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To implement schemas and logics in connectionist models, some form of basic-level organization is needed. This paper proposes such an organization, which is termed a discrete neural assembly. Each discrete neural assembly is in turn made up of discrete neurons (nodes), that is, a node that...
Sophisticated symbol processing in connectionist systems can be supported by two primitive representational techniques calledRelative-Position Encoding (RPE) andPattern-Similarity Association (PSA), and a selection technique calledTemporal-Winner-Take-All (TWTA). TWTA effects winner-take-all...
DISCERN is an integrated natural language processing system built entirely from distributed neural networks. It reads short narratives about stereotypical event sequences, stores them in episodic memory, generates fully expanded paraphrases of the narratives, and answers questions about them....
In this paper we describe DYNASTY, a multi-module distributed connectionist system designed to perform a very high-level symbolic task, namely, comprehension of goal/plan-based stories. DYNASTY has two phases of operation: learning and performance. During learning, each DYNASTY module acquires...
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