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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01bg257j09f
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dc.contributor.advisorBerry, Michael
dc.contributor.authorGaura, Alexander
dc.date.accessioned2020-09-29T17:04:06Z-
dc.date.available2020-09-29T17:04:06Z-
dc.date.created2020-05-12
dc.date.issued2020-09-29-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01bg257j09f-
dc.description.abstractTraditional neural network models are based on ideas from neuroscience that have since become outdated. This paper investigates the performance of new neural network models that are based on modern neuroscience and compares their performance to other models on similar datasets. In particular, these models are useful for unsupervised learning, and differ most in how they receive input, their layering, and their learning rule which is based on Hebbian plasticity.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleBrain-Based Machine Learning Algorithms - Alexander Gaura
dc.typePrinceton University Senior Theses
pu.date.classyear2020
pu.departmentMathematics
pu.pdf.coverpageSeniorThesisCoverPage
pu.contributor.authorid920060731
pu.certificateCenter for Statistics and Machine Learning
Appears in Collections:Mathematics, 1934-2023

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