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New preprint by brilliant @JosueNassar in my group. A new state-space model with multi-scale hierarchical dynamics prior. https://arxiv.org/abs/1811.12386 Collaboration with @scott_linderman and Monica Bugallo #sciencestorm #neuroscience #Bayesian #MachineLearning 1/6pic.twitter.com/127P61uIBn
2/6 Learning dynamics is key to understanding neural computation, but they can be too complex to understand. Can we view it at different levels of granularity? E.g. Lorenz system approximated with 2 linear dynamical systems?pic.twitter.com/UmD3IhS5Gm
3/6 Or with 4 linear dynamical systems (LDS)? Both of these are from a single model.pic.twitter.com/kX4xFLZQye
4/6 We propose a generative model, TrSLDS (Tree-structured recurrent switching LDS), extending @scott_linderman’s rSLDS (AISTATS 2017) with fully Bayesian sampling using Polya-Gamma augmentation and tree-structured stick breaking.pic.twitter.com/QIfPrC0ac6
5/6 Just from the spike trains, we can recover decision making dynamics from the XJ Wang’s 2002 spiking neural network model. (in another paper with @yuanz271 http://catniplab.github.io/publications/Nassar2018a.pdf …)pic.twitter.com/OAAcFhQDyl
6/6 It can also recover limit cycles from population V1 spike trains! Looking forward to applying to more neural data!pic.twitter.com/LVGFOy63Nx
Does your method beat LFADS?
Good Q. We will compare soon!
Amazing work! May I know if code is going to be made available anytime soon?
We'll make it public when it's accepted. (or maybe sooner!)
Just started flipping through this. So far it looks amazing!
This looks awesome. Congrats !!
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