Semiparametric spectral modeling of the Drosophila connectome

TL;DR

This paper introduces a semiparametric spectral modeling approach for the complete Drosophila larval connectome, leveraging Gaussian mixture modeling in the spectral embedding space to uncover biologically relevant neuronal structures.

Contribution

It proposes the latent structure model (LSM), a generalization of SBM and RDPG, enabling semiparametric GMM analysis of neural connectome data.

Findings

Capture of latent connectome structure

Revelation of biologically relevant neuronal properties

Enhanced modeling flexibility for neural networks

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.