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Backpropagation has long been criticized for being biologically implausible due to its reliance on concepts that are not viable in natural learning processes.
Minsky, M.: Steps toward Artificial Intelligence. Proceedings of the IRE 49
1961
Earlier work this paper cites.
Eccles, J.: From electrical to chemical transmission in the central nervous system. Notes and Records of the Royal Society of London 30
1976
Earlier work this paper cites.
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323
1986
Earlier work this paper cites.
Grossberg, S.: Competitive learning: From interactive activation to adaptive resonance. Cognitive Science 11
1987
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86
1998
Earlier work this paper cites.
Rao, R.P.N., Ballard, D.H.: Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2
1999
Earlier work this paper cites.
Friston, K.: A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences 360
2005
Earlier work this paper cites.
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., Reis, J.: Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems 47
2009
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
2009
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR (2015)
2015
Earlier work this paper cites.
Lee, D.H., Zhang, S., Fischer, A., Bengio, Y.: Difference target propagation. In: Machine Learning and Knowledge Discovery in Databases, pp. 498–515, Lecture Notes in Computer Science (2015), ISBN 978-3-319-23528-8
2015
Earlier work this paper cites.
Nugteren, C., Codreanu, V.: CLTune: A generic auto-tuner for OpenCL kernels. In: 2015 IEEE 9th International Symposium on Embedded Multicore/Many-core Systems-on-Chip, pp. 195–202 (2015)
2015
Earlier work this paper cites.
Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: Advances in Neural Information Processing Systems, vol. 28, Curran Associates, Inc. (2015)
2015
Earlier work this paper cites.
Zeki, S.: A massively asynchronous, parallel brain. Philosophical Transactions of the Royal Society B: Biological Sciences (2015)
2015
Cited alongside, same era.
2016
Cited alongside, same era.
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)
2016
Cited alongside, same era.
Nøkland, A.: Direct feedback alignment provides learning in deep neural networks. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 1045–1053 (2016)
2016
Cited alongside, same era.
Ororbia, A.G., Mali, A.: Biologically motivated algorithms for propagating local target representations. Proceedings of the AAAI Conference on Artificial Intelligence 33
2019
Later among the works it cites.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32, Curran Associates, Inc. (2019)
2019
Later among the works it cites.
Tang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., Gao, B., Qian, H., Bi, G., Song, S., Yang, J.J., Wu, H.: Bridging biological and artificial neural networks with emerging neuromorphic devices: Fundamentals, progress, and challenges. Advanced Materials 31
2019
Later among the works it cites.
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2017
Cited alongside, same era.
Czarnecki, W.M., Świrszcz, G., Jaderberg, M., Osindero, S., Vinyals, O., Kavukcuoglu, K.: Understanding synthetic gradients and decoupled neural interfaces. In: International Conference on Machine Learning, pp. 904–912 (2017)
2017
Cited alongside, same era.
Jaderberg, M., Czarnecki, W.M., Osindero, S., Vinyals, O., Graves, A., Silver, D., Kavukcuoglu, K.: Decoupled neural interfaces using synthetic gradients. In: International Conference on Machine Learning, pp. 1627–1635 (2017)
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
2017
Cited alongside, same era.
Whittington, J.C.R., Bogacz, R.: An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity. Neural Computation 29
2017
Cited alongside, same era.
Mostafa, H., Ramesh, V., Cauwenberghs, G.: Deep supervised learning using local errors. Frontiers in Neuroscience 12
2018
Cited alongside, same era.
Crafton, B., West, M., Basnet, P., Vogel, E., Raychowdhury, A.: Local learning in RRAM neural networks with sparse direct feedback alignment. In: International Symposium on Low Power Electronics and Design (2019)
2019
Cited alongside, same era.
Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, D., Chen, M., Lee, H., Ngiam, J., Le, Q.V., Wu, Y., Chen, z.: GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
2019
Cited alongside, same era.
Belilovsky, E., Eickenberg, M., Oyallon, E.: Decoupled greedy learning of cnns. In: Proceedings of the 37th International Conference on Machine Learning, pp. 736–745, PMLR (2020)
2020
Later among the works it cites.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale (2020)
2020
Later among the works it cites.
Frenkel, C., Lefebvre, M., Bol, D.: Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks. Frontiers in Neuroscience 15
2021
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Hinton, G.: The forward-forward algorithm: Some preliminary investigations (2022), arXiv:2212.13345
2022
Later among the works it cites.
2022
Later among the works it cites.
Millidge, B., Tschantz, A., Buckley, C.L.: Predictive coding approximates backprop along arbitrary computation graphs. Neural Computation 34
2022
Later among the works it cites.
Ororbia, A., Mali, A.A.: The Predictive Forward-Forward Algorithm. Proceedings of the Annual Meeting of the Cognitive Science Society 45
2023
Closest in time.
Lillicrap, T.P., Cownden, D., Tweed, D.B., Akerman, C.J.: Random synaptic feedback weights support error backpropagation for deep learning. Nature Communications 7
2041
Closest in time.