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Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms
Abstract
Abstract : Part I attempts to review the background, basic sources of data, concepts, and methodology to be employed in the study of perceptrons. In Chapter 2, a brief review of the main alternative approaches to the development of brain models is presented. Chapter 3 considers the physiological and psychological criteria for a suitable model, and attempts to evaluate the empirical evidence which is available on several important issues. Chapter 4 contains basic definitions and some of the notation to be used in later sections are presented. Parts II and III are devoted to a summary of the established theoretical results obtained to date. Part II (Chapters 5 through 14) deals with the theory of three-layer series-coupled perceptrons, on which most work has been done to date. Part III (Chapters 15 through 20) deals with the theory of multi-layer and cross-coupled perceptrons. Part IV is concerned with more speculative models and problems for future analysis. Of necessity, the final chapters become increasingly heuristic in character, as the theory of perceptrons is not yet complete, and new possibilities are continually coming to light.
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Cited by
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578
A learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402
A theory of cerebellar function
Albus, J. S. (1971) · Mathematical Biosciences · doi:10.1016/0025-5564(71)90051-4 · Cited by 2,539
Changeux, J.-P., & Danchin, A. (1976) · Nature · doi:10.1038/264705a0 · Cited by 1,448
A Survey of Quantization Methods for Efficient Neural Network Inference
Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., & Keutzer, K. (2022) · Low-Power Computer Vision · doi:10.1201/9781003162810-13 · Cited by 1,219
“traced back to the seminal work of [199, 200], where an identity operator was used to approximate gradient from the binary neurons.”
Machine learning in bioinformatics
Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J. A., Armañanzas, R., Santafé, G., Pérez, A., et al. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbk007 · Cited by 867
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Hippocampal–prefrontal input supports spatial encoding in working memory
Spellman, T., Rigotti, M., Ahmari, S. E., Fusi, S., Gogos, J. A., & Gordon, J. A. (2015) · Nature · doi:10.1038/nature14445 · Cited by 693
A synaptic organizing principle for cortical neuronal groups
Perin, R., Berger, T. K., & Markram, H. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016051108 · Cited by 718
Machine learning in acoustics: Theory and applications
Bianco, M. J., Gerstoft, P., Traer, J., Ozanich, E., Roch, M. A., Gannot, S., & Deledalle, C.-A. (2019) · The Journal of the Acoustical Society of America · doi:10.1121/1.5133944 · Cited by 609
Jones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533
Seide, F., Fu, H., Droppo, J., Li, G., & Yu, D. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-274 · Cited by 443
Bayesian just-so stories in psychology and neuroscience.
Bowers, J. S., & Davis, C. J. (2012) · Psychological Bulletin · doi:10.1037/a0026450 · Cited by 436
UX Design Innovation: Challenges for Working with Machine Learning as a Design Material
Dove, G., Halskov, K., Forlizzi, J., & Zimmerman, J. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025739 · Cited by 391
The unreasonable effectiveness of deep learning in artificial intelligence
Sejnowski, T. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907373117 · Cited by 352
Neural Rating Regression with Abstractive Tips Generation for Recommendation
Li, P., Wang, Z., Ren, Z., Bing, L., & Lam, W. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080822 · Cited by 313
ITO, M. (2002) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2002.tb07574.x · Cited by 312
Can deep learning improve genomic prediction of complex human traits?
Bellot, P., de los Campos, G., & Pérez-Enciso, M. (2018) · Genetics · doi:10.1534/genetics.118.301298 · Cited by 225
eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212
“In addition, we tested two other classifiers, the Linear Discriminant Analysis (LDA) [67] and Multi-layer Perceptron (MLP) [68], however, their performance is generally not as high as those of RF and ET.”
MethodIntegration of acoustic and visual speech signals using neural networks
Yuhas, B. P., Goldstein, M. H., & Sejnowski, T. J. (1989) · IEEE Communications Magazine · doi:10.1109/35.41402 · Cited by 209
Min, X., Yu, B., & Wang, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-39071-y · Cited by 170
Deep Learning for Mobile Multimedia: A Survey
Ota, K., Dao, M. S., Mezaris, V., & Natale, F. G. B. D. (2017) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3092831 · Cited by 135
Deep learning versus traditional machine learning methods for aggregated energy demand prediction
Paterakis, N. G., Mocanu, E., Gibescu, M., Stappers, B., & van Alst, W. (2017) · 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) · doi:10.1109/isgteurope.2017.8260289 · Cited by 118
McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J., & Schütze, H. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910416117 · Cited by 91
Kaur, A., Singh, Y., Neeru, N., Kaur, L., & Singh, A. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09649-9 · Cited by 79
On the Binding Problem in Artificial Neural Networks
Greff, K., van Steenkiste, S., & Schmidhuber, J. (2020) · arXiv · doi:10.48550/arxiv.2012.05208 · Cited by 102
Towards Probabilistic Verification of Machine Unlearning
Sommer, D. M., Song, L., Wagh, S., & Mittal, P. (2020) · arXiv · doi:10.48550/arxiv.2003.04247 · Cited by 38
On Incorporating Affective Support to an Intelligent Tutoring System: an Empirical Study
Cunha-Perez, C., Arevalillo-Herraez, M., Marco-Gimenez, L., & Arnau, D. (2018) · IEEE Revista Iberoamericana de Tecnologias del Aprendizaje · doi:10.1109/rita.2018.2831760 · Cited by 16
Transient localist representations in critical branching networks
Rodny, J. J., Shea, T. M., & Kello, C. T. (2017) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2016.1242760 · Cited by 4
Metadata
- Authors
- White, B. W., & Rosenblatt, F.
- Venue
- The American Journal of Psychology
- Publisher
- University of Illinois Press
- Published
- 1963-12
- Volume
- 76
- Issue
- 4
- Pages
- 705
- ISSN
- 0002-9556 (ISSN-L)
- OpenAlex
- W2008353316
- Cited by
- 1,792
- References
- 0
- FWCI
- 11.65
- Type
- journal article
- Language
- en
- Open access
- closed
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