Deconstructing the cerebellar learning algorithm

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Abstract/Contents

Abstract
This thesis is composed of experimental and analytic work deconstructing the cerebellar learning algorithm. The central finding is that two parallel neural circuits, with independent instructive signals, support cerebellum-dependent learning. An analytic framework for this result, based on overcoming signal-dependent noise, is proposed.

Description

Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Copyright date 2013
Publication date 2012, c2013; 2012
Issuance monographic
Language English

Creators/Contributors

Associated with Rinaldi, Jacob
Associated with Stanford University, Neurosciences Program.
Primary advisor Raymond, Jennifer L
Thesis advisor Raymond, Jennifer L
Thesis advisor Clandinin, Thomas R. (Thomas Robert), 1970-
Thesis advisor Greicius, Michael D
Thesis advisor Moore, Tirin, 1969-
Advisor Clandinin, Thomas R. (Thomas Robert), 1970-
Advisor Greicius, Michael D
Advisor Moore, Tirin, 1969-

Subjects

Genre Theses

Bibliographic information

Statement of responsibility Jacob Rinaldi.
Note Submitted to the Program in Neuroscience.
Thesis Thesis (Ph.D.)--Stanford University, 2012.
Location electronic resource

Access conditions

Copyright
© 2013 by Jacob Matteo Rinaldi
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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