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in00001255141 |
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20151003082651.0 |
| 008 |
920316s1991 txua tb 000 0 eng d |
| 035 |
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|a (OCoLC)25478987
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| 035 |
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|9 AGU6627AM
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| 040 |
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|a PMC
|c PMC
|d TXA
|d UtOrBLW
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| 049 |
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|a TXAS [tamu]
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| 090 |
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|a QA76
|b .C656 91-032
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| 100 |
1 |
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|a Saxon, James Bennett,
|d 1964-
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| 245 |
1 |
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|a Simulating sensorimotor systems with cortical topology /
|c James Bennett Saxon.
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| 264 |
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1 |
|a College Station, Tex. :
|b Texas A & M University, Computer Science Dept.,
|c [1991]
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| 300 |
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|a xi, 119 leaves :
|b illustrations ;
|c 28 cm.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a unmediated
|b n
|2 rdamedia
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| 338 |
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|a volume
|b nc
|2 rdacarrier
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| 490 |
1 |
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|a Technical report. Texas A & M University. Computer Science Dept. ;
|v TAMU-91-032
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| 500 |
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|a "July 1991."
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| 502 |
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|b M.S.
|c Texas A&M University
|d 1991.
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| 520 |
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|a Abstract: "This broadly oriented thesis defines different avenues into understanding brain-like intelligence. We categorize our research under the term neurobotics, which we have defined as the study of neurally inspired intelligent systems which causally interact with their external world. It comes at this issue from three different directions: the theoretical, the computational, and the empirical. We first focus on robot arm/robot eye sensorimotor systems by categorizing previous work into a theoretical timescape classification. Because of the simple and causal closed-loop between the arm and the eye, this system becomes a very useful system for developing actual models to test our theories of neurobotics.
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| 520 |
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|a To practically address the issues raised, we develop a large brain simulation environment, called the Neurobotics Simulation Package (NSP) which, [sic] is capable of simulating and visualizing complex sensorimotor systems based on heterogeneous neural networks representing multiple topological brain areas. Finally, to take us one step closer toward the empirical relevance to our theories, we explore the diverse capabilities of cortical areas in the brain by extending research on self- organizing neural networks (Kohonen, 1988; Obermeyer et al., 1990).
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| 520 |
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|a The results of our simulations, along with physiological data, suggest that a neural paradigm can be more powerful than the self organizing abstraction because it relaxes the requirements of a stringent topological mapping and allows for degenerate, distributed, spatially- organized, but also fragmented neural mappings (Stryker, 1989)."
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| 504 |
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|a Includes bibliographical references.
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| 650 |
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0 |
|a Robotics.
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| 650 |
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0 |
|a Neural networks (Computer science)
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| 650 |
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0 |
|a Artificial intelligence.
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| 830 |
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|a Technical report (Texas A & M University. Computer Science Department) ;
|v 91-032.
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| 999 |
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|a MARS
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| 999 |
f |
f |
|s 73d44215-130e-3266-8662-848b9bbfbc66
|i 1274aa9b-712f-34c6-9f6e-35f8b9e9d3c3
|t 0
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| 952 |
f |
f |
|p noncirc
|a Texas A&M University
|b College Station
|c Cushing Memorial Library & Archives
|d Cushing: Texas A&M (Does not check out)
|t 0
|e QA76 .C656 91-032
|h Library of Congress classification
|i unmediated -- volume
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| 998 |
f |
f |
|a QA76 .C656 91-032
|t 0
|l Cushing: Texas A&M (Does not check out)
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