[1] Niru Maheswaranathan, Alex Williams, Matthew Golub, Surya Ganguli, and David Sussillo. Universality and individuality in neural dynamics across large populations of recurrent networks. Advances in neural information processing systems, 32, 2019.
[2] Saurabh Vyas, Matthew D Golub, David Sussillo, and Krishna V Shenoy. Computation through neural population dynamics. Annual review of neuroscience, 43:249-275, 2020.
[3] Valerio Mante, David Sussillo, Krishna V Shenoy, and William T Newsome. Context-dependent computation by recurrent dynamics in prefrontal cortex. nature, 503(7474):78-84, 2013.
[4] Jing Wang, Devika Narain, Eghbal A Hosseini, and Mehrdad Jazayeri. Flexible timing by temporal scaling of cortical responses. Nature neuroscience, 21:102-110, 2018.
[5] Christopher J Cueva, Peter Y Wang, Matthew Chin, and Xue-Xin Wei. Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks. arXiv preprint arXiv:1912.10189, 2019.
[6] Abigail A Russo, Ramin Khajeh, Sean R Bittner, Sean M Perkins, John P Cunningham, Laurence F Abbott, and Mark M Churchland. Neural trajectories in the supplementary motor area and motor cortex exhibit distinct geometries, compatible with different classes of computation. Neuron, 107(4):745-758, 2020.
[7] Alexis Dubreuil, Adrian Valente, Manuel Beiran, Francesca Mastrogiuseppe, and Srdjan Ostojic. The role of population structure in computations through neural dynamics. Nature neuroscience, 25(6):783-794, 2022.
[8] Matthew G Perich, Charlotte Arlt, Sofia Soares, Megan E Young, Clayton P Mosher, Juri Minxha, Eugene Carter, Ueli Rutishauser, Peter H Rudebeck, Christopher D Harvey, et al. Inferring brain-wide interactions using data-constrained recurrent neural network models. BioRxiv, pages 2020-12, 2020.
[9] Feng Zhu, Andrew Sedler, Harrison A Grier, Nauman Ahad, Mark Davenport, Matthew Kaufman, Andrea Giovannucci, and Chethan Pandarinath. Deep inference of latent dynamics with spatio-temporal superresolution using selective backpropagation through time. Advances in Neural Information Processing Systems, 34:2331-2345, 2021.
[10] Adrian Valente, Jonathan W Pillow, and Srdjan Ostoic. Extracting computational mechanisms from neural data using low-rank rnns. Advances in Neural Information Processing Systems, 35:24072-24086, 2022.
[11] Justin Jude, Matthew G Perich, Lee E Miller, and Matthias H Hennig. Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation. arXiv preprint arXiv:2202.06159, 2022.
[12] Lea Duncker, Laura Driscoll, Krishna V Shenoy, Maneesh Sahani, and David Sussillo. Organizing recurrent network dynamics by task-computation to enable continual learning. Advances in neural information processing systems, 33:14387-14397, 2020.
[13] Harsha Gurnani and N Alex Cayco-Gajic. Signatures of task learning in neural representations. Current Opinion in Neurobiology, 83:102759, 2023.
[14] Friedrich Schuessler, Francesca Mastrogiuseppe, Alexis Dubreuil, Srdjan Ostojic, and Omri Barak. The interplay between randomness and structure during learning in rnns. Advances in neural information processing systems, 33:13352-13362, 2020.
[15] Francesca Mastrogiuseppe and Srdjan Ostojic. Linking connectivity, dynamics, and computations in low-rank recurrent neural networks. Neuron, 99(3):609-623, 2018.
[16] Matthew G Perich, Juan A Gallego, and Lee E Miller. A neural population mechanism for rapid learning. Neuron, 100(4):964-976, 2018.
[17] Barbara Feulner, Matthew G Perich, Raeed H Chowdhury, Lee E Miller, Juan A Gallego, and Claudia Clopath. Small, correlated changes in synaptic connectivity may facilitate rapid motor learning. Nature communications, 13(1):5163, 2022.
[18] Peter C Humphreys, Kayvon Daie, Karel Svoboda, Matthew Botvinick, and Timothy P Lillicrap. Bci learning phenomena can be explained by gradient-based optimization. bioRxiv, pages 2022-12, 2022.