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Science // https://www.researchgate.net/publication/221605378_Learning_While_Searching_in_Constraint-Satisfaction-Problems

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Kelleher J. D. (2019). Deep Learning. MIT Press // https://books.google.ru/books?id=ZU6qDwAAQBAJ

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Kharlamov A., Pilgun M. (2020). Neuroinformatics and Semantic Representations: Theory and Applications. Cambridge Scholars Publishing // https://books.google.ru/books?id=jEfhDwAAQBAJ

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Jain V. K. (2019). Machine Learning. Khanna Publishing House // https://books.google.ru/books?id=c6YEEAAAQBAJ

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Aizenberg I. N., Aizenberg N. N., Vandewalle J. (2000). Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer Science & Business Media // https://books.google.ru/books?id=g9LlraAp2-8C

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Dertouzos M. L. (1965). Threshold Logic: A Synthesis Approach. M.I.T. Press // https://books.google.ru/books?id=u2ZqQgAACAAJ

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Muroga S. (1971). Threshold Logic and Its Applications. John Wiley & Sons, New York // https://books.google.ru/books?id=wvtQAAAAMAAJ

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Hinton G. E., Salakhutdinov R. R. (2006). Reducing the Dimensionality of Data with Neural Networks / Science. — 2006-07-28. — Vol. 313, iss. 5786, pp. 504–507 // https://dx.doi.org/10.1126%2Fscience.1127647

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Hinton G. E., Osindero S. (2006). A fast learning algorithm for deep belief nets // http://www.cs.toronto.edu/~hinton/absps/fastnc.pdf

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Horita T., Murata T., Takanami I. (2006). A Multiple-Weight-and-Neuron-Fault Tolerant Digital Multilayer Neural Network / 2006 21st IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems // https://doi.org/10.1109/DFT.2006.8

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LeCun Y., Bengio Y., Hinton G. (2015). Deep learning / Nature, Vol. 521, pp. 436—444 // https://doi.org/10.1038/nature14539

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Raymond E. S., Steele G. L. (1996). The New Hacker's Dictionary. MIT Press // https://books.google.ru/books?id=g80P_4v4QbIC

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Levy S. (2010). Hackers: Heroes of the Computer Revolution. 25th Anniversary Edition. O'Reilly Media, Inc // https://books.google.ru/books?id=mShXzzKtpmEC

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Rosenblatt F. (1961). Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. Cornell aeronautical lab inc., Buffalo, New York. Defense Technical Information Center // https://books.google.ru/books?id=Tk2tDAEACAAJ

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Pater J. (2017). Did Frank Rosenblatt invent deep learning in 1962? // https://blogs.umass.edu/comphon/2017/06/15/did-frank-rosenblatt-invent-deep-learning-in-1962/

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Rosenblatt F. (1964). Analytic Techniques for the Study of Neural Nets / IEEE Transactions on Applications and Industry, Vol. 83(74), pp. 285–292 // https://doi.org/10.1109/tai.1964.5407758

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Rosenblatt F. (1967). Recent Work on Theoretical Models of Biological Memory / Computer and Information Sciences, Vol. 2, pp. 33—56 // https://blogs.umass.edu/brain-wars/files/2016/01/rosenblatt-1967.pdf

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Roberts P. A. (1992). Neuroanatomy. Springer-Verlag, p. 86 // https://books.google.ru/books?id=7zywoAEACAAJ

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Toro R., Perron M., Pike B., Richer L., Veillette S., Pausova Z., Paus T. (2008). Brain Size and Folding of the Human Cerebral Cortex / Cerebral Cortex, Vol. 18, Iss. 10, pp. 2352—2357 // https://doi.org/10.1093/cercor/bhm261

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Woolsey C. N., Marshall W. H., Bard P. (1942). Representation of cutaneous tactile sensibility in the cerebral cortex of the monkey as indicated by evoked potentials / Bulletin of the Johns Hopkins Hospital, Vol. 70, pp. 399—441.

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Adrian E. D. (1943). Afferent areas in the brain of ungulates / Brain, Vol. 66, Iss. 2, pp. 89—103 // https://doi.org/10.1093/brain/66.2.89

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Catania K. C. (2007). Evolution of the Somatosensory System — Clues from Specialized Species / Kaas J. S. (2007). Evolution of Nervous Systems: a comprehensive reference // https://books.google.ru/books?id=UX1PAQAAIAAJ

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Hubel D. H., Wiesel T. N. (1979). Brain Mechanisms of Vision / Scientific American, 241(3), pp. 150–162 // https://doi.org/10.1038/scientificamerican0979-150

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Minsky M., Papert S. A. (1969). Perceptrons: An Introduction to Computational Geometry. MIT Press // https://books.google.ru/books?id=KhI-uwEACAAJ

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Block H. D. (1970). A review of 'Perceptrons' / Information and Control, Vol. 17, pp. 510—522 // https://doi.org/10.1016/S0019-9958(70)90409-2

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Павлов И. П. (1923). Двадцатилетний опыт объективного изучения высшей нервной деятельности животных. Государственное издательство Москва—Петроград.

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Nicholls J. G., Martin A. R., Fuchs P. A., Brown D. A., Diamond M. E., Weisblat D. A. (2012). From Neuron to Brain. Sinauer // https://books.google.ru/books?id=eTLzXwAACAAJ

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Erling N. (2016). Nobel Prizes And Notable Discoveries. World Scientific // https://books.google.ru/books?id=IU4tDQAAQBAJ

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Nicholls J. G., Martin A. R., Fuchs P. A., Brown D. A., Diamond M. E., Weisblat D. A. (2012). From Neuron to Brain. Sinauer // https://books.google.ru/books?id=eTLzXwAACAAJ

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Цит. по: Николлс Д., Мартин Р., Валлас Б., Фукс П. (2017). От нейрона к мозгу / изд. 4-е. — М.: УРРС: Книжный дом «Либерком».

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Hubel D. H., Wiesel T. N. (1959). Receptive fields of single neurons in the cat's striate cortex / Journal of Physiology, 1959, vol. 148, pp. 574—591 // https://doi.org/10.1113/jphysiol.1959.sp006308

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Rosenblatt F. (1961). Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. Cornell aeronautical lab inc., Buffalo, New York. Defense Technical Information Center // https://books.google.ru/books?id=Tk2tDAEACAAJ

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Contributors (1983) / IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-13, No. 5, September/October 1983

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Fukushima K. (1975). Cognitron: A self-organizing multilayered neural network / Biological Cybernetics, 20(3-4), 121–136 // https://doi.org/10.1007/bf00342633

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Draelos R. (2019). Convolution vs. Cross-Correlation / GLASS BOX: Machine Learning and Medicine, by Rachel Lea Ballantyne Draelos, July 26, 2019 // https://glassboxmedicine.com/2019/07/26/convolution-vs-cross-correlation/

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Rosebrock A. (2021). Convolution and cross-correlation in neural networks / pyimagesearch, May 14, 2021 // https://www.pyimagesearch.com/2021/05/14/convolution-and-cross-correlation-in-neural-networks/

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Yann LeCun (2018) / Heidelberg Laureate Forum // https://www.heidelberg-laureate-forum.org/laureate/yann-lecun.html

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Lecun Y. Fun stuff / Yann LeCun home page // http://yann.lecun.com/ex/fun/

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