Weighted Network Density Predicts Range of Latent Variable Model Accuracy

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
Pages2414-2417
ISBN (Print)9781538636466
Publication statusPublished - Jul 2018

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2018-July
ISSN (Print)1557-170X

Conference

Title40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC' 18)
LocationHonolulu, Hawaii
PlaceUnited States
CityHonolulu, Hawaii
Period17 - 21 July 2018

Abstract

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.

Citation Format(s)

Weighted Network Density Predicts Range of Latent Variable Model Accuracy. / Palmerston, Jeremiah B.; She, Qi; Chan, Rosa H. M.

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. p. 2414-2417 8512738 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2018-July).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review