Cited by 3,315Neural Networks and ApplicationsCognitive Science and Education ResearchCognitive Computing and Networks

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Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Psychological and Biological Models

McClelland, James L., Rumelhart, David E., AU·1987·The MIT Press eBooks

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Abstract

What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind. The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network. Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.

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Jennings, N. R., Mamdani, E. H., Laresgoiti, I., et al. (1992) · Intelligent Systems Engineering · doi:10.1049/ise.1992.0011 · Cited by 61
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Owens, & Filkin (1989) · doi:10.1109/ijcnn.1989.118726 · Cited by 61
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(2015) · doi:10.3115/v1/w15-15 · Cited by 61
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Guo, Z., & Uhrig, R. E. (2003) · doi:10.1109/cogann.1992.273937 · Cited by 60
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Spirkovska, L., & Reid, M. B. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.207615 · Cited by 60
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Port, R. F. (1990) · Connection Science · doi:10.1080/09540099008915667 · Cited by 60
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Lu, T.-C., Yu, G., & Juang, J. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2249089 · Cited by 59
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Liu, H., Iberall, T., & Bekey, G. A. (1989) · IEEE Control Systems Magazine · doi:10.1109/37.24810 · Cited by 59
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Mjolsness, E., & Garrett, C. (1990) · Neural Networks · doi:10.1016/0893-6080(90)90055-p · Cited by 58
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Mohammed, O. A., Park, D.-C., Uler, F. G., et al. (1992) · IEEE Transactions on Magnetics · doi:10.1109/20.179633 · Cited by 58
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Won, D.-O., Jang, Y.-N., & Lee, S. (2022) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2022.3170544 · Cited by 62
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Jena, B., Kurian, P. J., Swain, D., et al. (2011) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2011.11.008 · Cited by 57
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Karuza, E. A., Kahn, A. E., Thompson‐Schill, S. L., & Bassett, D. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-12876-5 · Cited by 57
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Lengelle, R., & Denœux, T. (1996) · Neural Networks · doi:10.1016/0893-6080(95)00096-8 · Cited by 57
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Kim, B., Kim, S., & Kim, K. (2003) · Vacuum · doi:10.1016/s0042-207x(03)00075-7 · Cited by 57
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Guerriere, M., & Detsky, A. S. (1991) · Annals of Internal Medicine · doi:10.7326/0003-4819-115-11-906 · Cited by 57
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Suh, I. H., & Kim, T. W. (1994) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.298449 · Cited by 57
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Iwata, A., Nagasaka, Y., & Suzumura, N. (1990) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.59214 · Cited by 56
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Bolt, G. (1991) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1991.170591 · Cited by 55
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Newton, R. T., & Xu, Y. (1993) · IEEE Control Systems · doi:10.1109/37.247999 · Cited by 55
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Zhang, J., & Morris, J. (1996) · Fuzzy Sets and Systems · doi:10.1016/0165-0114(95)00295-2 · Cited by 55
Evolutional development of a multilevel neural network
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Cetin, B. C., Burdick, J. W., & Barhen, J. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1993.298667 · Cited by 50
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Authors: McClelland, J. L., Rumelhart, D. E., & AU
ORCID: McClelland, James L.
Venue: The MIT Press eBooks
Publisher: The MIT Press
Published: 1987
DOI: 10.7551/mitpress/5237.001.0001
OpenAlex: W4253020087
Cited by: 3,315
References: 0
Citations by year: 2026: 14 · 2025: 46 · 2024: 73 · 2023: 134 · 2022: 85 · 2021: 91 · 2020: 68 · 2019: 78 · 2018: 69 · 2017: 65 · 2016: 64 · 2015: 54 · 2014: 59 · 2013: 41 · 2012: 39
FWCI: 42.46
Type: book
Language: en
Open access: closed
Locations: The MIT Press eBooks (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
Keywords: parallel distributed processing, connectionism, artificial neural networks, cognitive architecture, symbolic computation, distributed knowledge, perception, memory, language, thinking, neuroscience, cognitive science

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Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Psychological and Biological Models

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