[33] A Artola, S Brocher, and W Singer. Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex. Nature, 347(6288): 69-72, 1990. doi: 10.1038/347069a0.
[34] Sukbin Lim, Jillian L McKee, Luke Woloszyn, Yali Amit, David J Freedman, David L Sheinberg, and Nicolas Brunel. Inferring learning rules from distributions of firing rates in cortical neurons. Nature neuroscience, 18(12):1804-1810, 2015. doi: 10.1038/nn.4158.
[35] E Oja. A simplified neuron model as a principal component analyzer. Journal of mathematical biology, 15(3):267-273, 1982. doi: 10.1007/bf00275687.
[36] E L Bienenstock, L N Cooper, and P W Munro. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. The Journal of neuroscience, 2(1):32-48, 1982. doi: 10.1523/JNEUROSCI.02-01-00032.1982.
[37] Blake A Richards and Timothy P Lillicrap. Dendritic solutions to the credit assignment problem. Current opinion in neurobiology, 54:28-36, 2019. doi: 10.1016/j.conb.2018.08.003.
[38] Axel Laborieux and Friedemann Zenke. Holomorphic equilibrium propagation computes exact gradients through finite size oscillations. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 12950-12963. Curran Associates, Inc., 2022.
[39] R P Rao and D H Ballard. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience, 2(1):79-87, 1999. doi: 10.1038/4580.
[40] M E Larkum, J J Zhu, and B Sakmann. A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature, 398(6725):338-341, 1999. doi: 10.1038/18686.
[41] C M Coussens, D S Kerr, and W C Abraham. Glucocorticoid receptor activation lowers the threshold for NMDA-receptor-dependent homosynaptic long-term depression in the hippocampus through activation of voltage-dependent calcium channels. Journal of neurophysiology, 78 (1):1-9, 1997. doi: 10.1152/jn.1997.78.1.1.
[42] Shan Shen, Xiaolong Jiang, Federico Scala, Jiakun Fu, Paul Fahey, Dmitry Kobak, Zhenghuan Tan, Na Zhou, Jacob Reimer, Fabian Sinz, and Andreas S Tolias. Distinct organization of two cortico-cortical feedback pathways. Nature communications, 13(1):6389, 2022. doi: 10.1038/s41467-022-33883-9.
[43] Andreas J Keller, Morgane M Roth, and Massimo Scanziani. Feedback generates a second receptive field in neurons of the visual cortex. Nature, 582(7813):545-549, 2020. doi: 10.1038/s41586-020-2319-4.
[44] Edward Zagha, Amanda E Casale, Robert N S Sachdev, Matthew J McGinley, and David A McCormick. Motor cortex feedback influences sensory processing by modulating network state. Neuron, 79(3):567-578, 2013. doi: 10.1016/j.neuron.2013.06.008.
[45] Aditya Gilra and Wulfram Gerstner. Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network. eLife, 6:e28295, 2017. doi: 10.7554/eLife.28295.
[46] Alireza Alemi, Christian Machens, Sophie Deneve, and Jean-Jacques Slotine. Learning Nonlinear Dynamics in Efficient, Balanced Spiking Networks Using Local Plasticity Rules. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 2018. doi: 10.1609/aaai.v32i1.11320.
[47] Alexander Meulemans, Matilde Tristany Farinha, Maria R. Cervera, João Sacramento, and Benjamin F. Grewe. Minimizing control for credit assignment with strong feedback. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 15458-15483. PMLR, 2022.