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.   Using state of the art Bayesian machine learning methods to condition on readily obtainable data, our method paves the way for neuroscientists to recover interpretable connectome-wide state representations, automatically estimate physiologically relevant parameter values from data, and perform simulations investigating intelligent lifeforms in silico. One of the goals of artificial intelligence, neuroscience and connectomics is to understand how sentience emerges from the interactions of the atomic units of the brain, to be able to probe these mechanisms on the deepest level in living organisms, and to be able to simulate this interaction ad infinitum [1] . In this work, we assemble an anatomically grounded, interpretable probabilistic brainbody simulator for the widely studied nematode roundworm Caenorhabditis elegans (C. elegans) [2, 3] . We then present methods for performing posterior inference in the time evolution of the state of the worm and estimate the global simulator parameter values from readily obtainable non-invasive calcium fluorescence data [4] . We refer to using an anatomically grounded model to infer latent states and parameters, conditioned on partial data, as a "virtual patch clamp" (VPC). The VPC also facilitates in silico experimentation on "digital" C. elegans specimens, by programmatically modifying the simulator and observing the resulting simulations; enabling rapid, wide-reaching, fully observable and perfectly repeatable exploration of hypotheses into the how the fundamental units of the neural circuit of C. elegans combine to create intelligent behaviour. In this work we have explored performing Bayesian inference in whole-connectome neural and whole-body C. elegans simulations. We describe the model-based Bayesian inference aspect of this as a "virtual patch clamp," whereby unobserved latent membrane potentials can be inferred from partial observations gathered non-invasively. Our choice of inference method facilitates estimation of the model evidence, a measure of how well the model explains the observed data. We presented a method for maximizing this evidence without requiring differentiable simulation components. In the past year several articles discussing open research issues pertaining to C. elegans simulation have been produced by the C. elegans community [1, 11] . Figure 1 (a) outlines the community planned development pipeline for C. elegans simulation. Our work addresses the implementation of the box simply labelled "optimization." We show on representative synthetic data that our method is capable of performing such an optimization. This approach promises to allow neuroscientists to peer deeper into the neural function of a living organism, testing hypothesis on neural function that were previously unreachable. It is widely touted that convolutional neural networks were developed by wide-scale study of the V1 cortex. We believe connectome-level optimization and simulation, as demonstrated here, is the next step in neuroscience to understanding the very root of intelligence, but also discovering and developing techniques building towards artificial general intelligence.
We develop a whole-connectome and body simulator for C. elegans and demonstrate joint state-space and parameter inference in the simulator.
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