[19] Ildefons Magrans de Abril, Junichiro Yoshimoto, and Kenji Doya. Connectivity inference from neural recording data: Challenges, mathematical bases and research directions. Neural Networks, 102:120-137, 2018.
[20] Lea Duncker and Maneesh Sahani. Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings. Current opinion in neurobiology, 70:163-170, 2021.
[21] Adil G Khan, Jasper Poort, Angus Chadwick, Antonin Blot, Maneesh Sahani, Thomas D Mrsic-Flogel, and Sonja B Hofer. Distinct learning-induced changes in stimulus selectivity and interactions of gabaergic interneuron classes in visual cortex. Nature neuroscience, 21(6):851-859, 2018.
[22] Angus Chadwick, Adil G Khan, Jasper Poort, Antonin Blot, Sonja B Hofer, Thomas D Mrsic-Flogel, and Maneesh Sahani. Learning shapes cortical dynamics to enhance integration of relevant sensory input. Neuron, 111(1):106-120, 2023.
[23] 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.
[24] Ian Stevenson and Konrad Koerding. Inferring spike-timing-dependent plasticity from spike train data. 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.
[25] Scott Linderman, Christopher H Stock, and Ryan P Adams. A framework for studying synaptic plasticity with neural spike train data. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 27. Curran Associates, Inc., 2014.
[26] Andrew M Saxe, James L McClelland, and Surya Ganguli. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv:1312.6120, 2013.
[27] Tamara G Kolda and Brett W Bader. Tensor decompositions and applications. SIAM review, 51(3):455-500, 2009.
[28] Alex H Williams, Tony Hyun Kim, Forea Wang, Saurabh Vyas, Stephen I Ryu, Krishna V Shenoy, Mark Schnitzer, Tamara G Kolda, and Surya Ganguli. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron, 98(6):1099-1115, 2018.
[29] Hugo Soulat, Sepiedeh Keshavarzi, Troy Margrie, and Maneesh Sahani. Probabilistic tensor decomposition of neural population spiking activity. Advances in Neural Information Processing Systems, 34:15969-15980, 2021.
[30] Arthur Pellegrino, Heike Stein, and N Alex Cayco-Gajic. Disentangling mixed classes of covariability in large-scale neural data. bioRxiv, pages 2023-03, 2023.
[31] Rishidev Chaudhuri, Berk Gerçek, Biraj Pandey, Adrien Peyrache, and Ila Fiete. The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nature neuroscience, 22(9):1512-1520, 2019.
[32] Richard J Gardner, Erik Hermansen, Marius Pachitariu, Yoram Burak, Nils A Baas, Benjamin A Dunn, May-Britt Moser, and Edvard I Moser. Toroidal topology of population activity in grid cells. Nature, 602(7895):123-128, 2022.
[33] Manuel Beiran, Nicolas Meirhaeghe, Hansem Sohn, Mehrdad Jazayeri, and Srdjan Ostojic. Parametric control of flexible timing through low-dimensional neural manifolds. Neuron, 2023.
[34] Xulu Sun, Daniel J O'Shea, Matthew D Golub, Eric M Trautmann, Saurabh Vyas, Stephen I Ryu, and Krishna V Shenoy. Cortical preparatory activity indexes learned motor memories. Nature, 602(7896):274-279, 2022.
[35] Darby M Losey, Jay A Hennig, Emily R Oby, Matthew D Golub, Patrick T Sadlter, Kristin M Quick, Stephen I Ryu, Elizabeth C Tyler-Kabara, Aaron P Batista, Byron M Yu, et al. Learning alters neural activity to simultaneously support memory and action. bioRxiv, pages 2022–07, 2022.
[36] Robert J Van Beers, Patrick Haggard, and Daniel M Wolpert. The role of execution noise in movement variability. Journal of neurophysiology, 91(2):1050-1063, 2004.
[37] L Bittner. Ls pontryagin, vg boltyanskii, rv gamkrelidze, ef mishechenko, the mathematical theory of optimal processes. viii+ 360 s. new york/london 1962. john wiley & sons. preis  $90/-$ , 1963.
[38] Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. Neural ordinary differential equations. Advances in neural information processing systems, 31, 2018.
[39] Balasubrahmanyan Srinivasan, Upadrasta Ravikirana Prasad, and Nalam Jaganmohan Rao. Back propagation through adjoints for the identification of nonlinear dynamic systems using recurrent neural models. IEEE Transactions on Neural Networks, 5(2):213-228, 1994.