Parallel distributed processing and neural networks: Origins, methodology and cognitive functions

Randolph W. Parks, Debra L. Long, Daniel S. Levine, David J. Crockett, Edith G. Mcgeer, Patrick L. Mcgeer, Irene E. Dalton, Ronald F. Zec, Robert E. Becker, Kerry L. Coburn, Gil Siler, Mark E. Nelson, James M. Bower

Research output: Contribution to journalArticlepeer-review


Parallel Distributed Processing (PDP), a computational methodology with origins in Associationism, is used to provide empirical information regarding neurobiological systems. Recently, supercomputers have enabled neuroscientists to model brain behavior-relationships. An overview of supercomputer architecture demonstrates the advantages of parallel over serial processing. Histological data provide physical evidence of the parallel distributed nature of certain aspects of the human brain, as do corresponding computer simulations. Whereas sensory networks follow more sequential neural network pathways, in vivo brain imaging studies of attention and rudimentary language tasks appear to involve multiple cortical and subcortical areas. Controversy remains as to whether associative models or Artificial Intelligence symbolic models better reflect neural networks of cognitive functions; however, considerable interest has shifted towards associative models.

Original languageEnglish (US)
Pages (from-to)195-214
Number of pages20
JournalInternational Journal of Neuroscience
Issue number2
StatePublished - 1991
Externally publishedYes


  • Artificial intelligence
  • Associationism
  • Cognition
  • Neural networks
  • Neuropsychology
  • Parallel distributed processing
  • Positron emission tomography

ASJC Scopus subject areas

  • Neuroscience(all)


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