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Determining the types of neurons within a nervous system plays a significantrole in the analysis of brain connectomics and the investigation ofneurological diseases. However, the efficiency of utilizing anatomical,physiological, or molecular characteristics of neurons is relatively low andcostly. With the advancements in electron microscopy imaging and analysistechniques for brain tissue, we are able to obtain whole-brain connectomeconsisting neuronal high-resolution morphology and connectivity information.However, few models are built based on such data for automated neuronclassification. In this paper, we propose NeuNet, a framework that combinesmorphological information of neurons obtained from skeleton and topologicalinformation between neurons obtained from neural circuit. Specifically, NeuNetconsists of three components, namely Skeleton Encoder, Connectome Encoder, andReadout Layer. Skeleton Encoder integrates the local information of neurons ina bottom-up manner, with a one-dimensional convolution in neural skeleton'spoint data; Connectome Encoder uses a graph neural network to capture thetopological information of neural circuit; finally, Readout Layer fuses theabove two information and outputs classification results. We reprocess andrelease two new datasets for neuron classification task from volume electronmicroscopy(VEM) images of human brain cortex and Drosophila brain. Experimentson these two datasets demonstrated the effectiveness of our model with accuracyof 0.9169 and 0.9363, respectively. Code and data are available at:https://github.com/WHUminghui/NeuNet.

Joint Learning Neuronal Skeleton and Brain Circuit Topology with Permutation Invariant Encoders for Neuron Classification