2026-09-19T06:30:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1291312026-06-06T00:56:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Una-May O'Reilly and Erik Hemberg.
Bhavaraju, Srilaya.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-01-06T17:41:49Z
2021-01-06T17:41:49Z
2020
2020
https://hdl.handle.net/1721.1/129131
1227274480
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 75-77).
Analyzing neuronal activity in developing neuronal networks can improve our understanding of neuronal dysfunctions underlying conditions such as Rett syndrome. Two-photon calcium imaging is used to capture neuronal network activity over time. This method produces large sets of images that are typically manually analyzed by skilled neuroscientists. Because this process is both time-consuming and subject to error, discovery of therapies that ameliorate network dysfunction may be slowed. We improve an existing, semi-autonomous machine learning pipeline for two-photon calcium imaging sequence analysis. We introduce to the pipeline neuron detection methods using supervised learning models, heuristic filtering of pixels for signal extraction, and event detection using deconvolution. With these methods, we improve neuron detection performance, alter signal-to-noise ratio of extracted calcium signals, and allow for integration of methods that infer action potential firing underlying these signals.
by Srilaya Bhavaraju.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
77 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Using machine learning for analysis of neuronal network activity
Thesis
application/pdf
2021-01-06T17:41:49Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Analyzing neuronal activity in developing neuronal networks can improve our understanding of neuronal dysfunctions underlying conditions such as Rett syndrome. Two-photon calcium imaging is used to capture neuronal network activity over time. This method produces large sets of images that are typically manually analyzed by skilled neuroscientists. Because this process is both time-consuming and subject to error, discovery of therapies that ameliorate network dysfunction may be slowed. We improve an existing, semi-autonomous machine learning pipeline for two-photon calcium imaging sequence analysis. We introduce to the pipeline neuron detection methods using supervised learning models, heuristic filtering of pixels for signal extraction, and event detection using deconvolution. With these methods, we improve neuron detection performance, alter signal-to-noise ratio of extracted calcium signals, and allow for integration of methods that infer action potential firing underlying these signals.</Abstract>
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