Jonathan W. Pillow, Ph.D.

Affiliations: 
Princeton Neuroscience Institute Princeton University, Princeton, NJ 
Area:
Computational Neuroscience, Vision
Website:
http://pillowlab.princeton.edu/
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"Jonathan Pillow"
Mean distance: 13.82 (cluster 17)
 
SNBCP

Parents

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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

Children

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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

Publications

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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
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