2026-09-20T11:02:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1206582026-06-16T18:53:07Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Nir Shavit, Edward S. Boyden, and Max Tegmark.
Rolnick, David (David S.)
Massachusetts Institute of Technology. Department of Mathematics.
Massachusetts Institute of Technology. Department of Mathematics
2019-03-01T19:55:50Z
2019-03-01T19:55:50Z
2018
2018
http://hdl.handle.net/1721.1/120658
1088414386
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 123-136).
Neural networks underpin both biological intelligence and modern Al systems, yet there is relatively little theory for how the observed behavior of these networks arises. Even the connectivity of neurons within the brain remains largely unknown, and popular deep learning algorithms lack theoretical justification or reliability guarantees. This thesis aims towards a more rigorous understanding of neural networks. We characterize and, where possible, prove essential properties of neural algorithms: expressivity, learning, and robustness. We show how observed emergent behavior can arise from network dynamics, and we develop algorithms for learning more about the network structure of the brain.
by David Rolnick.
Ph.D.
136 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
Mathematics.
Towards an integrated understanding of neural networks
Thesis
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