Jonathan W. Pillow

83 papers A* 18A 1Misc 1Journal 37Unranked 26

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Flexible inference of learning rules from de novo learning data using neural networks. CoRR Yuhan Helena Liu, Victor Geadah, Jonathan W. Pillow
2025	A*	`conf`	Flow-field inference from neural data using deep recurrent networks. ICML Timothy Doyeon Kim, Thomas Zhihao Luo, Tankut Can, Kamesh Krishnamurthy, Jonathan W. Pillow, Carlos D. Brody
2025	J	`jnl`	Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations. CoRR Victor Geadah, Juncal Arbelaiz, Harrison Ritz, Nathaniel D. Daw, Jonathan D. Cohen, Jonathan W. Pillow
2024	J	`jnl`	Active Learning for Discrete Latent Variable Models. Neural Comput. Aditi Jha, Zoe C. Ashwood, Jonathan W. Pillow
2024	A*	`conf`	Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems. NeurIPS Aditi Jha, Diksha Gupta, Carlos D. Brody, Jonathan W. Pillow
2024	J	`jnl`	Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression. Neural Comput. C. Daniel Greenidge, Benjamin Scholl, Jacob L. Yates, Jonathan W. Pillow
2024	—	`conf`	Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations. CDC Victor Geadah, Juncal Arbelaiz, Harrison Ritz, Nathaniel D. Daw, Jonathan D. Cohen, Jonathan W. Pillow
2024	A*	`conf`	Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems. ICLR Orren Karniol-Tambour, David M. Zoltowski, E. Mika Diamanti, Lucas Pinto, Carlos D. Brody, David W. Tank, Jonathan W. Pillow
2024	A*	`conf`	Parsing neural dynamics with infinite recurrent switching linear dynamical systems. ICLR Victor Geadah, International Brain Laboratory, Jonathan W. Pillow
2024	J	`jnl`	Sparse-Coding Variational Autoencoders. Neural Comput. Victor Geadah, Gabriel Barello, C. Daniel Greenidge, Adam S. Charles, Jonathan W. Pillow
2023	J	`jnl`	Scalable Variational Inference for Low-Rank Spatiotemporal Receptive Fields. Neural Comput. Lea Duncker, Kiersten M. Ruda, Greg D. Field, Jonathan W. Pillow
2023	J	`jnl`	Spectral learning of Bernoulli linear dynamical systems models for decision-making. CoRR Iris R. Stone, Yotam Sagiv, Il Memming Park, Jonathan W. Pillow
2023	J	`jnl`	Spectral learning of Bernoulli linear dynamical systems models for decision-making. Trans. Mach. Learn. Res. Iris R. Stone, Yotam Sagiv, Il Memming Park, Jonathan W. Pillow
2023	J	`jnl`	System Identification for Continuous-time Linear Dynamical Systems. CoRR Peter Halmos, Jonathan W. Pillow, David A. Knowles
2022	J	`jnl`	Bayesian Active Learning for Discrete Latent Variable Models. CoRR Aditi Jha, Zoe C. Ashwood, Jonathan W. Pillow
2022	J	`jnl`	Correcting motion induced fluorescence artifacts in two-channel neural imaging. PLoS Comput. Biol. Matthew S. Creamer, Kevin S. Chen, Andrew M. Leifer, Jonathan W. Pillow
2022	A*	`conf`	Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior. NeurIPS Zoe Ashwood, Aditi Jha, Jonathan W. Pillow
2022	A*	`conf`	Extracting computational mechanisms from neural data using low-rank RNNs. NeurIPS Adrian Valente, Jonathan W. Pillow, Srdjan Ostojic
2022	J	`jnl`	Loss-calibrated expectation propagation for approximate Bayesian decision-making. CoRR Michael J. Morais, Jonathan W. Pillow
2022	J	`jnl`	Probing the Relationship Between Latent Linear Dynamical Systems and Low-Rank Recurrent Neural Network Models. Neural Comput. Adrian Valente, Srdjan Ostojic, Jonathan W. Pillow
2021	J	`jnl`	Brain kernel: A new spatial covariance function for fMRI data. NeuroImage Anqi Wu, Samuel A. Nastase, Christopher Baldassano, Nicholas B. Turk-Browne, Kenneth A. Norman, Barbara E. Engelhardt, Jonathan W. Pillow
2021	A*	`conf`	Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction. ICML Aditi Jha, Michael J. Morais, Jonathan W. Pillow
2021	A*	`conf`	Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations. ICML Timothy Doyeon Kim, Thomas Zhihao Luo, Jonathan W. Pillow, Carlos D. Brody
2021	—	`conf`	Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. NeurIPS Datasets and Benchmarks 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
2021	J	`jnl`	Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. CoRR 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
2020	A*	`conf`	A general recurrent state space framework for modeling neural dynamics during decision-making. ICML David M. Zoltowski, Jonathan W. Pillow, Scott W. Linderman
2020	A*	`conf`	Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations. ICML Stephen L. Keeley, David M. Zoltowski, Yiyi Yu, Spencer L. Smith, Jonathan W. Pillow
2020	A*	`conf`	High-contrast "gaudy" images improve the training of deep neural network models of visual cortex. NeurIPS Benjamin Cowley, Jonathan W. Pillow
2020	J	`jnl`	High-contrast "gaudy" images improve the training of deep neural network models of visual cortex. CoRR Benjamin R. Cowley, Jonathan W. Pillow
2020	A*	`conf`	Identifying signal and noise structure in neural population activity with Gaussian process factor models. NeurIPS Stephen L. Keeley, Mikio C. Aoi, Yiyi Yu, Spencer L. Smith, Jonathan W. Pillow
2020	A*	`conf`	Inferring learning rules from animal decision-making. NeurIPS Zoe Ashwood, Nicholas A. Roy, Ji Hyun Bak, Jonathan W. Pillow
2020	J	`jnl`	Poisson balanced spiking networks. PLoS Comput. Biol. Camille E. Rullán Buxó, Jonathan W. Pillow
2019	J	`jnl`	Dependent relevance determination for smooth and structured sparse regression. J. Mach. Learn. Res. Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow
2019	J	`jnl`	Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations. CoRR Stephen L. Keeley, David M. Zoltowski, Yiyi Yu, Jacob L. Yates, Spencer L. Smith, Jonathan W. Pillow
2019	J	`jnl`	Fast shared response model for fMRI data. CoRR Hugo Richard, Lucas Martin, Ana Luísa Pinho, Jonathan W. Pillow, Bertrand Thirion
2019	J	`jnl`	Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. PLoS Comput. Biol. Mingbo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv
2018	J	`jnl`	Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. Neural Comput. Adam S. Charles, Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow
2018	A*	`conf`	Efficient inference for time-varying behavior during learning. NeurIPS Nicholas A. Roy, Ji Hyun Bak, Athena Akrami, Carlos D. Brody, Jonathan W. Pillow
2018	A*	`conf`	Learning a latent manifold of odor representations from neural responses in piriform cortex. NeurIPS Anqi Wu, Stan L. Pashkovski, Sandeep R. Datta, Jonathan W. Pillow
2018	A*	`conf`	Model-based targeted dimensionality reduction for neuronal population data. NeurIPS Mikio C. Aoi, Jonathan W. Pillow
2018	A*	`conf`	Power-law efficient neural codes provide general link between perceptual bias and discriminability. NeurIPS Michael J. Morais, Jonathan W. Pillow
2018	A*	`conf`	Scaling the Poisson GLM to massive neural datasets through polynomial approximations. NeurIPS David M. Zoltowski, Jonathan W. Pillow
2018	J	`jnl`	Shared Representational Geometry Across Neural Networks. CoRR Qihong Lu, Po-Hsuan Chen, Jonathan W. Pillow, Peter J. Ramadge, Kenneth A. Norman, Uri Hasson
2017	J	`jnl`	Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models. Neural Comput. Alison I. Weber, Jonathan W. Pillow
2017	—	`conf`	Gaussian process based nonlinear latent structure discovery in multivariate spike train data. NIPS Anqi Wu, Nicholas A. Roy, Stephen L. Keeley, Jonathan W. Pillow
2017	Misc	`conf`	Stochastic filtering of two-photon imaging using reweighted ℓ1. ICASSP Adam S. Charles, Alexander Song, Sue Ann Koay, David W. Tank, Jonathan W. Pillow
2016	—	`conf`	A Bayesian method for reducing bias in neural representational similarity analysis. NIPS Mingbo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv
2016	—	`conf`	Adaptive optimal training of animal behavior. NIPS Ji Hyun Bak, Jung Choi, Ilana Witten, Athena Akrami, Jonathan W. Pillow
2016	—	`conf`	Bayesian latent structure discovery from multi-neuron recordings. NIPS Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow
2015	—	`conf`	Convolutional spike-triggered covariance analysis for neural subunit models. NIPS Anqi Wu, Il Memming Park, Jonathan W. Pillow
2015	J	`jnl`	The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction. PLoS Comput. Biol. Ross S. Williamson, Maneesh Sahani, Jonathan W. Pillow
2014	J	`jnl`	Bayesian Active Learning of Neural Firing Rate Maps with Transformed Gaussian Process Priors. Neural Comput. Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow
2014	J	`jnl`	Bayesian entropy estimation for countable discrete distributions. J. Mach. Learn. Res. Evan Archer, Il Memming Park, Jonathan W. Pillow
2014	—	`conf`	Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit. NIPS Karin C. Knudson, Jacob L. Yates, Alexander Huk, Jonathan W. Pillow
2014	—	`conf`	Inferring synaptic conductances from spike trains with a biophysically inspired point process model. NIPS Kenneth W. Latimer, E. J. Chichilnisky, Fred Rieke, Jonathan W. Pillow
2014	—	`conf`	Low-dimensional models of neural population activity in sensory cortical circuits. NIPS Evan W. Archer, Urs Köster, Jonathan W. Pillow, Jakob H. Macke
2014	—	`conf`	Optimal prior-dependent neural population codes under shared input noise. NIPS Agnieszka Grabska-Barwinska, Jonathan W. Pillow
2014	—	`conf`	Sparse Bayesian structure learning with dependent relevance determination priors. NIPS Anqi Wu, Mijung Park, Oluwasanmi Koyejo, Jonathan W. Pillow
2013	J	`jnl`	Bayesian Entropy Estimation for Countable Discrete Distributions CoRR Evan Archer, Il Memming Park, Jonathan W. Pillow
2013	A	`conf`	Bayesian Structure Learning for Functional Neuroimaging. AISTATS Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell A. Poldrack, Jonathan W. Pillow
2013	J	`jnl`	Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data. Entropy Evan Archer, Il Memming Park, Jonathan W. Pillow
2013	—	`conf`	Bayesian entropy estimation for binary spike train data using parametric prior knowledge. NIPS Evan Archer, Il Memming Park, Jonathan W. Pillow
2013	—	`conf`	Bayesian inference for low rank spatiotemporal neural receptive fields. NIPS Mijung Park, Jonathan W. Pillow
2013	—	`conf`	Spectral methods for neural characterization using generalized quadratic models. NIPS Il Memming Park, Evan Archer, Nicholas Priebe, Jonathan W. Pillow
2013	—	`conf`	Spike train entropy-rate estimation using hierarchical Dirichlet process priors. NIPS Karin C. Knudson, Jonathan W. Pillow
2013	—	`conf`	Universal models for binary spike patterns using centered Dirichlet processes. NIPS Il Memming Park, Evan Archer, Kenneth W. Latimer, Jonathan W. Pillow
2012	—	`conf`	Bayesian active learning with localized priors for fast receptive field characterization. NIPS Mijung Park, Jonathan W. Pillow
2012	—	`conf`	Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. NIPS Evan Archer, Il Memming Park, Jonathan W. Pillow
2012	—	`conf`	Fully Bayesian inference for neural models with negative-binomial spiking. NIPS Jonathan W. Pillow, James G. Scott
2012	J	`jnl`	Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. J. Comput. Neurosci. Michael Vidne, Yashar Ahmadian, Jonathon Shlens, Jonathan W. Pillow, Jayant Kulkarni, Alan M. Litke, E. J. Chichilnisky, Eero P. Simoncelli, Liam Paninski
2011	—	`conf`	Active learning of neural response functions with Gaussian processes. NIPS Mijung Park, Greg Horwitz, Jonathan W. Pillow
2011	—	`conf`	Bayesian Spike-Triggered Covariance Analysis. NIPS Il Memming Park, Jonathan W. Pillow
2011	J	`jnl`	Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Comput. Yashar Ahmadian, Jonathan W. Pillow, Liam Paninski
2011	J	`jnl`	Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains. Neural Comput. Jonathan W. Pillow, Yashar Ahmadian, Liam Paninski
2011	J	`jnl`	Receptive Field Inference with Localized Priors. PLoS Comput. Biol. Mijung Park, Jonathan W. Pillow
2009	—	`conf`	Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. NIPS Jonathan W. Pillow
2008	—	`conf`	Characterizing neural dependencies with copula models. NIPS Pietro Berkes, Frank D. Wood, Jonathan W. Pillow
2007	—	`conf`	Neural characterization in partially observed populations of spiking neurons. NIPS Jonathan W. Pillow, Peter E. Latham
2005	J	`jnl`	Comparing integrate-and-fire models estimated using intracellular and extracellular data. Neurocomputing Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli
2004	J	`jnl`	Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model. Neural Comput. Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli
2003	J	`jnl`	Biases in white noise analysis due to non-Poisson spike generation. Neurocomputing Jonathan W. Pillow, Eero P. Simoncelli
2003	—	`conf`	Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. NIPS Jonathan W. Pillow, Liam Paninski, Eero P. Simoncelli
2000	J	`jnl`	Encoding multiple orientations in a recurrent network. Neurocomputing Richard S. Zemel, Jonathan W. Pillow