parallel distributed processing

E548708

cognitive framework computational framework connectionist approach neural network model family theoretical approach in cognitive science

Parallel distributed processing is a cognitive and computational framework in which mental processes emerge from the simultaneous activity of many simple, interconnected processing units, often implemented as neural networks.

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Predicate	Object
instanceOf	cognitive framework ⓘ computational framework ⓘ connectionist approach ⓘ neural network model family ⓘ theoretical approach in cognitive science ⓘ
alsoKnownAs	PDP ⓘ connectionism NERFINISHED ⓘ
appliedTo	language processing ⓘ memory modeling ⓘ motor control ⓘ pattern recognition ⓘ visual perception ⓘ
assumes	knowledge stored in connections rather than symbols ⓘ many simple units operating in parallel ⓘ processing is distributed across units ⓘ
basedOn	distributed representations ⓘ networks of simple processing units ⓘ neural network architectures ⓘ parallel computation ⓘ
contrastsWith	classical information-processing models ⓘ symbolic AI ⓘ
describes	cognition as patterns of activation over units ⓘ knowledge as weights on connections ⓘ learning as changes in connection strengths ⓘ mental processes as emergent from network activity ⓘ
developedBy	David E. Rumelhart NERFINISHED ⓘ James L. McClelland NERFINISHED ⓘ PDP Research Group NERFINISHED ⓘ
documentedIn	Parallel Distributed Processing: Explorations in the Microstructure of Cognition NERFINISHED ⓘ
emergedIn	1980s ⓘ
fieldOfStudy	artificial intelligence ⓘ cognitive psychology ⓘ cognitive science ⓘ computational neuroscience NERFINISHED ⓘ
hasKeyConcept	activation patterns ⓘ attractor states ⓘ backpropagation learning rule ⓘ connection weights ⓘ constraint satisfaction ⓘ content-addressable memory ⓘ distributed encoding of concepts ⓘ distributed memory ⓘ distributed representation of information ⓘ emergent computation ⓘ error-driven learning ⓘ graceful degradation ⓘ hidden units ⓘ layered network structures ⓘ learning by weight adjustment ⓘ parallel constraint satisfaction ⓘ parallel information processing ⓘ pattern completion ⓘ spreading activation ⓘ
hasVolume	Parallel Distributed Processing, Volume 1: Foundations NERFINISHED ⓘ Parallel Distributed Processing, Volume 2: Psychological and Biological Models NERFINISHED ⓘ
influenced	computational models of language ⓘ deep learning NERFINISHED ⓘ models of memory ⓘ models of perception ⓘ modern neural network architectures ⓘ
influencedBy	early neural network research ⓘ neuroscience ⓘ statistical learning theory ⓘ
relatedTo	artificial neural networks ⓘ associative memory models ⓘ connectionist cognitive models ⓘ
supports	generalization from experience ⓘ learning from examples ⓘ robustness to noise and damage ⓘ

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David E. Rumelhart → knownFor → parallel distributed processing ⓘ