[53] 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". In: Nature methods 15.10 (2018), pp. 805–815.
[54] 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". In: arXiv preprint arXiv:2109.04463 (2021).
[55] Omid G Sani, Hamidreza Abbaspourazad, Yan T Wong, Biban Pesaran, and Maryam M Shanechi. "Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification". In: Nature Neuroscience 24.1 (2021), pp. 140-149.
[56] Omid G Sani, Bijan Pesaran, and Maryam M Shanechi. "Where is all the nonlinearity: flexible nonlinear modeling of behaviorally relevant neural dynamics using recurrent neural networks". In: bioRxiv (2021).
[57] Marine Schimel, Ta-Chu Kao, Kristopher T Jensen, and Guillaume Hennequin. "iLQR-VAE: control-based learning of input-driven dynamics with applications to neural data". In: bioRxiv (2021).
[58] Steffen Schneider, Jin Hwa Lee, and Mackenzie Weygandt Mathis. "Learnable latent embeddings for joint behavioural and neural analysis". In: Nature (2023), pp. 1-9.
[59] Felix Schwock, Julien Bloch, Les Atlas, Shima Abadi, and Azadeh Yazdan-Shahmorad. "Estimating and Analyzing Neural Information flow using Signal Processing on Graphs". In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2023, pp. 1-5.
[60] Qi She and Anqi Wu. "Neural dynamics discovery via gaussian process recurrent neural networks". In: Uncertainty in Artificial Intelligence. PMLR. 2020, pp. 454-464.
[61] Le Song, Mladen Kolar, and Eric Xing. "Time-varying dynamic bayesian networks". In: Advances in neural information processing systems 22 (2009).
[62] Tengfei Song, Wenming Zheng, Peng Song, and Zhen Cui. "EEG emotion recognition using dynamical graph convolutional neural networks". In: IEEE Transactions on Affective Computing 11.3 (2018), pp. 532-541.
[63] Frieder Stolzenburg, Sandra Litz, Olivia Michael, and Oliver Obst. "The Power of Linear Recurrent Neural Networks". In: arXiv preprint arXiv:1802.03308 (2018).
[64] David Sussillo, Mark M Churchland, Matthew T Kaufman, and Krishna V Shenoy. “A neural network that finds a naturalistic solution for the production of muscle activity”. In: Nature neuroscience 18.7 (2015), pp. 1025–1033.
[65] David Sussillo, Rafal Jozefowicz, LF Abbott, and Chethan Pandarinath. "Lfads-latent factor analysis via dynamical systems". In: arXiv preprint arXiv:1608.06315 (2016).
[66] Siyi Tang, Jared A Dunnmon, Liangqiong Qu, Khaled K Saab, Christopher Lee-Messer, and Daniel L Rubin. "Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models". In: arXiv preprint arXiv:2211.11176 (2022).
[67] Michael Trumpis, Chia-Han Chiang, Amy L Orsborn, Brinnae Bent, Jinghua Li, John A Rogers, Bijan Pesaran, Gregory Cogan, and Jonathan Viventi. "Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human  $\mu$ ECoG". In: Journal of neural engineering 18.3 (2021), p. 036011.
[68] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. "Attention is all you need". In: Advances in neural information processing systems 30 (2017).
[69] Giuseppe Vinci, Valérie Ventura, Matthew A Smith, and Robert E Kass. "Adjusted regularization in latent graphical models: Application to multiple-neuron spike count data". In: The annals of applied statistics 12.2 (2018), p. 1068.
[70] Saurabh Vyas, Matthew D Golub, David Sussillo, and Krishna V Shenoy. "Computation through neural population dynamics". In: Annual review of neuroscience 43 (2020), pp. 249-275.
[71] Simon Wein, Alina Schüller, Ana Maria Tomé, Wilhelm M Malloni, Mark W Greenlee, and Elmar W Lang. "Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures". In: Network Neuroscience 6.3 (2022), pp. 665-701.