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Variational Joint Filtering

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.

Il Memming Park
PI contact information
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