Event chains and inverse problems with applications to neuroscience

Authors
Warner, Andrew
ORCID
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Other Contributors
Drew, Donald A. (Donald Allen), 1945-
Kramer, Peter Roland, 1971-
Krishnamoorthy, M. S.
Li, Fengyan
Issue Date
2012-12
Keywords
Mathematics
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Of particular interest is whether brains subjected to learning can be distinguished from brains generated by statistical procedures. The learning algorithms implemented include Hebbian, Anti-Hebbian, Oja, and Sanger's rule. All of these rules adaptively modify the connection strengths, and allow for the creation of new connections. The event-chain data shows conclusively that brains that have undergone learning are significantly different. This unique structure and behavior of learned brains allows for identification based on purely behavioral characteristics; specifically, the event-chain data is sufficient to distinguish between learned and unlearned brains models.
Description
December 2012
School of Science
Department
Dept. of Mathematical Sciences
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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