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We propose a new learning framework, signal propagation (sigprop), for propagating a learning signal and updating neural network parameters via a forward pass, as an alternative to backpropagation.
J. Backus, “Can programming be liberated from the von neumann style? a functional style and its algebra of programs,” Communications of the ACM , vol. 21, no. 8, pp. 613–641, 1978
1978
Earlier work this paper cites.
J. J. Hopfield, “Neurons with graded response have collective computational properties like those of two-state neurons,” Proceedings of the national academy of sciences , vol. 81, no. 10, pp. 3088–3092, 1984
1984
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature , vol. 323, no. 6088, p. 533, 1986
1986
Earlier work this paper cites.
S. Grossberg, “Competitive learning: From interactive activation to adaptive resonance,” Cognitive science , vol. 11, no. 1, pp. 23–63, 1987
1987
Earlier work this paper cites.
F. Crick, “The recent excitement about neural networks.” Nature , vol. 337, no. 6203, pp. 129–132, 1989
1989
Earlier work this paper cites.
R. J. Williams and D. Zipser, Gradient-based learning algorithms for recurrent connectionist networks . Citeseer, 1990
1990
Earlier work this paper cites.
A. P. Heinz, “Pipelined neural tree learning by error forward-propagation,” in Proceedings of ICNN’95-International Conference on Neural Networks , vol. 1. IEEE, 1995, pp. 394–397
1995
Earlier work this paper cites.
K. Hirasawa, M. Ohbayashi, M. Koga, and M. Harada, “Forward propagation universal learning network,” in Proceedings of International Conference on Neural Networks (ICNN’96) , vol. 1. IEEE, 1996, pp. 353–358
1996
Earlier work this paper cites.
Y. LeCun, “The mnist database of handwritten digits,” http://yann. lecun. com/exdb/mnist/ , 1998
1998
Earlier work this paper cites.
N. R. Mahapatra and B. Venkatrao, “The processor-memory bottleneck: problems and solutions,” Crossroads , vol. 5, no. 3es, p. 2, 1999
1999
Earlier work this paper cites.
X. Xie and H. S. Seung, “Equivalence of backpropagation and contrastive hebbian learning in a layered network,” Neural computation , vol. 15, no. 2, pp. 441–454, 2003
2003
Earlier work this paper cites.
Y. Ohama, N. Fukumura, and Y. Uno, “A forward-propagation rule for acquiring neural inverse models using a rls algorithm,” in International Conference on Neural Information Processing . Springer, 2004, pp. 585–591
2004
Earlier work this paper cites.
——, “A forward-propagation learning rule for neural inverse models using a method of recursive least squares,” Systems and Computers in Japan , vol. 36, no. 8, pp. 71–80, 2005
2005
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” 2011
2011
Earlier work this paper cites.
A. Mohemmed, S. Schliebs, S. Matsuda, and N. Kasabov, “Span: Spike pattern association neuron for learning spatio-temporal spike patterns,” International journal of neural systems , vol. 22, no. 04, p. 1250012, 2012
2012
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. icml , vol. 30, no. 1. Citeseer, 2013, p. 3
2013
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
M. Horowitz, “1.1 computing’s energy problem (and what we can do about it),” in 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) . IEEE, 2014, pp. 10–14
2014
Cited alongside, same era.
M. Jaderberg, W. M. Czarnecki, S. Osindero, O. Vinyals, A. Graves, D. Silver, and K. Kavukcuoglu, “Decoupled neural interfaces using synthetic gradients,” in International Conference on Machine Learning . PMLR, 2017, pp. 1627–1635
2017
Later among the works it cites.
J. Guerguiev, T. P. Lillicrap, and B. A. Richards, “Towards deep learning with segregated dendrites,” eLife , vol. 6, 2017
2017
Later among the works it cites.
W. M. Czarnecki, G. Świrszcz, M. Jaderberg, S. Osindero, O. Vinyals, and K. Kavukcuoglu, “Understanding synthetic gradients and decoupled neural interfaces,” in International Conference on Machine Learning . PMLR, 2017, pp. 904–912
2017
Later among the works it cites.
2017
Later among the works it cites.
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D.-H. Lee, S. Zhang, A. Fischer, and Y. Bengio, “Difference target propagation,” in Joint european conference on machine learning and knowledge discovery in databases . Springer, 2015, pp. 498–515
2015
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning . PMLR, 2015, pp. 448–456
2015
Cited alongside, same era.
Y. Cao, Y. Chen, and D. Khosla, “Spiking deep convolutional neural networks for energy-efficient object recognition,” International Journal of Computer Vision , vol. 113, no. 1, pp. 54–66, 2015
2015
Cited alongside, same era.
P. U. Diehl, D. Neil, J. Binas, M. Cook, S.-C. Liu, and M. Pfeiffer, “Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing,” in 2015 International joint conference on neural networks (IJCNN) . ieee, 2015, pp. 1–8
2015
Cited alongside, same era.
A. H. Marblestone, G. Wayne, and K. P. Kording, “Toward an integration of deep learning and neuroscience,” Frontiers in computational neuroscience , vol. 10, p. 94, 2016
2016
Cited alongside, same era.
J. H. Lee, T. Delbruck, and M. Pfeiffer, “Training deep spiking neural networks using backpropagation,” Frontiers in neuroscience , vol. 10, p. 508, 2016
2016
Cited alongside, same era.
T. P. Lillicrap, D. Cownden, D. B. Tweed, and C. J. Akerman, “Random synaptic feedback weights support error backpropagation for deep learning,” Nature communications , vol. 7, p. 13276, 2016
2016
Cited alongside, same era.
Q. Liao, J. Z. Leibo, and T. A. Poggio, “How important is weight symmetry in backpropagation?” in AAAI , 2016, pp. 1837–1844
2016
Cited alongside, same era.
Y. Ohama and T. Yoshimura, “A parallel forward-backward propagation learning scheme for auto-encoders,” in International Conference on Neural Information Processing . Springer, 2017, pp. 126–136
2017
Later among the works it cites.
2017
Later among the works it cites.
B. Rueckauer, I.-A. Lungu, Y. Hu, M. Pfeiffer, and S.-C. Liu, “Conversion of continuous-valued deep networks to efficient event-driven networks for image classification,” Frontiers in neuroscience , vol. 11, p. 682, 2017
2017
Later among the works it cites.
S. Yin, S. K. Venkataramanaiah, G. K. Chen, R. Krishnamurthy, Y. Cao, C. Chakrabarti, and J.-s. Seo, “Algorithm and hardware design of discrete-time spiking neural networks based on back propagation with binary activations,” in 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) . IEEE, 2017, pp. 1–5
2017
Later among the works it cites.
B. Scellier, A. Goyal, J. Binas, T. Mesnard, and Y. Bengio, “Extending the framework of equilibrium propagation to general dynamics,” 2018
2018
Later among the works it cites.
M. Bouvier, A. Valentian, T. Mesquida, F. Rummens, M. Reyboz, E. Vianello, and E. Beigne, “Spiking neural networks hardware implementations and challenges: A survey,” ACM Journal on Emerging Technologies in Computing Systems (JETC) , vol. 15, no. 2, pp. 1–35, 2019
2019
Later among the works it cites.
A. Nøkland and L. H. Eidnes, “Training neural networks with local error signals,” in International Conference on Machine Learning . PMLR, 2019, pp. 4839–4850
2019
Later among the works it cites.
E. Belilovsky, M. Eickenberg, and E. Oyallon, “Decoupled greedy learning of cnns,” in International Conference on Machine Learning . PMLR, 2020, pp. 736–745
2020
Later among the works it cites.
J. Kaiser, H. Mostafa, and E. Neftci, “Synaptic plasticity dynamics for deep continuous local learning (decolle),” Frontiers in Neuroscience , vol. 14, p. 424, 2020
2020
Later among the works it cites.
S. R. Kheradpisheh and T. Masquelier, “Temporal backpropagation for spiking neural networks with one spike per neuron,” International Journal of Neural Systems , vol. 30, no. 06, p. 2050027, 2020
2020
Later among the works it cites.
2021
Later among the works it cites.