RT Journal Article
SR Electronic
T1 Weighted Network Density Predicts Range of Latent Variable Model Accuracy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 343285
DO 10.1101/343285
A1 Jeremiah B. Palmerston
A1 Qi She
A1 Rosa H. M. Chan
YR 2018
UL http://biorxiv.org/content/early/2018/06/11/343285.abstract
AB 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.