The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging

TL;DR

This paper introduces a stochastic simulator of C. elegans that can infer hidden neuronal membrane potentials from calcium imaging data, bridging the gap between observable signals and underlying neural states.

Contribution

It presents the first anatomically grounded whole-connectome simulator capable of imputing single-cell neural states from partial calcium imaging observations.

Findings

Successful imputation of latent membrane potentials from calcium imaging data.

Effective parameter learning via variational optimization of SMC-based model evidence.

Scalable implementation on distributed systems for synthetic data analysis.

Abstract

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.