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A neural network investigation of th...

Smith, Michael A.

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A neural network investigation of the visuomotor transformation for saccades.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A neural network investigation of the visuomotor transformation for saccades./
作者:	Smith, Michael A.
面頁冊數:	206 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 4092.
Contained By:	Dissertation Abstracts International64-08B.
標題:	Psychology, Experimental. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ82826
ISBN:	0612828263

A neural network investigation of the visuomotor transformation for saccades.
Smith, Michael A.

A neural network investigation of the visuomotor transformation for saccades. - 206 p.

Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 4092.

Thesis (Ph.D.)--York University (Canada), 2003.

The goal of this study was to train artificial neural networks to generate accurate saccades in Listing's plane, and then determine how the hidden units performed the visuomotor transformation.

ISBN: 0612828263Subjects--Topical Terms:

517106
Psychology, Experimental.

A neural network investigation of the visuomotor transformation for saccades.
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245 1 2 $a A neural network investigation of the visuomotor transformation for saccades.
300 $a 206 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 4092.
500 $a Adviser: J. D. Crawford.
502 $a Thesis (Ph.D.)--York University (Canada), 2003.
520 $a The goal of this study was to train artificial neural networks to generate accurate saccades in Listing's plane, and then determine how the hidden units performed the visuomotor transformation.
520 $a CHAPTER 1 networks were designed to illuminate the geometric properties of the visuomotor transformation and therefore used a 3D vector for eye position and motor error, and a 2D vector for retinal error input. The networks in CHAPTER 2 were designed to facilitate a more direct comparison to the physiology of the real saccade generator and used a 6 dimensional encoding of eye position which was designed to mimic the push-pull organization of the extra-ocular muscles. The motor output used the same format as eye position. Visual input consisted of a "retinotopic" input map similar to that of the superficial superior colliculus. In addition, the input units used a Gaussian shaped visual receptive field with which to encode target location.
520 $a The hidden layer of the networks in CHAPTER 1 showed that units divided themselves into 4 parallel modules: a dominant "vector-propagation" class (&sim;50% of units) and 3 classes with specific spatial relations between position, visual, and motor tuning. Surprisingly, the vector-propagation class formed an orthogonal coordinate system aligned with Listing's plane. Selective "lesions" confirmed that this class provided the main drive for saccade magnitude and direction, while a balance between activity in the other classes was required for the correct eye position modulation.
520 $a The hidden units of the networks in CHAPTER 2 developed complex visual receptive fields reminiscent of those found in parietal cortex (Hamed et. al., 2001) while visual sensitivity of these units was modified by eye position using a multiplicative gain field mechanism. In addition, a population code for motor tuning was found to counter-shift with eye position.
520 $a We conclude that error-driven learning is sufficient to produce discrete functional modules and explicit coordinate systems, much like those observed in the real saccade generator and that gain fields work by shifting a motor population code from its visual representation in eye-centered coordinates to a motor vector in head-centered coordinates without ever needing to specify target location in craniotopic coordinates.
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791 $a Ph.D.
792 $a 2003
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