Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Flexible statistical inference for mechanistic models of neural dynamics
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SNPE instead of CDE-LFI | Gaussian-mixture inference with multimodal posteriors · Two-mean Gaussian mixture | posterior inference flexibility | Helped |
| Use SNPE instead of CDE-LFI | Gaussian-mixture inference across algorithmic rounds · Common-mean Gaussian mixture | numerical robustness | Helped |
| Use SNPE rather than CDE-LFI | GLM posterior estimation · 10-parameter GLM | posterior estimate agreement | No clear effect |
| Use SNPE rather than IBEA | Hodgkin-Huxley parameter inference · Simulated Hodgkin-Huxley data | ground-truth parameter approximation | No clear effect |
| Add a classifier to predict and reject parameter settings likely to produce failed simulations | SNPE · Autapse model | simulation efficiency | Helped |
| Add learned imputation values for missing features | SNPE posterior network · Hodgkin-Huxley simulations with non-spiking behavior | handling missing features | Helped |
| Replace hand-designed summary features with a recurrent network over voltage and current traces | SNPE · Hodgkin-Huxley time-series inference | posterior uncertainty for spike-related parameters | Helped |
| Use SNPE rather than SMC-ABC | 10-parameter GLM inference · GLM | inference performance | Helped |
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