Title: Semiparametric spectral modeling of the Drosophila connectome,
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.