Weighted Network Density Predicts Range of Latent Variable Model Accuracy

Current experimental techniques impose spatial limits on the number of neuronal units that can be recorded in-vivo. To model the neural dynamics utilizing these sampled data, Latent Variable Models (LVMs) have been proposed to study the common unobserved processes within the system that drives neural activities, through an implicit network with hidden states. Yet, relationships between these latent variable models and widely-studied network connectivity measures remained unclear. In this paper, a biologically plausible latent variable model was first fit to neural activity recorded via 2-photon microscopic calcium imaging in the murine primary visual cortex. Graph theoretic measures were then applied to quantify network properties in the recorded sub-regions. Comparison of weighted network measures with LVM prediction accuracy shows some network measures having a strong relationship with LVM prediction accuracy, while other measures do not have a robust relationship with LVM prediction accuracy. Results show LVM will achieve high accuracy in dense networks.