Event chains and inverse problems with applications to neuroscience
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Authors
Warner, Andrew
Issue Date
2012-12
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Mathematics
Alternative Title
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
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY