What is being modeled?
streaming high-dimensional spike trains in ongoing experiments
Description & purpose of resource
Variational Joint Filtering (VJF) is a flexible framework that online learns latent nonlinear state dynamics and filters latent states from high-dimensional spike trains.
VJF is amenable to real-time applications, enables experimentalist to monitor complex data in the ongoing experiments at an abstract level, and has the potential to automate analysis and experimental design in ways that testably track and modify behavior using stimuli designed to influence learning.
Temporal scales
10-3 - 1 s
This resource is currently
likely to require significant study prior to effective reuse
Key publications (e.g. describing or using resource)
Zhao, Y. and Park, I.M. Variational online learning of neural dynamics. Frontiers in Computational Neuroscience, 2020.
DOI link to publication describing this resource
10.3389/fncom.2020.00071
Link to resource
Collaborators
Il Memming Park
PI contact information
memming.park@stonybrook.edu
Keywords
BRAIN TMM
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