Neuronal classification from network connectivity via adjacency spectral embedding.

ABSTRACT: This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) in which neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (≈2¹²-2¹⁵ neurons), sparse

SUBMITTER: Mehta K 

PROVIDER: S-EPMC8567830 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature