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Optimization and optimal statistics ...

Brookings, Ted.

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Optimization and optimal statistics in neuroscience.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Optimization and optimal statistics in neuroscience./
Author:	Brookings, Ted.
Description:	147 p.
Notes:	Adviser: Jean Carlson.
Contained By:	Dissertation Abstracts International68-10B.
Subject:	Biology, Neuroscience. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3283681
ISBN:	9780549268796

Optimization and optimal statistics in neuroscience.
Brookings, Ted.

Optimization and optimal statistics in neuroscience. - 147 p.

Adviser: Jean Carlson.

Thesis (Ph.D.)--University of California, Santa Barbara, 2007.

Complex systems have certain common properties, with power law statistics being nearly ubiquitous. Despite this commonality, we show that a variety of mechanisms can be responsible for complexity, illustrated by the example of a lattice on a Cayley Tree. Because of this, analysis must probe more deeply than merely looking for power laws, instead details of the dynamics must be examined. We show how optimality---a frequently-overlooked source of complexity---can produce typical features such as power laws, and describe inherent trade-offs in optimal systems, such as performance vs. robustness to rare disturbances. When applied to biological systems such as the nervous system, optimality is particularly appropriate because so many systems have identifiable purpose. We show that the "grid cells" in rats are extremely efficient in storing position information. Assuming the system to be optimal allows us to describe the number and organization of grid cells. By analyzing systems from an optimal perspective provides insights that permit description of features that would otherwise be difficult to observe. As well, careful analysis of complex systems requires diligent avoidance of assumptions that are unnecessary or unsupported. Attributing unwarranted meaning to ambiguous features, or assuming the existence of a priori constraints may quickly lead to faulty results. By eschewing unwarranted and unnecessary assumptions about the distribution of neural activity and instead carefully integrating information from EEG and fMRI, we are able to dramatically improve the quality of source-localization. Thus maintaining a watchful eye towards principles of optimality, while avoiding unnecessary statistical assumptions is an effective theoretical approach to neuroscience.

ISBN: 9780549268796Subjects--Topical Terms:

1017680
Biology, Neuroscience.

Optimization and optimal statistics in neuroscience.
LDR:02751nam 2200301 a 45 001 942153
005 20110519
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020 $a 9780549268796
035 $a (UMI)AAI3283681
035 $a AAI3283681
040 $a UMI $c UMI
100 1 $a Brookings, Ted. $3 1266250
245 1 0 $a Optimization and optimal statistics in neuroscience.
300 $a 147 p.
500 $a Adviser: Jean Carlson.
500 $a Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6499.
502 $a Thesis (Ph.D.)--University of California, Santa Barbara, 2007.
520 $a Complex systems have certain common properties, with power law statistics being nearly ubiquitous. Despite this commonality, we show that a variety of mechanisms can be responsible for complexity, illustrated by the example of a lattice on a Cayley Tree. Because of this, analysis must probe more deeply than merely looking for power laws, instead details of the dynamics must be examined. We show how optimality---a frequently-overlooked source of complexity---can produce typical features such as power laws, and describe inherent trade-offs in optimal systems, such as performance vs. robustness to rare disturbances. When applied to biological systems such as the nervous system, optimality is particularly appropriate because so many systems have identifiable purpose. We show that the "grid cells" in rats are extremely efficient in storing position information. Assuming the system to be optimal allows us to describe the number and organization of grid cells. By analyzing systems from an optimal perspective provides insights that permit description of features that would otherwise be difficult to observe. As well, careful analysis of complex systems requires diligent avoidance of assumptions that are unnecessary or unsupported. Attributing unwarranted meaning to ambiguous features, or assuming the existence of a priori constraints may quickly lead to faulty results. By eschewing unwarranted and unnecessary assumptions about the distribution of neural activity and instead carefully integrating information from EEG and fMRI, we are able to dramatically improve the quality of source-localization. Thus maintaining a watchful eye towards principles of optimality, while avoiding unnecessary statistical assumptions is an effective theoretical approach to neuroscience.
590 $a School code: 0035.
650 4 $a Biology, Neuroscience. $3 1017680
650 4 $a Physics, Theory. $3 1019422
690 $a 0317
690 $a 0753
710 2 $a University of California, Santa Barbara. $b Physics. $3 1020469
773 0 $t Dissertation Abstracts International $g 68-10B.
790 $a 0035
790 1 0 $a Carlson, Jean, $e advisor
790 1 0 $a Grafton, Scott $e committee member
790 1 0 $a Ludwig, Andreas $e committee member
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3283681