Research map / Probabilistic and scientific modeling / Dynamical systems
Temporal alignment and latent Gaussian process factor inference in population spike trains
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add time-warping GP layer to svGPFA (ablation) | svGPFA · Macaque reaching task | leave-one-neuron-out predictive log-likelihood | Helped |
| time-warped latent model vs switching LDS for reaction time prediction | switching LDS · Macaque reaching task | R2: 0.52 -> 0.90 | Helped |
| inferred time-warping alignment vs MO or GC alignment | manual alignment · Macaque reaching task | std of hand position threshold crossing times | Helped |
| sparse variational inference (svGPFA) vs vLGP | Poisson GPFA · Synthetic spikes | approximation error vs runtime | Helped |
| add time-warping to point-process svGPFA (ablation) | pp-svGPFA · Synthetic spikes | RMSE of inferred firing rates | Helped |
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