default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJonathan W. Pillow
Person information
- affiliation: Princeton University, Princeton Neuroscience Institute, NJ, USA
- affiliation (former): The University of Texas at Austin, Department of Psychology
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j24]C. Daniel Greenidge, Benjamin Scholl, Jacob L. Yates, Jonathan W. Pillow:
Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression. Neural Comput. 36(2): 175-226 (2024)- [j23]Aditi Jha, Zoe C. Ashwood, Jonathan W. Pillow:
Active Learning for Discrete Latent Variable Models. Neural Comput. 36(3): 437-474 (2024) - [c43]Victor Geadah, International Brain Laboratory, Jonathan W. Pillow:
Parsing neural dynamics with infinite recurrent switching linear dynamical systems. ICLR 2024 - [c42]Orren Karniol-Tambour, David M. Zoltowski, E. Mika Diamanti, Lucas Pinto, Carlos D. Brody, David W. Tank, Jonathan W. Pillow:
Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems. ICLR 2024 - 2023
- [j22]Lea Duncker, Kiersten M. Ruda, Greg D. Field, Jonathan W. Pillow
:
Scalable Variational Inference for Low-Rank Spatiotemporal Receptive Fields. Neural Comput. 35(6): 995-1027 (2023) - [j21]Iris R. Stone, Yotam Sagiv, Il Memming Park, Jonathan W. Pillow:
Spectral learning of Bernoulli linear dynamical systems models for decision-making. Trans. Mach. Learn. Res. 2023 (2023) - [i10]Iris R. Stone, Yotam Sagiv, Il Memming Park, Jonathan W. Pillow:
Spectral learning of Bernoulli linear dynamical systems models for decision-making. CoRR abs/2303.02060 (2023) - [i9]Peter Halmos, Jonathan W. Pillow, David A. Knowles:
System Identification for Continuous-time Linear Dynamical Systems. CoRR abs/2308.11933 (2023) - 2022
- [j20]Adrian Valente, Srdjan Ostojic, Jonathan W. Pillow:
Probing the Relationship Between Latent Linear Dynamical Systems and Low-Rank Recurrent Neural Network Models. Neural Comput. 34(9): 1871-1892 (2022) - [j19]Matthew S. Creamer, Kevin S. Chen, Andrew M. Leifer, Jonathan W. Pillow:
Correcting motion induced fluorescence artifacts in two-channel neural imaging. PLoS Comput. Biol. 18(9): 1010421 (2022) - [c41]Zoe Ashwood, Aditi Jha, Jonathan W. Pillow:
Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior. NeurIPS 2022 - [c40]Adrian Valente, Jonathan W. Pillow, Srdjan Ostojic:
Extracting computational mechanisms from neural data using low-rank RNNs. NeurIPS 2022 - [i8]Michael J. Morais, Jonathan W. Pillow:
Loss-calibrated expectation propagation for approximate Bayesian decision-making. CoRR abs/2201.03128 (2022) - [i7]Aditi Jha, Zoe C. Ashwood, Jonathan W. Pillow:
Bayesian Active Learning for Discrete Latent Variable Models. CoRR abs/2202.13426 (2022) - 2021
- [j18]Anqi Wu, Samuel A. Nastase, Christopher Baldassano, Nicholas B. Turk-Browne, Kenneth A. Norman, Barbara E. Engelhardt, Jonathan W. Pillow:
Brain kernel: A new spatial covariance function for fMRI data. NeuroImage 245: 118580 (2021) - [c39]Aditi Jha, Michael J. Morais, Jonathan W. Pillow:
Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction. ICML 2021: 4850-4859 - [c38]Timothy D. Kim, Thomas Z. Luo, Jonathan W. Pillow, Carlos D. Brody:
Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations. ICML 2021: 5551-5561 - [c37]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, Jonathan W. Pillow, Il Memming Park, Eva L. Dyer, Chethan Pandarinath:
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. NeurIPS Datasets and Benchmarks 2021 - [i6]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, Jonathan W. Pillow, Il Memming Park, Eva L. Dyer, Chethan Pandarinath:
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. CoRR abs/2109.04463 (2021) - 2020
- [j17]Camille E. Rullán Buxó, Jonathan W. Pillow:
Poisson balanced spiking networks. PLoS Comput. Biol. 16(11) (2020) - [c36]Stephen L. Keeley, David M. Zoltowski, Yiyi Yu, Spencer L. Smith, Jonathan W. Pillow:
Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations. ICML 2020: 5177-5186 - [c35]David M. Zoltowski, Jonathan W. Pillow, Scott W. Linderman:
A general recurrent state space framework for modeling neural dynamics during decision-making. ICML 2020: 11680-11691 - [c34]Benjamin Cowley, Jonathan W. Pillow:
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex. NeurIPS 2020 - [c33]Zoe Ashwood, Nicholas A. Roy, Ji Hyun Bak, Jonathan W. Pillow:
Inferring learning rules from animal decision-making. NeurIPS 2020 - [c32]Stephen L. Keeley, Mikio C. Aoi, Yiyi Yu, Spencer L. Smith, Jonathan W. Pillow:
Identifying signal and noise structure in neural population activity with Gaussian process factor models. NeurIPS 2020 - [i5]Benjamin R. Cowley, Jonathan W. Pillow:
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex. CoRR abs/2006.11412 (2020)
2010 – 2019
- 2019
- [j16]Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow:
Dependent relevance determination for smooth and structured sparse regression. J. Mach. Learn. Res. 20: 89:1-89:43 (2019) - [j15]Mingbo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv:
Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. PLoS Comput. Biol. 15(5) (2019) - [i4]Stephen L. Keeley, David M. Zoltowski, Yiyi Yu, Jacob L. Yates, Spencer L. Smith, Jonathan W. Pillow:
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations. CoRR abs/1906.03318 (2019) - [i3]Hugo Richard, Lucas Martin, Ana Luísa Pinho, Jonathan W. Pillow, Bertrand Thirion:
Fast shared response model for fMRI data. CoRR abs/1909.12537 (2019) - 2018
- [j14]Adam S. Charles, Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow:
Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. Neural Comput. 30(4) (2018) - [c31]David M. Zoltowski, Jonathan W. Pillow:
Scaling the Poisson GLM to massive neural datasets through polynomial approximations. NeurIPS 2018: 3521-3531 - [c30]Michael J. Morais, Jonathan W. Pillow:
Power-law efficient neural codes provide general link between perceptual bias and discriminability. NeurIPS 2018: 5076-5085 - [c29]Anqi Wu, Stan L. Pashkovski, Sandeep R. Datta, Jonathan W. Pillow:
Learning a latent manifold of odor representations from neural responses in piriform cortex. NeurIPS 2018: 5383-5393 - [c28]Nicholas A. Roy, Ji Hyun Bak, Athena Akrami, Carlos D. Brody, Jonathan W. Pillow:
Efficient inference for time-varying behavior during learning. NeurIPS 2018: 5700-5710 - [c27]Mikio C. Aoi, Jonathan W. Pillow:
Model-based targeted dimensionality reduction for neuronal population data. NeurIPS 2018: 6691-6700 - [i2]Qihong Lu, Po-Hsuan Chen, Jonathan W. Pillow, Peter J. Ramadge, Kenneth A. Norman, Uri Hasson:
Shared Representational Geometry Across Neural Networks. CoRR abs/1811.11684 (2018) - 2017
- [j13]Alison I. Weber, Jonathan W. Pillow:
Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models. Neural Comput. 29(12) (2017) - [c26]Adam S. Charles, Alexander Song, Sue Ann Koay, David W. Tank, Jonathan W. Pillow:
Stochastic filtering of two-photon imaging using reweighted ℓ1. ICASSP 2017: 1038-1042 - [c25]Anqi Wu, Nicholas A. Roy, Stephen L. Keeley, Jonathan W. Pillow:
Gaussian process based nonlinear latent structure discovery in multivariate spike train data. NIPS 2017: 3496-3505 - 2016
- [c24]Ji Hyun Bak, Jung Choi, Ilana Witten, Athena Akrami, Jonathan W. Pillow:
Adaptive optimal training of animal behavior. NIPS 2016: 1939-1947 - [c23]Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow:
Bayesian latent structure discovery from multi-neuron recordings. NIPS 2016: 2002-2010 - [c22]Mingbo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv:
A Bayesian method for reducing bias in neural representational similarity analysis. NIPS 2016: 4952-4960 - 2015
- [j12]Ross S. Williamson, Maneesh Sahani, Jonathan W. Pillow:
The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction. PLoS Comput. Biol. 11(4) (2015) - [c21]Anqi Wu, Il Memming Park, Jonathan W. Pillow:
Convolutional spike-triggered covariance analysis for neural subunit models. NIPS 2015: 793-801 - 2014
- [j11]Evan Archer, Il Memming Park, Jonathan W. Pillow:
Bayesian entropy estimation for countable discrete distributions. J. Mach. Learn. Res. 15(1): 2833-2868 (2014) - [j10]Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow:
Bayesian Active Learning of Neural Firing Rate Maps with Transformed Gaussian Process Priors. Neural Comput. 26(8): 1519-1541 (2014) - [c20]Evan W. Archer, Urs Köster, Jonathan W. Pillow, Jakob H. Macke:
Low-dimensional models of neural population activity in sensory cortical circuits. NIPS 2014: 343-351 - [c19]Kenneth W. Latimer, E. J. Chichilnisky, Fred Rieke, Jonathan W. Pillow:
Inferring synaptic conductances from spike trains with a biophysically inspired point process model. NIPS 2014: 954-962 - [c18]Karin C. Knudson, Jacob L. Yates, Alexander Huk, Jonathan W. Pillow:
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit. NIPS 2014: 1215-1223 - [c17]Anqi Wu, Mijung Park, Oluwasanmi Koyejo, Jonathan W. Pillow:
Sparse Bayesian structure learning with dependent relevance determination priors. NIPS 2014: 1628-1636 - [c16]Agnieszka Grabska-Barwinska, Jonathan W. Pillow:
Optimal prior-dependent neural population codes under shared input noise. NIPS 2014: 1880-1888 - 2013
- [j9]Evan Archer, Il Memming Park, Jonathan W. Pillow:
Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data. Entropy 15(5): 1738-1755 (2013) - [c15]Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell A. Poldrack, Jonathan W. Pillow:
Bayesian Structure Learning for Functional Neuroimaging. AISTATS 2013: 489-497 - [c14]Evan Archer, Il Memming Park, Jonathan W. Pillow:
Bayesian entropy estimation for binary spike train data using parametric prior knowledge. NIPS 2013: 1700-1708 - [c13]Karin C. Knudson, Jonathan W. Pillow:
Spike train entropy-rate estimation using hierarchical Dirichlet process priors. NIPS 2013: 2076-2084 - [c12]Il Memming Park, Evan Archer, Nicholas Priebe, Jonathan W. Pillow:
Spectral methods for neural characterization using generalized quadratic models. NIPS 2013: 2454-2462 - [c11]Il Memming Park, Evan Archer, Kenneth W. Latimer, Jonathan W. Pillow:
Universal models for binary spike patterns using centered Dirichlet processes. NIPS 2013: 2463-2471 - [c10]Mijung Park, Jonathan W. Pillow:
Bayesian inference for low rank spatiotemporal neural receptive fields. NIPS 2013: 2688-2696 - [i1]Evan Archer, Il Memming Park, Jonathan W. Pillow:
Bayesian Entropy Estimation for Countable Discrete Distributions. CoRR abs/1302.0328 (2013) - 2012
- [j8]Michael Vidne, Yashar Ahmadian, Jonathon Shlens, Jonathan W. Pillow, Jayant Kulkarni, Alan M. Litke, E. J. Chichilnisky, Eero P. Simoncelli, Liam Paninski:
Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. J. Comput. Neurosci. 33(1): 97-121 (2012) - [c9]Jonathan W. Pillow, James G. Scott:
Fully Bayesian inference for neural models with negative-binomial spiking. NIPS 2012: 1907-1915 - [c8]Evan Archer, Il Memming Park, Jonathan W. Pillow:
Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. NIPS 2012: 2024-2032 - [c7]Mijung Park, Jonathan W. Pillow:
Bayesian active learning with localized priors for fast receptive field characterization. NIPS 2012: 2357-2365 - 2011
- [j7]Jonathan W. Pillow, Yashar Ahmadian, Liam Paninski:
Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains. Neural Comput. 23(1): 1-45 (2011) - [j6]Yashar Ahmadian, Jonathan W. Pillow, Liam Paninski:
Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Comput. 23(1): 46-96 (2011) - [j5]Mijung Park, Jonathan W. Pillow:
Receptive Field Inference with Localized Priors. PLoS Comput. Biol. 7(10) (2011) - [c6]Il Memming Park, Jonathan W. Pillow:
Bayesian Spike-Triggered Covariance Analysis. NIPS 2011: 1692-1700 - [c5]Mijung Park, Greg Horwitz, Jonathan W. Pillow:
Active learning of neural response functions with Gaussian processes. NIPS 2011: 2043-2051
2000 – 2009
- 2009
- [c4]Jonathan W. Pillow:
Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. NIPS 2009: 1473-1481 - 2008
- [c3]Pietro Berkes, Frank D. Wood, Jonathan W. Pillow:
Characterizing neural dependencies with copula models. NIPS 2008: 129-136 - 2007
- [c2]Jonathan W. Pillow, Peter E. Latham:
Neural characterization in partially observed populations of spiking neurons. NIPS 2007: 1161-1168 - 2005
- [j4]Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli:
Comparing integrate-and-fire models estimated using intracellular and extracellular data. Neurocomputing 65-66: 379-385 (2005) - 2004
- [j3]Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli:
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model. Neural Comput. 16(12): 2533-2561 (2004) - 2003
- [j2]Jonathan W. Pillow, Eero P. Simoncelli:
Biases in white noise analysis due to non-Poisson spike generation. Neurocomputing 52-54: 109-115 (2003) - [c1]Jonathan W. Pillow, Liam Paninski, Eero P. Simoncelli:
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. NIPS 2003: 1311-1318 - 2000
- [j1]Richard S. Zemel, Jonathan W. Pillow:
Encoding multiple orientations in a recurrent network. Neurocomputing 32-33: 609-616 (2000)
Coauthor Index
Il Park 0002
aka: Il Memming Park
aka: Il Memming Park
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-09-13 00:40 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
