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Gaussian process based nonlinear latent structure discovery in multivariate spike train data

NeurIPS 2017 · Probabilistic and scientific modeling › Dynamical systems · approach: Low-rank and diffusion neural dynamics · 81 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
decoupled Laplace approximation (dLA) instead of tLA or aLA for latent inference (ablation)P-GPLVM · simulated tuning curvesR2 and convergence speedHelped
decoupled Laplace approximation (dLA) instead of tLA or aLAP-GPLVM · rat hippocampal dataR2 and PLLHelped
Poisson observation model instead of Gaussian (GPLVM) (ablation)GPLVM with nonlinear latent mapping · simulated tuning curvesR2Helped
nonlinear GP-based tuning curves instead of linear mapping (P-GPFA)P-GPLVM · simulated sinusoid and Gaussian bump tuning curvesR2Helped
Poisson GP latent model with exponential-link tuning curves vs P-GPFA and PfLDSP-GPLVM · rat hippocampal dataPLLHelped
P-GPFA ARD kernel for the latent GP instead of RBF-like smoothnessP-GPFA · simulated Gaussian bump tuning curvesR2 stabilityHurt

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