Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations

Stephen L. Keeley
Stephen L. Keeley
Yiyi Yu
Yiyi Yu
Spencer L. Smith
Spencer L. Smith

CoRR, 2019.

Cited by: 1|Views12
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Abstract:

Gaussian Process Factor Analysis (GPFA) has been broadly applied to the problem of identifying smooth, low-dimensional temporal structure underlying large-scale neural recordings. However, spike trains are non-Gaussian, which motivates combining GPFA with discrete observation models for binned spike count data. The drawback to this appr...More

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