98 citations · 139 across the 9 of their papers we have counts for
10 papers
Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments
Ayesha Vermani, Matthew Dowling, Hyungju Jeon +6
Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence…
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…
Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains
Matthew Dowling, Yuan Zhao, Il Memming Park
Latent Gaussian process (GP) models are widely used in neuroscience to uncover hidden state evolutions from sequential observations, mainly in neural activity recordings. While lat…
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…
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
Yuan Zhao, Il Memming Park
A central challenge in neuroscience is understanding how neural system implements computation through its dynamics. We propose a nonlinear time series model aimed at characterizing…
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…