Modeling neural connectivity of caenorhabditis elegans
Name
1015202175-MIT.pdf
Description
Full printable version
Size
1.26 MB
Format
Adobe PDF
Checksum (MD5)
6534b348aafad7992be7e41edb1ae3ed
Author(s)
Fabre, Guadalupe I
Advisor(s)
Steven W. Flavell.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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