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Neural Networks for Knowledge Representation and Inference [NOOK Book]

Overview

First Published in 1993. Routledge is an imprint of Taylor & Francis, an informa company.
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Neural Networks for Knowledge Representation and Inference

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Overview

First Published in 1993. Routledge is an imprint of Taylor & Francis, an informa company.
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Product Details

  • ISBN-13: 9781134771615
  • Publisher: Taylor & Francis
  • Publication date: 4/15/2013
  • Sold by: Barnes & Noble
  • Format: eBook
  • Pages: 528
  • File size: 11 MB
  • Note: This product may take a few minutes to download.

Table of Contents

Preface
List of Contributors
1 Why are Neural Networks Relevant to Higher Cognitive Function? 1
2 On Using Analogy to Reconcile Connections and Symbols 27
3 Semiotics, Meaning, and Discursive Neural Networks 65
4 Continuous Symbol Systems: The Logic of Connectionism 83
5 Representing Discrete Structures in a Hopfield-Style Network 123
6 Modeling and Stability Analysis of a Truth Maintenance System Neural Network 143
7 Propositional Logic, Nonmonotonic Reasoning, and Symmetric Networks - On Bridging the Gap Between Symbolic and Connectionist Knowledge Representation 175
8 The Representation of Knowledge and Rules in Hierarchical Neural Networks 205
9 Connectionist Models of Commonsense Reasoning 241
10 Toward Connectionist Representation of Legal Knowledge 269
11 Markov Random Fields for Text Comprehension 283
12 A Study in Numerical Perversity: Teaching Arithmetic to a Neural Network 311
13 Toward a Theory of Learning and Representing Causal Inferences in Neural Networks 339
14 Brain and the Structure of Narrative 375
15 Neuroelectric Eigenstructures of Mental Representation 419
16 Automatic Versus Controlled Processing in Variable Temporal Context and Stimulus-Response Mapping 447
Author Index 471
Subject Index 483
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