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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Howard, D. (1995) · The Quarterly Journal of Experimental Psychology Section A · doi:10.1080/14640749508401426 · Cited by 80
Parallel, self-organizing, hierarchical neural networks
Ersoy, O. K., & Hong, D. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80229 · Cited by 80
An incremental approach to developing intelligent neural network controllers for robots
Meeden, L. (1996) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.499797 · Cited by 80
Capturing the objects of vision with neural networks
Peters, B., & Kriegeskorte, N. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01194-6 · Cited by 79
Brain reflections: A circuit‐based framework for understanding information processing and cognitive control
Gratton, G. (2017) · Psychophysiology · doi:10.1111/psyp.13038 · Cited by 78
Robustness analysis of radial base function and multi-layered feed-forward neural network models
Derks, E. P. P. A., Pastor, M. S. S., & Buydens, L. M. C. (1995) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(95)80039-c · Cited by 79
Some extensions of radial basis functions and their applications in artificial intelligence
Girosi, F. (1992) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(92)90172-e · Cited by 79
Pattern recognition by graph matching using the Potts MFT neural networks
Suganthan, P. N., Teoh, E. K., & Mital, D. P. (1995) · Pattern Recognition · doi:10.1016/0031-3203(94)00166-j · Cited by 79
On learning a union of half spaces
Baum, E. B. (1990) · Journal of Complexity · doi:10.1016/0885-064x(90)90012-3 · Cited by 76
Integration versus interactive activation: The joint influence of stimulus and context in perception
Massaro, D. W., & Cohen, M. M. (1991) · Cognitive Psychology · doi:10.1016/0010-0285(91)90006-a · Cited by 79
Programmable impulse neural circuits
Meador, J. L., Wu, A., Cole, C., Nintunze, N., & Chintrakulchai, P. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.80295 · Cited by 77
Introduction: The Emergence of Constraints in Generative Phonology and a Comparison of Three Current Constraint-Based Models
LaCharité, D., & Paradis, C. (1993) · The Canadian Journal of Linguistics / La revue canadienne de linguistique · doi:10.1017/s0008413100014717 · Cited by 78
Underground blast induced ground shock and its modelling using artificial neural network
Lu, Y. (2005) · Computers and Geotechnics · doi:10.1016/j.compgeo.2005.01.007 · Cited by 78
Invariance and neural nets
Barnard, E., & Casasent, D. P. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.134287 · Cited by 75
Universal learning network and its application to chaos control
Hirasawa, K., Wang, X., Murata, J., Hu, J., & Jin, C. (2000) · Neural Networks · doi:10.1016/s0893-6080(99)00100-8 · Cited by 78
Speaker recognition—general classifier approaches and data fusion methods
Ramachandran, R. P., Farrell, K. R., Ramachandran, R., & Mammone, R. J. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(01)00235-7 · Cited by 77
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Marshall, N. (2008) · Management Learning · doi:10.1177/1350507608093712 · Cited by 85
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Oh, S.-H. (2011) · Neurocomputing · doi:10.1016/j.neucom.2010.11.024 · Cited by 77
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Lu, Q., Hasson, U., & Norman, K. A. (2022) · eLife · doi:10.7554/elife.74445 · Cited by 91
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Taylor, J. G., & Coombes, S. (1993) · Neural Networks · doi:10.1016/0893-6080(93)90009-l · Cited by 76
A simple and effective method for removal of hidden units and weights
Hagiwara, M. (1994) · Neurocomputing · doi:10.1016/0925-2312(94)90055-8 · Cited by 74
An Artificial Life Approach to Language
Parisi, D. (1997) · Brain and Language · doi:10.1006/brln.1997.1815 · Cited by 74
Convolutional neural networks with fractional order gradient method
Sheng, D., Wei, Y., Chen, Y., & Wang, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.017 · Cited by 77
Cavity method for quantum spin glasses on the Bethe lattice
Laumann, C. R., Scardicchio, A., & Sondhi, S. L. (2008) · Physical Review B · doi:10.1103/physrevb.78.134424 · Cited by 75
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Gabora, L. M. (2007) · Cognitive Systems Research · doi:10.1016/j.cogsys.2007.05.004 · Cited by 76
The interaction of implicit learning, explicit hypothesis testing learning and implicit-to-explicit knowledge extraction
Sun, R., Zhang, X., Slusarz, P., & Mathews, R. C. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.07.002 · Cited by 74
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Moreira-Matias, L., Cats, O., Gama, J., Mendes-Moreira, J., & de Sousa, J. F. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.06.031 · Cited by 75
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Johnston, W. J., Palmer, S. E., & Freedman, D. J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007544 · Cited by 78
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Blauch, N. M., Behrmann, M., & Plaut, D. C. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112566119 · Cited by 78
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Li, R., Mukaidono, M., & Turksen, I. B. (2002) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(02)00050-7 · Cited by 74
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Ghosh‐Dastidar, S., & Adeli, H. (2009) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-03156-4_17 · Cited by 74
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Milik, M., Sauer, D., Brunmark, A. P., et al. (1998) · Nature Biotechnology · doi:10.1038/nbt0898-753 · Cited by 74
FUN: optimization of fuzzy rule based systems using neural networks
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Han, M., Snow, P. B., Epstein, J. I., et al. (2000) · Urology · doi:10.1016/s0090-4295(00)00815-3 · Cited by 74
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Li, D., & Spiegel, D. (1992) · Psychiatric Annals · doi:10.3928/0048-5713-19920301-11 · Cited by 72
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Lehtonen, M., Vorobyev, V., Hugdahl, K., Tuokkola, T., & Laine, M. (2006) · Brain and Language · doi:10.1016/j.bandl.2006.04.011 · Cited by 72
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Merz, C. J. (1996) · Lecture notes in statistics · doi:10.1007/978-1-4612-2404-4_27 · Cited by 71
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Galván, I. M., Zaldı́var, J. M., Hernández, H., et al. (1996) · Computers & Chemical Engineering · doi:10.1016/0098-1354(95)00231-6 · Cited by 72
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Rei, Spirkovska, & Ochoa (1989) · doi:10.1109/ijcnn.1989.118653 · Cited by 71
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Tsukamoto, Y., & Omi, N. (2016) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00104 · Cited by 64
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Bengio, Y., & Courville, A. C. (2013) · Intelligent systems reference library · doi:10.1007/978-3-642-36657-4_1 · Cited by 66
Connectionist Simulation of Attitude Learning: Asymmetries in the Acquisition of Positive and Negative Evaluations
Eiser, J. R., Fázio, R. H., Stafford, T., & Prescott, T. J. (2003) · Personality and Social Psychology Bulletin · doi:10.1177/0146167203254605 · Cited by 66
A CMOS analog adaptive BAM with on-chip learning and weight refreshing
Linares-Barranco, B., Sánchez‐Sinencio, E., Rodriguez-Vazquez, A., & Huertas, J. L. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.217187 · Cited by 64
A comparison between criterion functions for linear classifiers, with an application to neural nets
Barnard, E., & Casasent, D. P. (1989) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.44018 · Cited by 64
Semantic Memory and Priming
McNamara, T. P., & Holbrook, J. B. (2003) · Handbook of Psychology · doi:10.1002/0471264385.wei0416 · Cited by 67
From the head to the heart: Some thoughts on similarities between brain function and morphogenesis, and on their significance for research methodology and biological theory
Grobstein, P. (1988) · Cellular and Molecular Life Sciences · doi:10.1007/bf01939890 · Cited by 64
An application of real-time error compensation on a turning center
Lo, C.-H., Yuan, J., & Ni, J. (1995) · International Journal of Machine Tools and Manufacture · doi:10.1016/0890-6955(95)97296-c · Cited by 63
Artificial neural networks based models for automatic performance of musical scores
Bresin, R. (1998) · Journal of New Music Research · doi:10.1080/09298219808570748 · Cited by 63
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Myshkin, N. K., Kwon, O., Grigoriev, А. Y., et al. (1997) · Wear · doi:10.1016/s0043-1648(96)07432-7 · Cited by 63
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Singh, R. M., & Datta, B. (2004) · Environmental Forensics · doi:10.1080/15275920490495873 · Cited by 63
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Goodacre, R., & Kell, D. B. (1993) · Analytica Chimica Acta · doi:10.1016/0003-2670(93)85062-o · Cited by 63
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Dai, H., Zhang, B., & Wang, W. (2014) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2014.12.002 · Cited by 62
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Dhond, R. P., Marinković, K., Dale, A. M., Witzel, T., & Halgren, E. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00047-8 · Cited by 61
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Ritt, N. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511486449 · Cited by 62
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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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