Research map / Probabilistic and scientific modeling / Dynamical systems
Gaussian process based nonlinear latent structure discovery in multivariate spike train data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| decoupled Laplace approximation (dLA) instead of tLA or aLA for latent inference (ablation) | P-GPLVM · simulated tuning curves | R2 and convergence speed | Helped |
| decoupled Laplace approximation (dLA) instead of tLA or aLA | P-GPLVM · rat hippocampal data | R2 and PLL | Helped |
| Poisson observation model instead of Gaussian (GPLVM) (ablation) | GPLVM with nonlinear latent mapping · simulated tuning curves | R2 | Helped |
| nonlinear GP-based tuning curves instead of linear mapping (P-GPFA) | P-GPLVM · simulated sinusoid and Gaussian bump tuning curves | R2 | Helped |
| Poisson GP latent model with exponential-link tuning curves vs P-GPFA and PfLDS | P-GPLVM · rat hippocampal data | PLL | Helped |
| P-GPFA ARD kernel for the latent GP instead of RBF-like smoothness | P-GPFA · simulated Gaussian bump tuning curves | R2 stability | Hurt |
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