@inproceedings{d1d3c251107c4262adfc220665cc4c05,
title = "Learning Structured Neural Dynamics from Single Trial Population Recording",
abstract = "To understand the complex nonlinear dynamics of neural circuits, we fit a structured state-space model called tree-structured recurrent switching linear dynamical system (TrSLDS) to noisy high-dimensional neural time series. TrSLDS is a multi-scale hierarchical generative model for the state-space dynamics where each node of the latent tree captures locally linear dynamics. TrSLDS can be learned efficiently and in a fully Bayesian manner using Gibbs sampling. We showcase TrSLDS' potential of inferring low-dimensional interpretable dynamical systems on a variety of examples.",
keywords = "Bayesian inference, dynamical system, population spike trains, state-space model, statistical neuroscience",
author = "Josue Nassar and Linderman, \{Scott W.\} and Yuan Zhao and Monica Bugallo and Park, \{Il Memming\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 ; Conference date: 28-10-2018 Through 31-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ACSSC.2018.8645122",
language = "English",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "666--670",
editor = "Matthews, \{Michael B.\}",
booktitle = "Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018",
}