Neural Networks for Knowledge Representation and Inference

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advanced neural network reasoning applications
Automatic Attention Response
Basis Waveforms
C3 Cluster
case-based
Category=JM
Category=UYQ
Category=UYQN
CNV.
Complex Cognitive Control
connectionism
connectionist
connectionist models
Dendritic Microprocesses
Dendritic Network
energy
epistemology in AI
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eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
ERP Component
ERP Response
experimental psychology
Finite Dimensional Euclidean Spaces
Frontolimbic Forebrain
functions
Hilbert Spaces
hopfield
implementational
Input Signal
Instrumental Conditioning
Knowledge Acquisition
legal knowledge modeling
Maximal Cliques
Memory Set Items
models
narrative cognition
Neural Channel
Operand Order
PCA Method
PDP Model
quadratic
reasoning
Stable Storage Results
Stimulus Matrix
symbolic reasoning
Vice Versa

Product details

  • ISBN 9780805811582
  • Weight: 1140g
  • Dimensions: 152 x 229mm
  • Publication Date: 01 Oct 1993
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones.

Organized into four major sections, this volume:
* outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum;
* introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs;
* shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations;
* discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.

Daniel S. Levine, Manuel Aparicio IV