Semiparametric spectral modeling of the Drosophila connectome
Machine Learning
2017-05-10 v1
Abstract
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.
Keywords
Cite
@article{arxiv.1705.03297,
title = {Semiparametric spectral modeling of the Drosophila connectome},
author = {Carey E. Priebe and Youngser Park and Minh Tang and Avanti Athreya and Vince Lyzinski and Joshua T. Vogelstein and Yichen Qin and Ben Cocanougher and Katharina Eichler and Marta Zlatic and Albert Cardona},
journal= {arXiv preprint arXiv:1705.03297},
year = {2017}
}