Research map / Probabilistic and scientific modeling / Dynamical systems
Bayesian latent structure discovery from multi-neuron recordings
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly infer the network and its latent variables instead of fitting an L1-regularized GLM network first | Network GLM · Synthetic data | network recovery | Helped |
| Jointly infer latent locations and network structure instead of fitting locations to a thresholded GLM network | Network GLM · Synthetic data | connection probability estimation | Helped |
| Use a latent distance model for adjacency and an SBM for weights (ablation) | Network GLM · Primate retinal ganglion cells | held-out neuron predictive log likelihood | Helped |
| Use network GLM latent locations rather than 2D Bernoulli LDS emission vectors as cell locations (ablation) | Retinal ganglion cell location inference · Primate retinal ganglion cells | correlation of inferred and true cell distances | Helped |
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