neural data

Probabilistic Geometric Principal Component Analysis with application to neural data ICLR 2025 Inferring stochastic low-rank recurrent neural networks from neural data NeurIPS 2024 Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data ICML 2023 iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data ICLR 2022 Neural Network Poisson Models for Behavioural and Neural Spike Train Data ICML 2022 Non-reversible Gaussian processes for identifying latent dynamical structure in neural data NeurIPS 2020 Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks UAI 2019 Neural system identification for large populations separating “what” and “where” NeurIPS 2017 Clustered factor analysis of multineuronal spike data NeurIPS 2014 Approximate inference in latent Gaussian-Markov models from continuous time observations NeurIPS 2013 Bayesian entropy estimation for binary spike train data using parametric prior knowledge NeurIPS 2013 Bayesian inference for low rank spatiotemporal neural receptive fields NeurIPS 2013 Robust learning of low-dimensional dynamics from large neural ensembles NeurIPS 2013 Fully Bayesian inference for neural models with negative-binomial spiking NeurIPS 2012 Dynamical segmentation of single trials from population neural data NeurIPS 2011 Switching state space model for simultaneously estimating state transitions and nonstationary firing rates NeurIPS 2010 Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies NeurIPS 2009 Dependent Dirichlet Process Spike Sorting NeurIPS 2008