Copeland, B. Jack, and Diane Proudfoot. “Alan Turing’s forgotten ideas in computer science.” Scientific American 280.4 (1999): 98-103.
Hopfield, John J. “Neural networks and physical systems with emergent collective computational abilities.” Proceedings of the National Academy of Sciences 79.8 (1982): 2554-2558.
Hopfield, John J. “Neurons with graded response have collective computational properties like those of two-state neurons.” Proceedings of the National Academy of Sciences81.10 (1984): 3088-3092.
Hopfield, John J., and David W. Tank. ““Neural” computation of decisions in optimization problems.” Biological cybernetics52.3 (1985): 141-152.
Kohonen, Teuvo. “Essentials of the self-organizing map.” Neural networks 37 (2013): 52-65.
Backpropagation:
Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. Learning internal representations by error propagation. No. ICS-8506. California Univ San Diego La Jolla Inst for Cognitive Science, 1985.
Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. “Learning representations by back-propagating errors.” nature 323.6088 (1986): 533.
Capsule Networks:
Sabour, Sara, Nicholas Frosst, and Geoffrey E. Hinton. “Dynamic routing between capsules.” Advances in Neural Information Processing Systems. 2017.
Hinton, Geoffrey, Nicholas Frosst, and Sara Sabour. “Matrix capsules with EM routing.” (2018).
Convolutional Networks:
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
Zeiler, Matthew D., and Rob Fergus. “Visualizing and understanding convolutional networks.” European conference on computer vision. Springer, Cham, 2014.
Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).
Szegedy, Christian, et al. “Going deeper with convolutions.” Cvpr, 2015.
Schmidhuber, Jürgen. “Deep learning in neural networks: An overview.” Neural networks 61 (2015): 85-117.
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553 (2015): 436.
He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).
Generative Adversarial Networks:
Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in neural information processing systems. 2014
Spiking Neural Networks:
Maass, Wolfgang. “Networks of spiking neurons: the third generation of neural network models.” Neural networks 10.9 (1997): 1659-1671.
Diesmann, Markus, Marc-Oliver Gewaltig, and Ad Aertsen. “Stable propagation of synchronous spiking in cortical neural networks.” Nature 402.6761 (1999): 529.
Brunel, Nicolas. “Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.” Journal of computational neuroscience 8.3 (2000): 183-208.
Kempter, Richard, Wulfram Gerstner, and J. Leo Van Hemmen. “Hebbian learning and spiking neurons.” Physical Review E 59.4 (1999): 4498.
Universal Approximation Theorem:
Cybenko, George. “Approximation by superpositions of a sigmoidal function.” Mathematics of control, signals and systems 2.4 (1989): 303-314.
Hornik, Kurt, Maxwell Stinchcombe, and Halbert White. “Multilayer feedforward networks are universal approximators.” Neural networks 2.5 (1989): 359-366.
Hornik, Kurt. “Approximation capabilities of multilayer feedforward networks.” Neural networks 4.2 (1991): 251-257.