Probing the Relationship Between Latent Linear Dynamical Systems and Low-Rank Recurrent Neural Network Models | 0 | 0.34 | 2022 |

Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. | 0 | 0.34 | 2021 |

Inferring Latent Dynamics Underlying Neural Population Activity Via Neural Differential Equations | 0 | 0.34 | 2021 |

Factor-Analytic Inverse Regression For High-Dimension, Small-Sample Dimensionality Reduction | 0 | 0.34 | 2021 |

High-contrast “gaudy” images improve the training of deep neural network models of visual cortex | 0 | 0.34 | 2020 |

Identifying signal and noise structure in neural population activity with Gaussian process factor models | 0 | 0.34 | 2020 |

Poisson balanced spiking networks | 1 | 0.35 | 2020 |

Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations. | 0 | 0.34 | 2020 |

Inferring learning rules from animal decision-making | 0 | 0.34 | 2020 |

A general recurrent state space framework for modeling neural dynamics during decision-making | 0 | 0.34 | 2020 |

Dependent relevance determination for smooth and structured sparse regression. | 0 | 0.34 | 2019 |

Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations. | 0 | 0.34 | 2019 |

Shared Representational Geometry Across Neural Networks. | 0 | 0.34 | 2018 |

Model-based targeted dimensionality reduction for neuronal population data. | 0 | 0.34 | 2018 |

Efficient inference for time-varying behavior during learning. | 1 | 0.41 | 2018 |

Learning a latent manifold of odor representations from neural responses in piriform cortex. | 0 | 0.34 | 2018 |

Scaling the Poisson GLM to massive neural datasets through polynomial approximations. | 0 | 0.34 | 2018 |

Power-law efficient neural codes provide general link between perceptual bias and discriminability. | 0 | 0.34 | 2018 |

Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. | 1 | 0.37 | 2018 |

Stochastic filtering of two-photon imaging using reweighted ℓ1. | 0 | 0.34 | 2017 |

Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models. | 8 | 0.56 | 2017 |

A Bayesian method for reducing bias in neural representational similarity analysis. | 0 | 0.34 | 2016 |

Adaptive optimal training of animal behavior. | 0 | 0.34 | 2016 |

Bayesian latent structure discovery from multi-neuron recordings. | 1 | 0.35 | 2016 |

Convolutional spike-triggered covariance analysis for neural subunit models | 1 | 0.35 | 2015 |

The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction. | 8 | 0.57 | 2015 |

Bayesian Active Learning of Neural Firing Rate Maps with Transformed Gaussian Process Priors | 2 | 0.38 | 2014 |

Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit. | 1 | 0.41 | 2014 |

Inferring synaptic conductances from spike trains with a biophysically inspired point process model. | 0 | 0.34 | 2014 |

Low-dimensional models of neural population activity in sensory cortical circuits. | 6 | 0.48 | 2014 |

Sparse Bayesian structure learning with dependent relevance determination priors. | 0 | 0.34 | 2014 |

Optimal prior-dependent neural population codes under shared input noise. | 0 | 0.34 | 2014 |

Spectral methods for neural characterization using generalized quadratic models. | 8 | 0.59 | 2013 |

Bayesian entropy estimation for binary spike train data using parametric prior knowledge. | 2 | 0.44 | 2013 |

Spike train entropy-rate estimation using hierarchical Dirichlet process priors. | 1 | 0.38 | 2013 |

Bayesian entropy estimation for countable discrete distributions | 6 | 0.72 | 2013 |

Bayesian inference for low rank spatiotemporal neural receptive fields. | 4 | 0.46 | 2013 |

Bayesian Structure Learning for Functional Neuroimaging. | 2 | 0.41 | 2013 |

Universal models for binary spike patterns using centered Dirichlet processes. | 3 | 0.46 | 2013 |

Bayesian and Quasi-Bayesian Estimators for Mutual Information from Discrete Data. | 5 | 0.56 | 2013 |

Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. | 28 | 1.34 | 2012 |

Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. | 6 | 0.62 | 2012 |

Fully Bayesian inference for neural models with negative-binomial spiking. | 6 | 0.56 | 2012 |

Bayesian active learning with localized priors for fast receptive field characterization. | 8 | 0.73 | 2012 |

Bayesian Spike-Triggered Covariance Analysis. | 16 | 0.92 | 2011 |

Receptive Field Inference With Localized Priors | 13 | 0.83 | 2011 |

Active learning of neural response functions with Gaussian processes. | 7 | 0.52 | 2011 |

Efficient Markov chain Monte Carlo methods for decoding neural spike trains. | 21 | 1.36 | 2011 |

Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains | 17 | 0.97 | 2011 |

Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. | 8 | 0.58 | 2009 |