Johnson*, R. E., Linderman*, S. W., Panier, T., Wee, C. L., Song, E., Herrera, K. J., … Engert, F. (2019). Probabilistic Models of Larval Zebrafish Behavior: Structure on Many Scales. *BioRxiv*. https://doi.org/10.1101/672246
bioRxiv
Linderman, S. W., Nichols, A. L. A., Blei, D. M., Zimmer, M., & Paninski, L. (2019). Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans. *BioRxiv*. https://doi.org/10.1101/621540
bioRxiv
Nassar, J., Linderman, S. W., Park, M., & Bugallo, M. (2019). Tree-structured locally linear dynamics model to uproot Bayesian neural data analysis. *Computational and Systems Neuroscience (Cosyne) Abstracts*.

Raju, R. V., Li, Z., Linderman, S. W., & Pitkow, X. (2019). Inferring implicit inference. *Computational and Systems Neuroscience (Cosyne) Abstracts*.

Glaser, J., Linderman, S. W., Whiteway, M., Perich, M., Dekleva, B., Miller, L., & Cunningham, L. P. J. (2019). State space models for multiple interacting neural populations. *Computational and Systems Neuroscience (Cosyne) Abstracts*.

Markowitz, J., Gillis, W., Murmann, J., Linderman, S. W., Sabatini, B., & Datta, S. (2019). Resolving the neural mechanisms of reinforcement learning through new behavioral technologies. *Computational and Systems Neuroscience (Cosyne) Abstracts*.

Linderman, S. W., Sharma, A., Johnson, R. E., & Engert, F. (2019). Point process latent variable models of larval zebrafish behavior. *Computational and Systems Neuroscience (Cosyne) Abstracts*.

Nassar, J., Linderman, S. W., Bugallo, M., & Park, I. M. (2019). Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling. In *International Conference on Learning Representations (ICLR)*.
Paper
arXiv