Semiparametric spectral modeling of the Drosophila connectome

Department of Applied Mathematics and Statistics
Center for Imaging Science
Human Language Technology Center of Excellence
Johns Hopkins University
University of Cincinnati
HHMI Janelia Research Campus

C.E. Priebe, Y. Park, M. Tang, A, Athreya, V. Lyzinski, J. Vogelstein, Y. Qin, B. Cocanougher, K. Eichler, M. Zlatic, A. Cardona, “Semiparametric spectral modeling of the Drosophila connectome,” Journal of the American Statistical Association Application and Case Studies, submmitted, 2017.

More comprehensive version was published in

Avanti Athreya, Donniell E. Fishkind, Keith Levin, Vince Lyzinski, Youngser Park, Yichen Qin, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe, "Statistical inference on random dot product graphs: a survey," Journal of Machine Learning Research, 18(226):1−92, 2018.


We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. The resulting connectome code derived via semiparametric Gaussian mixture mod- eling composed with adjacency spectral embedding captures biologically relevant neuronal prop- erties.

Keywords: Connectome; Network; Graph; Spectral embedding; Mixture model; Clustering