The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging
This work addresses the challenge of inferring brain states at single-cell fidelity from practical measurements in neuroscience, representing an incremental step in simulation-based imputation.
The researchers tackled the problem of imputing latent membrane potentials from partial calcium imaging in C. elegans by developing a stochastic whole-brain and body simulator, achieving the first known use of an anatomically grounded connectome simulator for this purpose.
We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations. This is the first attempt we know of to "complete the circle," where an anatomically grounded whole-connectome simulator is used to impute a time-varying "brain" state at single-cell fidelity from covariates that are measurable in practice. The sequential Monte Carlo (SMC) method we employ not only enables imputation of said latent states but also presents a strategy for learning simulator parameters via variational optimization of the noisy model evidence approximation provided by SMC. Our imputation and parameter estimation experiments were conducted on distributed systems using novel implementations of the aforementioned techniques applied to synthetic data of dimension and type representative of that which are measured in laboratories currently.