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An Efficient and Flexible Spike Train Model via Empirical Bayes
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An Efficient and Flexible Spike Train Model via Empirical Bayes

Qi She
Xiaoli Wu
Beth Jelfs
Adam S. Charles
Rosa H.M.Chan
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Papers citing "An Efficient and Flexible Spike Train Model via Empirical Bayes"

4 / 4 papers shown
Title
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
Qi She
Anqi Wu
BDL
37
34
0
01 Jul 2019
Linear dynamical neural population models through nonlinear embeddings
Linear dynamical neural population models through nonlinear embeddings
Yuanjun Gao
Evan Archer
Liam Paninski
John P. Cunningham
68
155
0
26 May 2016
Discovering Latent Network Structure in Point Process Data
Discovering Latent Network Structure in Point Process Data
Scott W. Linderman
Ryan P. Adams
91
283
0
04 Feb 2014
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Matthew D. Hoffman
Andrew Gelman
169
4,309
0
18 Nov 2011

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