Inference in Connectionist Networks
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- Shastri Lokendra
- International Computer Science Institute
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Abstract
We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency—as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a network of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? We briefly review work in connectionist modeling that attempts to address this challenge by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, systematic rules, and knowledge about entities and types, and yet perform a wide range of explanatory and predictive inferences within a few hundred milliseconds.
Journal
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- Cognitive Studies: Bulletin of the Japanese Cognitive Science Society
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Cognitive Studies: Bulletin of the Japanese Cognitive Science Society 10 (1), 45-57, 2003
Japanese Cognitive Science Society
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Keywords
Details 詳細情報について
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- CRID
- 1390282679459267328
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- NII Article ID
- 130004490668
- 80015820720
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- NII Book ID
- AN1047304X
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- ISSN
- 18815995
- 13417924
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- NDL BIB ID
- 6506900
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- CiNii Articles
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- Abstract License Flag
- Disallowed