Jonathan W. Pillow

Affiliations:
  • Princeton University, Princeton Neuroscience Institute, NJ, USA
  • The University of Texas at Austin, Department of Psychology (former)


According to our database1, Jonathan W. Pillow authored at least 69 papers between 2000 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Scalable Variational Inference for Low-Rank Spatiotemporal Receptive Fields.
Neural Comput., June, 2023

System Identification for Continuous-time Linear Dynamical Systems.
CoRR, 2023

Spectral learning of Bernoulli linear dynamical systems models for decision-making.
CoRR, 2023

2022
Correcting motion induced fluorescence artifacts in two-channel neural imaging.
PLoS Comput. Biol., September, 2022

Probing the Relationship Between Latent Linear Dynamical Systems and Low-Rank Recurrent Neural Network Models.
Neural Comput., 2022

Bayesian Active Learning for Discrete Latent Variable Models.
CoRR, 2022

Loss-calibrated expectation propagation for approximate Bayesian decision-making.
CoRR, 2022

Extracting computational mechanisms from neural data using low-rank RNNs.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Brain kernel: A new spatial covariance function for fMRI data.
NeuroImage, 2021

Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations.
Proceedings of the 38th International Conference on Machine Learning, 2021

Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Poisson balanced spiking networks.
PLoS Comput. Biol., 2020

Identifying signal and noise structure in neural population activity with Gaussian process factor models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Inferring learning rules from animal decision-making.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

High-contrast "gaudy" images improve the training of deep neural network models of visual cortex.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A general recurrent state space framework for modeling neural dynamics during decision-making.
Proceedings of the 37th International Conference on Machine Learning, 2020

Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias.
PLoS Comput. Biol., 2019

Dependent relevance determination for smooth and structured sparse regression.
J. Mach. Learn. Res., 2019

Fast shared response model for fMRI data.
CoRR, 2019

Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations.
CoRR, 2019

2018
Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability.
Neural Comput., 2018

Shared Representational Geometry Across Neural Networks.
CoRR, 2018

Scaling the Poisson GLM to massive neural datasets through polynomial approximations.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning a latent manifold of odor representations from neural responses in piriform cortex.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Efficient inference for time-varying behavior during learning.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Power-law efficient neural codes provide general link between perceptual bias and discriminability.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Model-based targeted dimensionality reduction for neuronal population data.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models.
Neural Comput., 2017

Gaussian process based nonlinear latent structure discovery in multivariate spike train data.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Stochastic filtering of two-photon imaging using reweighted ℓ1.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016
Bayesian latent structure discovery from multi-neuron recordings.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Bayesian method for reducing bias in neural representational similarity analysis.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Adaptive optimal training of animal behavior.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction.
PLoS Comput. Biol., 2015

Convolutional spike-triggered covariance analysis for neural subunit models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Bayesian Active Learning of Neural Firing Rate Maps with Transformed Gaussian Process Priors.
Neural Comput., 2014

Bayesian entropy estimation for countable discrete distributions.
J. Mach. Learn. Res., 2014

Sparse Bayesian structure learning with dependent relevance determination priors.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Inferring synaptic conductances from spike trains with a biophysically inspired point process model.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Optimal prior-dependent neural population codes under shared input noise.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Low-dimensional models of neural population activity in sensory cortical circuits.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data.
Entropy, 2013

Bayesian inference for low rank spatiotemporal neural receptive fields.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Spectral methods for neural characterization using generalized quadratic models.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Universal models for binary spike patterns using centered Dirichlet processes.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Spike train entropy-rate estimation using hierarchical Dirichlet process priors.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Bayesian entropy estimation for binary spike train data using parametric prior knowledge.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Bayesian Structure Learning for Functional Neuroimaging.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Modeling the impact of common noise inputs on the network activity of retinal ganglion cells.
J. Comput. Neurosci., 2012

Fully Bayesian inference for neural models with negative-binomial spiking.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Bayesian active learning with localized priors for fast receptive field characterization.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Bayesian estimation of discrete entropy with mixtures of stick-breaking priors.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011
Receptive Field Inference with Localized Priors.
PLoS Comput. Biol., 2011

Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains.
Neural Comput., 2011

Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains.
Neural Comput., 2011

Bayesian Spike-Triggered Covariance Analysis.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Active learning of neural response functions with Gaussian processes.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2009
Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008
Characterizing neural dependencies with copula models.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007
Neural characterization in partially observed populations of spiking neurons.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2005
Comparing integrate-and-fire models estimated using intracellular and extracellular data.
Neurocomputing, 2005

2004
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model.
Neural Comput., 2004

2003
Biases in white noise analysis due to non-Poisson spike generation.
Neurocomputing, 2003

Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

2000
Encoding multiple orientations in a recurrent network.
Neurocomputing, 2000


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