A novel mechanism for switching a neural system from one state to another. - Chethan Pandarinath; Illya Bomash; Jonathan D. Victor; Glen T. Prusky; Wayne Tschetter; Sheila Nirenberg (Frontiers Comput. Neurosci., 2010)

A system for optically controlling neural circuits with very high spatial and temporal resolution. - Chethan Pandarinath; Eric T. Carlson; Sheila Nirenberg (BIBE, 2013)

LFADS - Latent Factor Analysis via Dynamical Systems. - David Sussillo; Rafal Józefowicz; L. F. Abbott; Chethan Pandarinath (CoRR, 2016)

Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces. - Nir Even-Chen; Sergey D. Stavisky; Chethan Pandarinath; Paul Nuyujukian; Christine H. Blabe; Leigh R. Hochberg; Jaimie M. Henderson; Krishna V. Shenoy (IEEE Trans. Biomed. Eng., 2018)

A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain-Computer Interfaces. - Francis R. Willett; Brian A. Murphy; Daniel Young; William D. Memberg; Christine H. Blabe; Chethan Pandarinath; Brian Franco; Jad Saab; Benjamin L. Walter; Jennifer A. Sweet; Jonathan P. Miller; Jaimie M. Henderson; et al. (IEEE Trans. Biomed. Eng., 2018)

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data. - Mohammad Reza Keshtkaran; Chethan Pandarinath (NeurIPS, 2019)

Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. - Felix Pei; Joel Ye; David M. Zoltowski; Anqi Wu; Raeed H. Chowdhury; Hansem Sohn; Joseph E. O'Doherty; Krishna V. Shenoy; Matthew T. Kaufman; Mark M. Churchland; Mehrdad Jazayeri; Lee E. Miller; et al. (NeurIPS Datasets and Benchmarks, 2021)

Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time. - Feng Zhu; Andrew R. Sedler; Harrison A. Grier; Nauman Ahad; Mark A. Davenport; Matthew T. Kaufman; Andrea Giovannucci; Chethan Pandarinath (NeurIPS, 2021)

Representation learning for neural population activity with Neural Data Transformers. - Joel Ye; Chethan Pandarinath (CoRR, 2021)

Expressive architectures enhance interpretability of dynamics-based neural population models. - Andrew R. Sedler; Christopher Versteeg; Chethan Pandarinath (CoRR, 2022)

Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity. - Christopher Versteeg; Andrew R. Sedler; Jonathan D. McCart; Chethan Pandarinath (NeurReps, 2023)

lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems. - Andrew R. Sedler; Chethan Pandarinath (CoRR, 2023)

High-performance neural population dynamics modeling enabled by scalable computational infrastructure. - Aashish N. Patel; Andrew R. Sedler; Jingya Huang; Chethan Pandarinath; Vikash Gilja (J. Open Source Softw., 2023)

Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark. - Brianna Karpowicz; Joel Ye; Chaofei Fan; Pablo Tostado-Marcos; Fabio Rizzoglio; Clayton Washington; Thiago Scodeler; Diogo de Lucena; Samuel R. Nason-Tomaszewski; Matthew Mender; Xuan Ma; Ezequiel M. Arneodo; et al. (NeurIPS, 2024)

Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes. - Jonathan D. McCart; Andrew R. Sedler; Christopher Versteeg; Domenick Mifsud; Mattia Rigotti-Thompson; Chethan Pandarinath (CoRR, 2024)