Grossberg, S. (1987). Competitive learning: From interactive activation to adaptive resonance. Cognitive Science, 11(1):23-63.
He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs]. arXiv: 1512.03385.
Helmholtz, H. v., Gullstrand, A., Kries, J. v., Nagel, W. A., and University College, L. L. S. (1909). Handbuch der Physiologischen Optik [electronic resource]. Hamburg ; Leipzig : verlag von Leopold Voss.
Kadkhodaie, Z. and Simoncelli, E. (2021). Stochastic solutions for linear inverse problems using the prior implicit in a denoiser. In Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W., editors, Advances in Neural Information Processing Systems, volume 34, pages 13242-13254. Curran Associates, Inc.
Kar, K., Kubilius, J., Schmidt, K., Issa, E. B., and DiCarlo, J. J. (2019). Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. Nat. Neurosci., 22(6):974-983.
Keller, G. B. and Mrsic-Flogel, T. D. (2018). Predictive Processing: A Canonical Cortical Computation. Neuron, 100(2):424-435.
Khaligh-Razavi, S.-M. and Kriegeskorte, N. (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput. Biol., 10(11):e1003915.
Kietzmann, T. C., Spoerer, C. J., Sorensen, L. K. A., Cichy, R. M., Hauk, O., and Kriegeskorte, N. (2019). Recurrence is required to capture the representational dynamics of the human visual system. Proc. Natl. Acad. Sci. U. S. A., 116(43):21854-21863.
Kreiman, G. and Serre, T. (2020). Beyond the feedforward sweep: feedback computations in the visual cortex. Ann. N. Y. Acad. Sci., 1464(1):222-241.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 25, pages 1097-1105. Curran Associates, Inc.
Kunin, D., Bloom, J. M., Goeva, A., and Seed, C. (2019). Loss Landscapes of Regularized Linear Autoencoders. arXiv:1901.08168 [cs, stat]. arXiv: 1901.08168.
Kunin, D., Nayebi, A., Sagastuy-Brena, J., Ganguli, S., Bloom, J., and Yamins, D. L. K. (2020). Two Routes to Scalable Credit Assignment without Weight Symmetry. arXiv:2003.01513 [cs, q-bio, stat]. arXiv: 2003.01513.
Lee, D.-H., Zhang, S., Fischer, A., and Bengio, Y. (2014). Difference Target Propagation. arXiv:1412.7525 [cs]. arXiv: 1412.7525.
Lee, T. S. and Mumford, D. (2003). Hierarchical bayesian inference in the visual cortex. JOSA A, 20(7):1434-1448.
Liao, Q., Leibo, J. Z., and Poggio, T. (2015). How important is weight symmetry in backpropagation?
Lillicrap, T. P., Cownden, D., Tweed, D. B., and Akerman, C. J. (2016). Random synaptic feedback weights support error backpropagation for deep learning. Nature Communications, 7:13276.
Lillicrap, T. P. and Santoro, A. (2019a). Backpropagation through time and the brain. Curr. Opin. Neurobiol., 55:82-89.
Lillicrap, T. P. and Santoro, A. (2019b). Backpropagation through time and the brain. *Current Opinion in Neurobiology*, 55:82-89.
Lindsay, G. W. (2021). Convolutional neural networks as a model of the visual system: Past, present, and future. J. Cogn. Neurosci., 33(10):2017-2031.
Markov, N. T., Ercsey-Ravasz, M., Essen, D. C. V., Knoblauch, K., Toroczkai, Z., and Kennedy, H. (2013). Cortical High-Density Counterstream Architectures. Science, 342(6158):1238406.