% Key papers for Low-rank and diffusion neural dynamics (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2017_gaussian_56337,
  title = {{Gaussian process based nonlinear latent structure discovery in multivariate spike train data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9133},
}

@inproceedings{neurips2016_bayesian_55598,
  title = {{Bayesian latent structure discovery from multi-neuron recordings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7363},
}

@inproceedings{neurips2018_temporal_57679,
  title = {{Temporal alignment and latent Gaussian process factor inference in population spike trains}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11988},
}

@inproceedings{neurips2016_linear_55853,
  title = {{Linear dynamical neural population models through nonlinear embeddings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7337},
}

@inproceedings{icml2016_learning_35884,
  title = {{Learning Population-Level Diffusions with Generative RNNs}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/hashimoto16.html},
}

@inproceedings{iclr2018_synthesizing_28658,
  title = {{Synthesizing realistic neural population activity patterns using Generative Adversarial Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/143},
}

@inproceedings{neurips2018_scaling_57609,
  title = {{Scaling the Poisson GLM to massive neural datasets through polynomial approximations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11353},
}

@inproceedings{neurips2019_enabling_58212,
  title = {{Enabling hyperparameter optimization in sequential autoencoders for spiking neural data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14478},
}

@inproceedings{icml2021_inferring_39465,
  title = {{Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9219},
}

@inproceedings{neurips2017_extracting_56305,
  title = {{Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9344},
}

@inproceedings{neurips2020_identifying_60042,
  title = {{Identifying signal and noise structure in neural population activity with Gaussian process factor models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18644},
}

@inproceedings{aaai2017_differentiating_103946,
  title = {{Differentiating Between Posed and Spontaneous Expressions with Latent Regression Bayesian Network}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11225},
}

@inproceedings{iclr2017_online_28245,
  title = {{Online Bayesian Transfer Learning for Sequential Data Modeling}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2017},
  url = {https://openreview.net/forum?id=ByqiJIqxg},
}

@inproceedings{neurips2020_non_60436,
  title = {{Non-reversible Gaussian processes for identifying latent dynamical structure in neural data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19001},
}

@inproceedings{neurips2022_extracting_64691,
  title = {{Extracting computational mechanisms from neural data using low-rank RNNs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54761},
}

@inproceedings{icml2025_flow_47738,
  title = {{Flow-field inference from neural data using deep recurrent networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44862},
}

@inproceedings{neurips2020_a_59296,
  title = {{A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18658},
}

@inproceedings{iclr2023_understanding_31943,
  title = {{Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11197},
}

@inproceedings{neurips2021_deep_62538,
  title = {{Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26350},
}

@inproceedings{neurips2021_scalable_62575,
  title = {{Scalable Bayesian GPFA with automatic relevance determination and discrete noise models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28289},
}

@inproceedings{neurips2024_inferring_71040,
  title = {{Inferring stochastic low-rank recurrent neural networks from neural data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96170},
}

@inproceedings{neurips2024_latent_72762,
  title = {{Latent Diffusion for Neural Spiking Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94632},
}

@inproceedings{iclr2024_multi_34702,
  title = {{Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18372},
}

@inproceedings{neurips2025_high_78212,
  title = {{High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=cGks3s79hW},
}

@inproceedings{neurips2025_slow_76418,
  title = {{Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=J0SbYYY0po},
}

@inproceedings{iclr2026_coupled_88591,
  title = {{Coupled Transformer Autoencoder for Disentangling Multi-Region Neural Latent Dynamics}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=oeoCgcYIyf},
}

@inproceedings{iclr2024_bayesian_34683,
  title = {{Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18391},
}

@inproceedings{icml2024_spike_45247,
  title = {{Spike Distance Function as a Learning Objective for Spike Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33205},
}

@inproceedings{icml2026_identifying_50289,
  title = {{Identifying Connectivity Distributions from Neural Dynamics Using Flows}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=AuiUlu3OmT},
}

@inproceedings{icml2026_a_51903,
  title = {{A Factorized Low-Rank RNN Framework for Uncovering Independent Neural Latent Dynamics and Connectivity}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=QvIbmX9jBr},
}

@inproceedings{neurips2024_neural_71452,
  title = {{Neural Embeddings Rank: Aligning 3D latent dynamics with movements}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95804},
}

@inproceedings{neurips2025_dynamical_78889,
  title = {{Dynamical modeling of nonlinear latent factors in multiscale neural activity with real-time inference}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=jOHgjZaGqd},
}

@inproceedings{neurips2025_scalable_79761,
  title = {{Scalable inference of functional neural connectivity at submillisecond timescales}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=shMjc5zvqL},
}

@inproceedings{neurips2025_extracting_79986,
  title = {{Extracting task-relevant preserved dynamics from contrastive aligned neural recordings}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=uvTea5Rfek},
}

@inproceedings{neurips2025_error_80226,
  title = {{Error Forcing in Recurrent Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=xVI8g50Qfk},
}

@inproceedings{iclr2025_discovering_80645,
  title = {{Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31122},
}

@inproceedings{neurips2026_uncovering_95179,
  title = {{Uncovering the latent structure of interwoven population and temporal codes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/152608},
}

@inproceedings{iclr2025_netformer_82696,
  title = {{NetFormer: An interpretable model for recovering dynamical connectivity in neuronal population dynamics}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29102},
}

@inproceedings{neurips2026_predictive_89592,
  title = {{Predictive Representation Learning for Partially Observed Neural Dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/151732},
}