[1] João Sacramento, Rui Ponte Costa, Yoshua Bengio, and Walter Senn. Dendritic cortical microcircuits approximate the backpropagation algorithm. In S Bengio, H Wallach, H Larochelle, K Grauman, N Cesa-Bianchi, and R Garnett, editors, Advances in Neural Information Processing Systems 31, pages 8721-8732. Curran Associates, Inc., 2018.
[2] Alexander Meulemans, Matilde Tristany Farinha, Javier Garcia Ordonez, Pau Vilimelis Aceituno, João Sacramento, and Benjamin F. Grewe. Credit assignment in neural networks through deep feedback control. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 4674-4687. Curran Associates, Inc., 2021.
[3] 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.
[4] Alexandre Payeur, Jordan Guerguiev, Friedemann Zenke, Blake A Richards, and Richard Naud. Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nature neuroscience, 2021. doi: 10.1038/s41593-021-00857-x.
[5] Will Greedy, Heng Wei Zhu, Joseph Pemberton, Jack Mellor, and Rui Ponte Costa. Single-phase deep learning in cortico-cortical networks. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 24213-24225. Curran Associates, Inc., 2022.
[6] Robert Urbanczik and Walter Senn. Learning by the dendritic prediction of somatic spiking. Neuron, 81(3):521-528, 2014. doi: 10.1016/j.neuron.2013.11.030.
[7] Jordan Guerguiev, Timothy P Lillicrap, and Blake A Richards. Towards deep learning with segregated dendrites. eLife, 6, 2017. doi: 10.7554/eLife.22901.
[8] 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.
[9] James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. JAX: composable transformations of Python+NumPy programs, 2018. URL http://github.com/google/jax.
[10] Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee. Flax: A neural network library and ecosystem for JAX, 2023. URL http://github.com/google/flax.
[11] Patrick Kidger. On Neural Differential Equations. PhD thesis, University of Oxford, 2021.
[12] TensorFlow Datasets, a collection of ready-to-use datasets. https://www.tensorflow.org/datasets.
[13] Ch Tsitouras. Runge-Kutta pairs of order 5(4) satisfying only the first column simplifying assumption. Computers & mathematics with applications, 62(2):770-775, 2011. doi: 10.1016/j.camwa.2011.06.002.
[14] Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. Optuna: A next-generation hyperparameter optimization framework. arXiv, 2019.
[15] James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for HyperParameter optimization. In J Shawe-Taylor, R Zemel, P Bartlett, F Pereira, and K Q Weinberger, editors, Advances in Neural Information Processing Systems, volume 24. Curran Associates, Inc., 2011.