Self-supervised Contrastive Graph Views for Learning Neuron-Level Circuit Network

Junchi Li, Guojia Wan, Minghui Liao, Fei Liao, Bo Du

Abstract: Learning Neuron-level Circuit Network can be used on automatic neuron classification and connection prediction, both of which are fundamental tasks for connectome reconstruction and deciphering brain functions. Traditional approaches to this learning process have relied on extensive neuron typing and labor-intensive proofread. In this paper, we introduce FlyGCL, a self-supervised learning approach designed to automatically learn neuron-level circuit networks, enabling the capture of the connectome’s topological feature. Specifically, we leverage graph augmentation methods to generate various contrastive graph views. The proposed method differentiates between positive and negative samples in these views, allowing it to encode the structural representation of neurons as adaptable latent features that can be used for downstream tasks such as neuron classification and connection prediction. To evaluate our method, we construct two new Neuron-level Circuit Network datasets, named HemiBrain-C and Manc-C, derived from the FlyEM project. Experimental results show that FlyGCL attains neuron classification accuracies of 73.8% and 57.4%, respectively, with >0.95 AUC in connection prediction tasks. Our code and data are available at GitHub Repository https://github.com/mxz12119/FlyGCL.