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A model of microtubule based learnin...

Pfaffmann, Jeffrey Oswald.

A model of microtubule based learning for perception-action behavior control.

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	A model of microtubule based learning for perception-action behavior control./
作者:	Pfaffmann, Jeffrey Oswald.
面頁冊數:	269 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1340.
Contained By:	Dissertation Abstracts International64-03B.
標題:	Computer Science. -
電子資源:	Download fulltext (下載全文)
ISBN:	0496342592

A model of microtubule based learning for perception-action behavior control.
Pfaffmann, Jeffrey Oswald.

A model of microtubule based learning for perception-action behavior control. - 269 p.

Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1340.

Thesis (Ph.D.)--Wayne State University, 2003.

The eukaryotic cell is a computational device that performs perception-action behavior, which requires a long-range signaling mechanism. The micro-tubule network is the only intracellular structure that provides the structural characteristics needed for this type of intracellular signaling. From these characteristics, and preliminary experimental evidence, a variety of signaling mechanisms have been proposed in the literature. To explore this hypothesis, the microtubule learning model (MtLM) is presented that combines a biologically motivated learning mechanism with an abstract vibratory signaling dynamics, providing a bridge between machine learning and mainstream cell biology.

ISBN: 0496342592Subjects--Topical Terms:

1000005419
Computer Science.

A model of microtubule based learning for perception-action behavior control.
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245 1 2 $a A model of microtubule based learning for perception-action behavior control.
300 $a 269 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1340.
500 $a Advisers: Monica Brockmeyer; Silvano Colombano.
502 $a Thesis (Ph.D.)--Wayne State University, 2003.
520 $a The eukaryotic cell is a computational device that performs perception-action behavior, which requires a long-range signaling mechanism. The micro-tubule network is the only intracellular structure that provides the structural characteristics needed for this type of intracellular signaling. From these characteristics, and preliminary experimental evidence, a variety of signaling mechanisms have been proposed in the literature. To explore this hypothesis, the microtubule learning model (MtLM) is presented that combines a biologically motivated learning mechanism with an abstract vibratory signaling dynamics, providing a bridge between machine learning and mainstream cell biology.
520 $a The presented MtLM is shown to perform well within the context of two different perception-action frameworks. The first framework, the "center finding problem", is a robot navigation task where the MtLM must find the center of a virtual two-dimensional space when given a random starting point. The second framework, the biot, is a novel biomimetic robot architecture that consists of several segments interconnected in a highly context-sensitive fashion (exhibiting some degree of randomness). This framework is designed to simulate the context-sensitivity that is typical of interactions inherent between different components of the eukaryotic cell, providing the MtLM with a biologically plausible learning task.
520 $a This work, by providing a functional example of intracellular long-range signaling through the MtLM, reinforces the hypothesis that the long-range signaling mechanism in the eukaryotic cell is the microtubule network. Additionally, application of the MtLM to these differing frameworks illustrates the importance of structure in any system constructed in a bottom-up fashion, and highlights the differences between information processing tasks typically performed at the cellular level and in higher-order cognitive tasks. Lastly, this work also illustrates the strength of the MtLM as a control mechanism for producing tuned oscillatory activity.
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