Semiparametric spectral modeling of the Drosophila connectome
This work addresses network modeling for the Drosophila connectome, which is incremental as it builds on existing stochastic block models and random dot product graphs.
The researchers tackled the problem of modeling the larval Drosophila mushroom body connectome by introducing the latent structure model (LSM), a generalization of the stochastic block model, and applied semiparametric Gaussian mixture modeling in the adjacency spectral embedding space to capture latent structure and elucidate biologically relevant neuronal properties.
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.