Preprints

  • O’Shea DJ*, Duncker L*, Goo W, Sun X, Vyas S, Trautmann EM, Diester I, Ramakrishnan C, Deisseroth K, Sahani M** , Shenoy KV** (2022). Direct neural perturbations reveal a dynamical mechanism for robust computation. bioRxiv. [paper]

Journal Articles

  • Duncker L, Ruda KM, Field GD, Pillow JW (2022). Scalable variational inference for low-rank spatio-temporal receptive fields. Neural Computation (in press). [paper] [code]

Conference Proceedings

  • Costacurta JC, Duncker L, Sheffer B, Williams AH, Gillis W, Weinreb C, Markowitz JE, Datta SR, Linderman SW (2022). Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs. In Adcances in Neural Information Processing Systems (NeurIPS). [paper]

  • Duncker L*, Driscoll LN*, Shenoy KV, Sahani M** & Sussillo D** (2020). Organizing recurrent network dynamics by task-computation to enable continual learning. Advances in Neural Information Processing Systems. [paper] [supp] [code]

  • Duncker L, Bohner G, Boussard J, & Sahani M (2019). Learning interpretable continuous-time models of latent stochastic dynamical systems. 36th International Conference on Machine Learning (ICML). [paper] [supp] [code]

  • Duncker L, & Sahani M (2018). Temporal alignment and latent Gaussian process factor inference in population spike trains. In Advances in Neural Information Processing Systems (pp. 10466-10476). [paper] [supp]

Invited Journal Articles

  • Driscoll LN, Duncker L, Harvey CD (2021). Representational drift: Emerging theories for continual learning and experimental future directions. Current Opinion in Neurobiology, 76:102609 [paper]

  • Duncker L, & Sahani M (2021). Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings. Current Opinion in Neurobiology, 70:163–170 [paper]

Conference Abstracts

  • O’Shea DJ*, Duncker L*, Vyas S, Sun X, Sahani M, Shenoy KV (2022, poster). Electrical but not optogenetic stimulation drives nonlinear contraction of neural states. Computational and Systems Neuroscience (COSYNE). Lisbon, PT. II-069.

  • Duncker L, O’Shea DJ, Shenoy KV, Sahani M (2020, poster). A dynamical model with E/I balance explains robustness to optogenetic stimulation in motor cortex. Computational and Systems Neuroscience (COSYNE). Denver, CO. III-41.

  • Duncker L, Bohner G, Boussard J, & Sahani M (2019, poster). Inferring interpretable nonlinear stochastic dynamics from population spike trains. Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE), Lisbon, PT. II-102.

  • Duncker L, Sahani M (2018, poster). Disentangling neural population variability using time-warped point-process GPFA. Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE), Denver, CO. II-87.

  • Duncker L, O’Shea DJ, Goo W, Shenoy KV, Sahani M (2017, talk). Low-rank non-stationary population dynamics can account for robustness to optogenetic stimulation. Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE), Salt Lake City, UT. T-29.

  • Duncker L, Ravi S, Field GD, Pillow JW (2017, poster). Scalable variational inference for low-rank receptive fields with nonstationary smoothness. Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE), Salt Lake City, UT. I-86.

  • Adam V, Duncker L, Sahani M (2017, poster). Continuous-time point-process GPFA using sparse variational methods. Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE), Salt Lake City, UT. I-2.