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Neural network models for spatial da...

Parsons, Olga.

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Neural network models for spatial data mining, map production, and cortical direction selectivity.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neural network models for spatial data mining, map production, and cortical direction selectivity./
作者:	Parsons, Olga.
面頁冊數:	156 p.
附註:	Source: Dissertation Abstracts International, Volume: 63-10, Section: B, page: 4527.
Contained By:	Dissertation Abstracts International63-10B.
標題:	Biology, Neuroscience. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3069267
ISBN:	0493888152

Neural network models for spatial data mining, map production, and cortical direction selectivity.
Parsons, Olga.

Neural network models for spatial data mining, map production, and cortical direction selectivity. - 156 p.

Source: Dissertation Abstracts International, Volume: 63-10, Section: B, page: 4527.

Thesis (Ph.D.)--Boston University, 2003.

A family of ARTMAP neural networks for incremental supervised learning has been developed over the last decade. The Sensor Exploitation Group of MIT Lincoln Laboratory (LL) has incorporated an early version of this network as the recognition engine of a hierarchical system for fusion and data mining of multiple registered geospatial images. The LL system has been successfully fielded, but it is limited to target vs. non-target identifications and does not produce whole maps. This dissertation expands the capabilities of the LL system so that it learns to identify arbitrarily many target classes at once and can thus produce a whole map. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of a consistent procedure and a benchmark testbed has permitted the evaluation of candidate recognition networks as well as pre- and post-processing and feature extraction options. The resulting default ARTMAP network and mapping methodology set a standard for a variety of related mapping problems and application domains.

ISBN: 0493888152Subjects--Topical Terms:

1017680
Biology, Neuroscience.

Neural network models for spatial data mining, map production, and cortical direction selectivity.
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500 $a Source: Dissertation Abstracts International, Volume: 63-10, Section: B, page: 4527.
500 $a Major Professor: Gail A. Carpenter.
502 $a Thesis (Ph.D.)--Boston University, 2003.
520 $a A family of ARTMAP neural networks for incremental supervised learning has been developed over the last decade. The Sensor Exploitation Group of MIT Lincoln Laboratory (LL) has incorporated an early version of this network as the recognition engine of a hierarchical system for fusion and data mining of multiple registered geospatial images. The LL system has been successfully fielded, but it is limited to target vs. non-target identifications and does not produce whole maps. This dissertation expands the capabilities of the LL system so that it learns to identify arbitrarily many target classes at once and can thus produce a whole map. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of a consistent procedure and a benchmark testbed has permitted the evaluation of candidate recognition networks as well as pre- and post-processing and feature extraction options. The resulting default ARTMAP network and mapping methodology set a standard for a variety of related mapping problems and application domains.
520 $a The second part of the dissertation investigates the development of cortical direction selectivity. The possible role of visual experience and oculomotor behavior in the maturation of cells in the primary visual cortex is studied. The responses of neurons in the thalamus and cortex of the cat are modeled when natural scenes are scanned by several types of eye movements. Inspired by the Hebbian-like synaptic plasticity, which is based upon correlations between cell activations, the second-order statistical structure of thalamo-cortical activity is examined. In the simulations, patterns of neural activity that lead to a correct refinement of cell responses are observed during visual fixation, when small ocular movements occur, but are not observed in the presence of large saccades. Simulations also replicate experiments in which kittens are reared under stroboscopic illumination. The abnormal fixational eye movements of these cats may account for the puzzling finding of a specific loss of cortical direction selectivity but preservation of orientation selectivity. This work indicates that the oculomotor behavior of visual fixation may play an important role in the refinement of cell response selectivity.
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