Research map / Probabilistic and scientific modeling / Dynamical systems
A universal probabilistic spike count model reveals ongoing modulation of neural variability
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace Poisson likelihood with universal count model (GP mapping) | spike count regression model · mouse head direction cells (33 cells) | cross-validated log-likelihood | Helped |
| replace Poisson/hNB likelihood with universal count model | spike count regression model · synthetic heteroscedastic CMP population | KS goodness-of-fit (TKS) | Helped |
| use GP mapping instead of ANN mapping in UCM (ablation) | UCM · synthetic data with latent variable | overfitting / cvLL | Helped |
| use heteroscedastic negative binomial instead of Poisson | count regression baseline · mouse head direction cells | cross-validated log-likelihood | Hurt |
| add two latent dimensions to regression-only UCM (ablation) | UCM · mouse head direction cells | cross-validated log-likelihood | Helped |
| increase latent dimensions beyond two (ablation) | UCM · mouse head direction cells | cross-validated log-likelihood | Hurt |
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