[15] Byron M Yu, John P Cunningham, Gopal Santhanam, Stephen Ryu, Krishna V Shenoy, and Maneesh Sahani. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Advances in neural information processing systems, 21, 2008.
[16] Yuanjun Gao, Evan W Archer, Liam Paninski, and John P Cunningham. Linear dynamical neural population models through nonlinear embeddings. Advances in neural information processing systems, 29, 2016.
[17] Yuan Zhao and Il Memming Park. Variational latent gaussian process for recovering single-trial dynamics from population spike trains. Neural computation, 29(5):1293-1316, 2017.
[18] Scott Linderman, Matthew Johnson, Andrew Miller, Ryan Adams, David Blei, and Liam Paninski. Bayesian learning and inference in recurrent switching linear dynamical systems. In Artificial Intelligence and Statistics, pages 914–922. PMLR, 2017.
[19] Daniel Durstewitz. A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements. PLoS computational biology, 13(6):e1005542, 2017.
[20] Felix Pei, Joel Ye, David Zoltowski, Anqi Wu, Raeed H Chowdhury, Hansem Sohn, Joseph E O'Doherty, Krishna V Shenoy, Matthew T Kaufman, Mark Churchland, et al. Neural latents benchmark'21: evaluating latent variable models of neural population activity. arXiv preprint arXiv:2109.04463, 2021.
[21] Chethan Pandarinath, Daniel J O'Shea, Jasmine Collins, Rafal Jozefowicz, Sergey D Stavisky, Jonathan C Kao, Eric M Trautmann, Matthew T Kaufman, Stephen I Ryu, Leigh R Hochberg, et al. Inferring single-trial neural population dynamics using sequential auto-encoders. Nature methods, 15(10):805–815, 2018.
[22] Mohammad Reza Keshtkaran, Andrew R Sedler, Raeed H Chowdhury, Raghav Tandon, Diya Basrai, Sarah L Nguyen, Hansem Sohn, Mehrdad Jazayeri, Lee E Miller, and Chethan Pandarinath. A large-scale neural network training framework for generalized estimation of single-trial population dynamics. Nature Methods, pages 1-6, 2022.
[23] Steffen Schneider, Jin Hwa Lee, and Mackenzie Weygandt Mathis. Learnable latent embeddings for joint behavioral and neural analysis. arXiv preprint arXiv:2204.00673, 2022.
[24] Tze Hui Koh, William E Bishop, Takashi Kawashima, Brian B Jeon, Ranjani Srinivasan, Yu Mu, Ziqiang Wei, Sandra J Kuhlman, Misha B Ahrens, Steven M Chase, et al. Dimensionality reduction of calcium-imaged neuronal population activity. Nature Computational Science, 3(1):71-85, 2023.
[25] Wilson Truccolo, Uri T Eden, Matthew R Fellows, John P Donoghue, and Emery N Brown. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. Journal of neurophysiology, 93(2):1074-1089, 2005.
[26] Jonathan W Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M Litke, EJ Chichilnisky, and Eero P Simoncelli. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454(7207):995-999, 2008.
[27] Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, and Eva Dyer. Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers. Advances in Neural Information Processing Systems, 35:2377-2391, 2022.
[28] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[29] Rohan Gala, Agata Budzillo, Fahimeh Baftizadeh, Jeremy Miller, Nathan Gouwens, Anton Arkhipov, Gabe Murphy, Bosiljka Tasic, Hongkui Zeng, Michael Hawrylycz, et al. Consistent cross-modal identification of cortical neurons with coupled autoencoders. Nature computational science, 1(2):120-127, 2021.