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Steven W. Flavell. Fabre, Guadalupe I Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2017-12-20T17:24:57Z 2017-12-20T17:24:57Z 2017 2017 http://hdl.handle.net/1721.1/112842 1015202175 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 39-40). Little research has been done regarding the use of modeling techniques to estimate the connectivity of a nervous system by analyzing data recorded from its neurons. In this thesis, three main methods were implemented to solve this task: Bayesian inference, artificial neural networks, and ODE reverse engineering. The goal is to apply these methods to data recorded from C. elegans neurons and estimate the connections between these. The top two performing methods on simulated data were feed-forward neural networks and ODE reverse engineering. The worst performing methods were recurrent neural networks and Bayesian inference. Out of the methods tried, feed-forward neural network was the most robust to changes in parameters of the simulation network and noise. Furthermore, this technique is easily generalizable since it did not rely on any particular feature of the simulation network to achieve its good performance. Nevertheless, all methods are easily reproducible for any further research. by Guadalupe I. Fabre. M.Eng. 41 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Modeling neural connectivity of caenorhabditis elegans Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="97b07750-abcd-4693-8920-0d604270795f"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Modeling neural connectivity of caenorhabditis elegans</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Fabre, Guadalupe I</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Little research has been done regarding the use of modeling techniques to estimate the connectivity of a nervous system by analyzing data recorded from its neurons. In this thesis, three main methods were implemented to solve this task: Bayesian inference, artificial neural networks, and ODE reverse engineering. The goal is to apply these methods to data recorded from C. elegans neurons and estimate the connections between these. The top two performing methods on simulated data were feed-forward neural networks and ODE reverse engineering. The worst performing methods were recurrent neural networks and Bayesian inference. Out of the methods tried, feed-forward neural network was the most robust to changes in parameters of the simulation network and noise. Furthermore, this technique is easily generalizable since it did not rely on any particular feature of the simulation network to achieve its good performance. Nevertheless, all methods are easily reproducible for any further research.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>