98 citations · 139 across the 9 of their papers we have counts for
7 papers · 1 filter
eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling
Matthew Dowling, Yuan Zhao, Il Memming Park
State-space graphical models and the variational autoencoder framework provide a principled apparatus for learning dynamical systems from data. State-of-the-art probabilistic appro…
Real-Time Variational Method for Learning Neural Trajectory and its Dynamics
Matthew Dowling, Yuan Zhao, Il Memming Park
Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorith…
Rescuing neural spike train models from bad MLE
Diego M. Arribas, Yuan Zhao, Il Memming Park
The standard approach to fitting an autoregressive spike train model is to maximize the likelihood for one-step prediction. This maximum likelihood estimation (MLE) often leads to…
Non-parametric generalized linear model
Matthew Dowling, Yuan Zhao, Il Memming Park
A fundamental problem in statistical neuroscience is to model how neurons encode information by analyzing electrophysiological recordings. A popular and widely-used approach is to…
Streaming Variational Monte Carlo
Yuan Zhao, Josue Nassar, Ian Jordan +2
Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simul…
Variational online learning of neural dynamics
Yuan Zhao, Il Memming Park
New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underl…
Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains
Yuan Zhao, Il Memming Park
When governed by underlying low-dimensional dynamics, the interdependence of simultaneously recorded population of neurons can be explained by a small number of shared factors, or…