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Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity.

F. Pei, J. Ye, D. Zoltowski, A. Wu, R. Chowdhury, H. Sohn, J. O'Doherty, K. Shenoy, M. Kaufman, M. Churchland, M. Jazayeri, L. Miller, J. Pillow, I. Park, E. Dyer, and C. Pandarinath. NeurIPS Datasets and Benchmarks, (2021)

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High-performance neural population dynamics modeling enabled by scalable computational infrastructure.A. Patel, A. Sedler, J. Huang, C. Pandarinath, and V. Gilja. J. Open Source Softw., 8 (83): 5023 (March 2023)LFADS - Latent Factor Analysis via Dynamical Systems.D. Sussillo, R. Józefowicz, L. Abbott, and C. Pandarinath. CoRR, (2016)Expressive architectures enhance interpretability of dynamics-based neural population models.A. Sedler, C. Versteeg, and C. Pandarinath. CoRR, (2022)Representation learning for neural population activity with Neural Data Transformers.J. Ye, and C. Pandarinath. CoRR, (2021)A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain-Computer Interfaces.F. Willett, B. Murphy, D. Young, W. Memberg, C. Blabe, C. Pandarinath, B. Franco, J. Saab, B. Walter, J. Sweet and 8 other author(s). IEEE Trans. Biomed. Eng., 65 (9): 2066-2078 (2018)Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time.F. Zhu, A. Sedler, H. Grier, N. Ahad, M. Davenport, M. Kaufman, A. Giovannucci, and C. Pandarinath. NeurIPS, page 2331-2345. (2021)A system for optically controlling neural circuits with very high spatial and temporal resolution.C. Pandarinath, E. Carlson, and S. Nirenberg. BIBE, page 1-6. IEEE Computer Society, (2013)Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity.F. Pei, J. Ye, D. Zoltowski, A. Wu, R. Chowdhury, H. Sohn, J. O'Doherty, K. Shenoy, M. Kaufman, M. Churchland and 6 other author(s). NeurIPS Datasets and Benchmarks, (2021)Enabling hyperparameter optimization in sequential autoencoders for spiking neural data.M. Keshtkaran, and C. Pandarinath. NeurIPS, page 15911-15921. (2019)Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces.N. Even-Chen, S. Stavisky, C. Pandarinath, P. Nuyujukian, C. Blabe, L. Hochberg, J. Henderson, and K. Shenoy. IEEE Trans. Biomed. Eng., 65 (8): 1771-1784 (2018)

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Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity.

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Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity.