Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains

11 April 2016

Papers citing "Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains"

17 / 17 papers shown

Title
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time Feng Zhu Andrew R. Sedler Harrison A. Grier Nauman Ahad Mark A. Davenport Matthew T. Kaufman Andrea Giovannucci C. Pandarinath 69 10 0 29 Oct 2021
Nonnegative spatial factorization F. W. Townes Barbara E. Engelhardt 35 11 0 12 Oct 2021
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity Felix Pei Joel Ye D. Zoltowski Anqi Wu Raeed H. Chowdhury ... L. Miller Jonathan W. Pillow Il Memming Park Eva L. Dyer C. Pandarinath 296 90 0 09 Sep 2021
Representation learning for neural population activity with Neural Data Transformers Joel Ye C. Pandarinath AI4TS AI4CE 230 57 0 02 Aug 2021
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model Rui Meng K. Bouchard AI4TS 50 2 0 25 Jun 2021
Gaussian Process Convolutional Dictionary Learning Andrew H. Song Bahareh Tolooshams Demba E. Ba 110 2 0 28 Mar 2021
Building population models for large-scale neural recordings: opportunities and pitfalls C. Hurwitz N. Kudryashova A. Onken Matthias H Hennig 73 39 0 03 Feb 2021
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE Ding Zhou Xue-Xin Wei DRL 254 84 0 09 Nov 2020
Point process models for sequence detection in high-dimensional neural spike trains Alex H. Williams Anthony Degleris Yixin Wang Scott W. Linderman AI4TS 57 30 0 10 Oct 2020
Unifying and generalizing models of neural dynamics during decision-making D. Zoltowski Jonathan W. Pillow Scott W. Linderman 40 8 0 13 Jan 2020
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Mohammad Reza Keshtkaran C. Pandarinath 58 37 0 21 Aug 2019
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks Qi She Anqi Wu BDL 51 34 0 01 Jul 2019
Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations Stephen L. Keeley D. Zoltowski Yiyi Yu Jacob L. Yates S. L. Smith Jonathan W. Pillow 47 20 0 07 Jun 2019
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling Josue Nassar Scott W. Linderman M. Bugallo Il-Su Park AI4CE 112 73 0 29 Nov 2018
Variational online learning of neural dynamics Yuan Zhao Il Memming Park BDL OffRL 74 9 0 27 Jul 2017
LFADS - Latent Factor Analysis via Dynamical Systems David Sussillo Rafal Jozefowicz L. F. Abbott C. Pandarinath AI4CE 74 91 0 22 Aug 2016
Linear dynamical neural population models through nonlinear embeddings Yuanjun Gao Evan Archer Liam Paninski John P. Cunningham 84 155 0 26 May 2016