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Local Hebbian learning is believed to be inferior in performance to end-to-end training using a backpropagation algorithm.
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GitHub repository "Biological Learning", https://github.com/DimaKrotov/Biological_Learning
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See for a example a current leader board: https://benchmarks.ai/cifar-10
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D. Heeger. Perception Lecture Notes: Light/Dark Adaptation. Available: http://www.cns.nyu.edu/ david/courses/perception/lecturenotes/light-adapt/light-adapt.html
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R.Collins. Lecture 7: Correspondence Matching. Available: http://www.cse.psu.edu/ rtc12/CSE486/lecture07.pdf
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Appelhans, D., Auerbach, G., Averill, D., Black, R., Brown, A., Buono, D., Cash, R., Chen, D., Deindl, M., Duffy, D. and Eastman, G., 2018. Functionality and performance of NVLink with IBM POWER9 processors. IBM Journal of Research and Development, 62(4-5)
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Krotov, D. and Hopfield, J., 2019. Unsupervised learning by competing hidden units, Proceedings of the National Academy of Sciences, 116 (16) 7723-7731; DOI: 10.1073/pnas.1820458116
2019
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