Research map / Probabilistic and scientific modeling / Dynamical systems
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn non-reversibility parameter alpha instead of pinning it to zero (ablation) | planar SE-kernel GP · pendulum and Duffing oscillator trajectories | cross-validated marginal likelihood: alpha optimized to 0.99 (pendulum), 0.9 (Duffing) | Helped |
| learn non-reversibility parameter alpha instead of pinning it to zero (ablation) | planar SE-kernel GP · pendulum and Duffing oscillator trajectories | extrapolation of x2 given x1 | Helped |
| replace independent SE latent priors (GPFA) with non-reversible planar priors (GPFADS) | GPFA · Van der Pol oscillator plus reversible distractor | demixing / cross-validated marginal likelihood: |alpha12|=0.88 vs |alpha34|=0.13 | Helped |
| allow non-reversibility (alpha learned) vs alpha set to zero, with more latent dimensions (ablation) | GPFADS · monkey M1 reaching, M=2,4,6 | test marginal likelihood difference | Helped |
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