Jonathan W. Pillow, Ph.D.

Princeton Neuroscience Institute Princeton University, Princeton, NJ 
Computational Neuroscience, Vision
"Jonathan Pillow"
Mean distance: 13.82 (cluster 17)


Sign in to add mentor
Rich S. Zemel research assistant University of Arizona
Eero P. Simoncelli grad student 2001-2005 Princeton
 (Neural coding and the statistical modeling of neuronal responses.)
Peter Latham post-doc 2005-2008 Gatsby Computational Neuroscience Unit


Sign in to add trainee
Anqi Wu grad student 2014- Princeton
Nick A Roy grad student 2015- Princeton
Mike Morais grad student 2016- Princeton
Mijung Park grad student 2009-2013 UT Austin
Kenneth W. Latimer grad student 2011-2015 UT Austin
Jacob Yates grad student 2011-2016 UT Austin
Ji Hyun Bak grad student 2015-2016 Princeton
Mikio Aoi post-doc Princeton
Adam Charles post-doc 2015- Princeton
Brian D. DePasquale post-doc 2016- Princeton
Abigail A Russo post-doc 2019- Princeton
Il Memming Park post-doc 2010-2014 UT Austin
BETA: Related publications


You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect.

Zoltowski DM, Latimer KW, Yates JL, et al. (2019) Discrete Stepping and Nonlinear Ramping Dynamics Underlie Spiking Responses of LIP Neurons during Decision-Making. Neuron
Aoi MC, Pillow JW. (2018) Model-based targeted dimensionality reduction for neuronal population data. Advances in Neural Information Processing Systems. 31: 6690-6699
Roy NA, Bak JH, Akrami A, et al. (2018) Efficient inference for time-varying behavior during learning. Advances in Neural Information Processing Systems. 31: 5695-5705
Zoltowski DM, Pillow JW. (2018) Scaling the Poisson GLM to massive neural datasets through polynomial approximations. Advances in Neural Information Processing Systems. 31: 3517-3527
Bak JH, Pillow JW. (2018) Adaptive stimulus selection for multi-alternative psychometric functions with lapses. Journal of Vision. 18: 4
Knöll J, Pillow JW, Huk AC. (2018) Lawful tracking of visual motion in humans, macaques, and marmosets in a naturalistic, continuous, and untrained behavioral context. Proceedings of the National Academy of Sciences of the United States of America
Rokers B, Fulvio JM, Pillow JW, et al. (2018) Systematic misperceptions of 3-D motion explained by Bayesian inference. Journal of Vision. 18: 23
Charles AS, Park M, Weller JP, et al. (2018) Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. Neural Computation. 1-34
Wu A, Roy NA, Keeley S, et al. (2017) Gaussian process based nonlinear latent structure discovery in multivariate spike train data. Advances in Neural Information Processing Systems. 30: 3496-3505
Murugan M, Jang HJ, Park M, et al. (2017) Combined Social and Spatial Coding in a Descending Projection from the Prefrontal Cortex. Cell. 171: 1663-1677.e16
See more...