Encoding multiple orientations in a recurrent network. - Richard S. Zemel; Jonathan W. Pillow (Neurocomputing, 2000)

Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. - Jonathan W. Pillow; Liam Paninski; Eero P. Simoncelli (NIPS, 2003)

Biases in white noise analysis due to non-Poisson spike generation. - Jonathan W. Pillow; Eero P. Simoncelli (Neurocomputing, 2003)

Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model. - Liam Paninski; Jonathan W. Pillow; Eero P. Simoncelli (Neural Comput., 2004)

Comparing integrate-and-fire models estimated using intracellular and extracellular data. - Liam Paninski; Jonathan W. Pillow; Eero P. Simoncelli (Neurocomputing, 2005)

Neural characterization in partially observed populations of spiking neurons. - Jonathan W. Pillow; Peter E. Latham (NIPS, 2007)

Characterizing neural dependencies with copula models. - Pietro Berkes; Frank D. Wood; Jonathan W. Pillow (NIPS, 2008)

Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. - Jonathan W. Pillow (NIPS, 2009)

Active learning of neural response functions with Gaussian processes. - Mijung Park; Greg Horwitz; Jonathan W. Pillow (NIPS, 2011)

Bayesian Spike-Triggered Covariance Analysis. - Il Memming Park; Jonathan W. Pillow (NIPS, 2011)

Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. - Yashar Ahmadian; Jonathan W. Pillow; Liam Paninski (Neural Comput., 2011)

Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains. - Jonathan W. Pillow; Yashar Ahmadian; Liam Paninski (Neural Comput., 2011)

Receptive Field Inference with Localized Priors. - Mijung Park; Jonathan W. Pillow (PLoS Comput. Biol., 2011)

Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. - Evan Archer; Il Memming Park; Jonathan W. Pillow (NIPS, 2012)

Bayesian active learning with localized priors for fast receptive field characterization. - Mijung Park; Jonathan W. Pillow (NIPS, 2012)

Fully Bayesian inference for neural models with negative-binomial spiking. - Jonathan W. Pillow; James G. Scott (NIPS, 2012)

Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. - Michael Vidne; Yashar Ahmadian; Jonathon Shlens; Jonathan W. Pillow; Jayant Kulkarni; Alan M. Litke; E. J. Chichilnisky; Eero P. Simoncelli; Liam Paninski (J. Comput. Neurosci., 2012)

Bayesian Structure Learning for Functional Neuroimaging. - Mijung Park; Oluwasanmi Koyejo; Joydeep Ghosh; Russell A. Poldrack; Jonathan W. Pillow (AISTATS, 2013)

Bayesian entropy estimation for binary spike train data using parametric prior knowledge. - Evan Archer; Il Memming Park; Jonathan W. Pillow (NIPS, 2013)

Spike train entropy-rate estimation using hierarchical Dirichlet process priors. - Karin C. Knudson; Jonathan W. Pillow (NIPS, 2013)

Universal models for binary spike patterns using centered Dirichlet processes. - Il Memming Park; Evan Archer; Kenneth W. Latimer; Jonathan W. Pillow (NIPS, 2013)

Spectral methods for neural characterization using generalized quadratic models. - Il Memming Park; Evan Archer; Nicholas Priebe; Jonathan W. Pillow (NIPS, 2013)

Bayesian inference for low rank spatiotemporal neural receptive fields. - Mijung Park; Jonathan W. Pillow (NIPS, 2013)

Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data. - Evan Archer; Il Memming Park; Jonathan W. Pillow (Entropy, 2013)

Low-dimensional models of neural population activity in sensory cortical circuits. - Evan W. Archer; Urs Köster; Jonathan W. Pillow; Jakob H. Macke (NIPS, 2014)

Optimal prior-dependent neural population codes under shared input noise. - Agnieszka Grabska-Barwinska; Jonathan W. Pillow (NIPS, 2014)

Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit. - Karin C. Knudson; Jacob L. Yates; Alexander Huk; Jonathan W. Pillow (NIPS, 2014)

Inferring synaptic conductances from spike trains with a biophysically inspired point process model. - Kenneth W. Latimer; E. J. Chichilnisky; Fred Rieke; Jonathan W. Pillow (NIPS, 2014)

Sparse Bayesian structure learning with dependent relevance determination priors. - Anqi Wu; Mijung Park; Oluwasanmi Koyejo; Jonathan W. Pillow (NIPS, 2014)

Bayesian entropy estimation for countable discrete distributions. - Evan Archer; Il Memming Park; Jonathan W. Pillow (J. Mach. Learn. Res., 2014)

Bayesian Active Learning of Neural Firing Rate Maps with Transformed Gaussian Process Priors. - Mijung Park; J. Patrick Weller; Gregory D. Horwitz; Jonathan W. Pillow (Neural Comput., 2014)

Convolutional spike-triggered covariance analysis for neural subunit models. - Anqi Wu; Il Memming Park; Jonathan W. Pillow (NIPS, 2015)

The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction. - Ross S. Williamson; Maneesh Sahani; Jonathan W. Pillow (PLoS Comput. Biol., 2015)

Adaptive optimal training of animal behavior. - Ji Hyun Bak; Jung Choi; Ilana Witten; Athena Akrami; Jonathan W. Pillow (NIPS, 2016)

A Bayesian method for reducing bias in neural representational similarity analysis. - Mingbo Cai; Nicolas W. Schuck; Jonathan W. Pillow; Yael Niv (NIPS, 2016)

Bayesian latent structure discovery from multi-neuron recordings. - Scott W. Linderman; Ryan P. Adams; Jonathan W. Pillow (NIPS, 2016)

Stochastic filtering of two-photon imaging using reweighted ℓ1. - Adam S. Charles; Alexander Song; Sue Ann Koay; David W. Tank; Jonathan W. Pillow (ICASSP, 2017)

Gaussian process based nonlinear latent structure discovery in multivariate spike train data. - Anqi Wu; Nicholas A. Roy; Stephen L. Keeley; Jonathan W. Pillow (NIPS, 2017)

Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models. - Alison I. Weber; Jonathan W. Pillow (Neural Comput., 2017)

Model-based targeted dimensionality reduction for neuronal population data. - Mikio C. Aoi; Jonathan W. Pillow (NeurIPS, 2018)

Power-law efficient neural codes provide general link between perceptual bias and discriminability. - Michael J. Morais; Jonathan W. Pillow (NeurIPS, 2018)

Efficient inference for time-varying behavior during learning. - Nicholas A. Roy; Ji Hyun Bak; Athena Akrami; Carlos D. Brody; Jonathan W. Pillow (NeurIPS, 2018)

Learning a latent manifold of odor representations from neural responses in piriform cortex. - Anqi Wu; Stan L. Pashkovski; Sandeep R. Datta; Jonathan W. Pillow (NeurIPS, 2018)

Scaling the Poisson GLM to massive neural datasets through polynomial approximations. - David M. Zoltowski; Jonathan W. Pillow (NeurIPS, 2018)

Shared Representational Geometry Across Neural Networks. - Qihong Lu; Po-Hsuan Chen; Jonathan W. Pillow; Peter J. Ramadge; Kenneth A. Norman; Uri Hasson (CoRR, 2018)

Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. - Adam S. Charles; Mijung Park; J. Patrick Weller; Gregory D. Horwitz; Jonathan W. Pillow (Neural Comput., 2018)

Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations. - Stephen L. Keeley; David M. Zoltowski; Yiyi Yu; Jacob L. Yates; Spencer L. Smith; Jonathan W. Pillow (CoRR, 2019)

Fast shared response model for fMRI data. - Hugo Richard; Lucas Martin; Ana Luísa Pinho; Jonathan W. Pillow; Bertrand Thirion (CoRR, 2019)

Dependent relevance determination for smooth and structured sparse regression. - Anqi Wu; Oluwasanmi Koyejo; Jonathan W. Pillow (J. Mach. Learn. Res., 2019)

Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias. - Mingbo Cai; Nicolas W. Schuck; Jonathan W. Pillow; Yael Niv (PLoS Comput. Biol., 2019)

Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations. - Stephen L. Keeley; David M. Zoltowski; Yiyi Yu; Spencer L. Smith; Jonathan W. Pillow (ICML, 2020)

A general recurrent state space framework for modeling neural dynamics during decision-making. - David M. Zoltowski; Jonathan W. Pillow; Scott W. Linderman (ICML, 2020)

High-contrast "gaudy" images improve the training of deep neural network models of visual cortex. - Benjamin Cowley 002; Jonathan W. Pillow (NeurIPS, 2020)

Inferring learning rules from animal decision-making. - Zoe Ashwood; Nicholas A. Roy; Ji Hyun Bak; Jonathan W. Pillow (NeurIPS, 2020)

Identifying signal and noise structure in neural population activity with Gaussian process factor models. - Stephen L. Keeley; Mikio C. Aoi; Yiyi Yu; Spencer L. Smith; Jonathan W. Pillow (NeurIPS, 2020)

Poisson balanced spiking networks. - Camille E. Rullán Buxó; Jonathan W. Pillow (PLoS Comput. Biol., 2020)

Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction. - Aditi Jha; Michael J. Morais; Jonathan W. Pillow (ICML, 2021)

Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations. - Timothy D. Kim; Thomas Zhihao Luo; Jonathan W. Pillow; Carlos D. Brody (ICML, 2021)

Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. - 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; et al. (NeurIPS Datasets and Benchmarks, 2021)

Brain kernel: A new spatial covariance function for fMRI data. - Anqi Wu; Samuel A. Nastase; Christopher Baldassano; Nicholas B. Turk-Browne; Kenneth A. Norman; Barbara E. Engelhardt; Jonathan W. Pillow (NeuroImage, 2021)

Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior. - Zoe Ashwood; Aditi Jha; Jonathan W. Pillow (NeurIPS, 2022)

Extracting computational mechanisms from neural data using low-rank RNNs. - Adrian Valente; Jonathan W. Pillow; Srdjan Ostojic (NeurIPS, 2022)

Loss-calibrated expectation propagation for approximate Bayesian decision-making. - Michael J. Morais; Jonathan W. Pillow (CoRR, 2022)

Bayesian Active Learning for Discrete Latent Variable Models. - Aditi Jha; Zoe C. Ashwood; Jonathan W. Pillow (CoRR, 2022)

Probing the Relationship Between Latent Linear Dynamical Systems and Low-Rank Recurrent Neural Network Models. - Adrian Valente; Srdjan Ostojic; Jonathan W. Pillow (Neural Comput., 2022)

Correcting motion induced fluorescence artifacts in two-channel neural imaging. - Matthew S. Creamer; Kevin S. Chen; Andrew M. Leifer; Jonathan W. Pillow (PLoS Comput. Biol., 2022)

System Identification for Continuous-time Linear Dynamical Systems. - Peter Halmos; Jonathan W. Pillow; David A. Knowles (CoRR, 2023)

Spectral learning of Bernoulli linear dynamical systems models for decision-making. - Iris R. Stone; Yotam Sagiv; Il Memming Park; Jonathan W. Pillow (Trans. Mach. Learn. Res., 2023)

Scalable Variational Inference for Low-Rank Spatiotemporal Receptive Fields. - Lea Duncker; Kiersten M. Ruda; Greg D. Field; Jonathan W. Pillow (Neural Comput., 2023)

Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations. - Victor Geadah; Juncal Arbelaiz; Harrison Ritz; Nathaniel D. Daw; Jonathan D. Cohen 003; Jonathan W. Pillow (CDC, 2024)

Parsing neural dynamics with infinite recurrent switching linear dynamical systems. - Victor Geadah; International Brain Laboratory; Jonathan W. Pillow (ICLR, 2024)

Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems. - Orren Karniol-Tambour; David M. Zoltowski; E. Mika Diamanti; Lucas Pinto; Carlos D. Brody; David W. Tank; Jonathan W. Pillow (ICLR, 2024)

Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems. - Aditi Jha; Diksha Gupta; Carlos D. Brody; Jonathan W. Pillow (NeurIPS, 2024)

Sparse-Coding Variational Autoencoders. - Victor Geadah; Gabriel Barello; C. Daniel Greenidge; Adam S. Charles; Jonathan W. Pillow (Neural Comput., 2024)

Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression. - C. Daniel Greenidge; Benjamin Scholl; Jacob L. Yates; Jonathan W. Pillow (Neural Comput., 2024)

Active Learning for Discrete Latent Variable Models. - Aditi Jha; Zoe C. Ashwood; Jonathan W. Pillow (Neural Comput., 2024)