Jonathan W. Pillow

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 42

Top Topics

Neural Dynamics

Dynamical Systems

Animal Behavior

Dimensionality Reduction

Top Venues

Publications

Victor Geadah, International Brain Laboratory, Jonathan W. Pillow
Parsing neural dynamics with infinite recurrent switching linear dynamical systems. ICLR (2024)
Aditi Jha, Zoe C. Ashwood, Jonathan W. Pillow
Active Learning for Discrete Latent Variable Models. Neural Comput. 36 (3) (2024)
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)
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) (2024)
Peter Halmos, Jonathan W. Pillow, David A. Knowles
System Identification for Continuous-time Linear Dynamical Systems. CoRR (2023)
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)
Iris R. Stone, Yotam Sagiv, Il Memming Park, Jonathan W. Pillow
Spectral learning of Bernoulli linear dynamical systems models for decision-making. CoRR (2023)
Lea Duncker, Kiersten M. Ruda, Greg D. Field, Jonathan W. Pillow
Scalable Variational Inference for Low-Rank Spatiotemporal Receptive Fields. Neural Comput. 35 (6) (2023)
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) (2022)
Michael J. Morais, Jonathan W. Pillow
Loss-calibrated expectation propagation for approximate Bayesian decision-making. CoRR (2022)
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) (2022)
Adrian Valente, Jonathan W. Pillow, Srdjan Ostojic
Extracting computational mechanisms from neural data using low-rank RNNs. NeurIPS (2022)
Aditi Jha, Zoe C. Ashwood, Jonathan W. Pillow
Bayesian Active Learning for Discrete Latent Variable Models. CoRR (2022)
Zoe Ashwood, Aditi Jha, Jonathan W. Pillow
Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior. NeurIPS (2022)
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)
Aditi Jha, Michael J. Morais, Jonathan W. Pillow
Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction. ICML (2021)
Timothy D. Kim, Thomas Z. Luo, Jonathan W. Pillow, Carlos Brody
Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations. ICML (2021)
Anqi Wu, Samuel A. Nastase, Christopher A. 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 (2021)
Felix Pei, Joel Ye, David L. 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 (2021)
Camille E. Rullán Buxó, Jonathan W. Pillow
Poisson balanced spiking networks. PLoS Comput. Biol. 16 (11) (2020)
Benjamin R. Cowley, Jonathan W. Pillow
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex. CoRR (2020)
Stephen L. Keeley, David L. 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)
David M. Zoltowski, Jonathan W. Pillow, Scott W. Linderman
A general recurrent state space framework for modeling neural dynamics during decision-making. ICML (2020)
Stephen L. Keeley, Mikio 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)
Zoe Ashwood, Nicholas A. Roy, Ji Hyun Bak, Jonathan W. Pillow
Inferring learning rules from animal decision-making. NeurIPS (2020)
Benjamin Cowley, Jonathan W. Pillow
High-contrast "gaudy" images improve the training of deep neural network models of visual cortex. NeurIPS (2020)
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 (2019)
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)
Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow
Dependent relevance determination for smooth and structured sparse regression. J. Mach. Learn. Res. 20 (2019)
Michael J. Morais, Jonathan W. Pillow
Power-law efficient neural codes provide general link between perceptual bias and discriminability. NeurIPS (2018)
Qihong Lu, Po-Hsuan Chen, Jonathan W. Pillow, Peter J. Ramadge, Kenneth A. Norman, Uri Hasson
Shared Representational Geometry Across Neural Networks. CoRR (2018)
David M. Zoltowski, Jonathan W. Pillow
Scaling the Poisson GLM to massive neural datasets through polynomial approximations. NeurIPS (2018)
Mikio Aoi, Jonathan W. Pillow
Model-based targeted dimensionality reduction for neuronal population data. NeurIPS (2018)
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)
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)
Nicholas A. Roy, Ji Hyun Bak, Athena Akrami, Carlos Brody, Jonathan W. Pillow
Efficient inference for time-varying behavior during learning. NeurIPS (2018)
Alison I. Weber, Jonathan W. Pillow
Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models. Neural Comput. 29 (12) (2017)
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)
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)
Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow
Bayesian latent structure discovery from multi-neuron recordings. NIPS (2016)
Mingbo Cai, Nicolas W. Schuck, Jonathan W. Pillow, Yael Niv
A Bayesian method for reducing bias in neural representational similarity analysis. NIPS (2016)
Ji Hyun Bak, Jung Choi, Ilana Witten, Athena Akrami, Jonathan W. Pillow
Adaptive optimal training of animal behavior. NIPS (2016)
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)
Anqi Wu, Il Memming Park, Jonathan W. Pillow
Convolutional spike-triggered covariance analysis for neural subunit models. NIPS (2015)
Agnieszka Grabska-Barwinska, Jonathan W. Pillow
Optimal prior-dependent neural population codes under shared input noise. NIPS (2014)
Karin C. Knudson, Jacob L. Yates, Alexander Huk, Jonathan W. Pillow
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit. NIPS (2014)
Anqi Wu, Mijung Park, Oluwasanmi Koyejo, Jonathan W. Pillow
Sparse Bayesian structure learning with dependent relevance determination priors. NIPS (2014)
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) (2014)
Evan Archer, Il Memming Park, Jonathan W. Pillow
Bayesian entropy estimation for countable discrete distributions. J. Mach. Learn. Res. 15 (1) (2014)
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)
Evan W. Archer, Urs Köster, Jonathan W. Pillow, Jakob H. Macke
Low-dimensional models of neural population activity in sensory cortical circuits. NIPS (2014)
Evan Archer, Il Memming Park, Jonathan W. Pillow
Bayesian Entropy Estimation for Countable Discrete Distributions CoRR (2013)
Karin C. Knudson, Jonathan W. Pillow
Spike train entropy-rate estimation using hierarchical Dirichlet process priors. NIPS (2013)
Il Memming Park, Evan Archer, Nicholas Priebe, Jonathan W. Pillow
Spectral methods for neural characterization using generalized quadratic models. NIPS (2013)
Mijung Park, Jonathan W. Pillow
Bayesian inference for low rank spatiotemporal neural receptive fields. NIPS (2013)
Il Memming Park, Evan Archer, Kenneth W. Latimer, Jonathan W. Pillow
Universal models for binary spike patterns using centered Dirichlet processes. NIPS (2013)
Evan Archer, Il Memming Park, Jonathan W. Pillow
Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data. Entropy 15 (5) (2013)
Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell A. Poldrack, Jonathan W. Pillow
Bayesian Structure Learning for Functional Neuroimaging. AISTATS (2013)
Evan Archer, Il Memming Park, Jonathan W. Pillow
Bayesian entropy estimation for binary spike train data using parametric prior knowledge. NIPS (2013)
Mijung Park, Jonathan W. Pillow
Bayesian active learning with localized priors for fast receptive field characterization. NIPS (2012)
Jonathan W. Pillow, James G. Scott
Fully Bayesian inference for neural models with negative-binomial spiking. NIPS (2012)
Evan Archer, Il Memming Park, Jonathan W. Pillow
Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. NIPS (2012)
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) (2012)
Mijung Park, Jonathan W. Pillow
Receptive Field Inference with Localized Priors. PLoS Comput. Biol. 7 (10) (2011)
Yashar Ahmadian, Jonathan W. Pillow, Liam Paninski
Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Comput. 23 (1) (2011)
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) (2011)
Mijung Park, Greg Horwitz, Jonathan W. Pillow
Active learning of neural response functions with Gaussian processes. NIPS (2011)
Il Memming Park, Jonathan W. Pillow
Bayesian Spike-Triggered Covariance Analysis. NIPS (2011)
Jonathan W. Pillow
Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. NIPS (2009)
Pietro Berkes, Frank D. Wood, Jonathan W. Pillow
Characterizing neural dependencies with copula models. NIPS (2008)
Jonathan W. Pillow, Peter E. Latham
Neural characterization in partially observed populations of spiking neurons. NIPS (2007)
Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli
Comparing integrate-and-fire models estimated using intracellular and extracellular data. Neurocomputing (2005)
Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model. Neural Comput. 16 (12) (2004)
Jonathan W. Pillow, Eero P. Simoncelli
Biases in white noise analysis due to non-Poisson spike generation. Neurocomputing (2003)
Jonathan W. Pillow, Liam Paninski, Eero P. Simoncelli
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. NIPS (2003)
Richard S. Zemel, Jonathan W. Pillow
Encoding multiple orientations in a recurrent network. Neurocomputing (2000)