  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.
