our algorithm. This method searches only for repeated patterns with total synchronous firing activity. Thus, we also consider the idea of a more relaxed approach, where we can discover patterns that have also some temporal correlation within a time window interval. Subsequently, a supervised classifier is used, which takes as input the reconstructed signals in order to discriminate the true from the noisy activation patterns. Experimental results show that our algorithm creates a dictionary which, when used to produce the reconstructed patterns given to the classifier, it results to a classification accuracy of 60% in the case of synchronous firing activity and 90% when we search for patterns in bigger time window intervals. By comparison, the classifier achieves a classification rate of only 51% when raw data is used as input. We also demonstrate that our system achieves better results both quantitatively as well as qualitatively when compared with K-SVD, an established dictionary learning algorithm.
Language English
Subject Sparse coding
Αραιή κωδικοποίηση
Issue date 2018-07-20
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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