  Determining the types of neurons within a nervous system plays a significant
role in the analysis of brain connectomics and the investigation of
neurological diseases. However, the efficiency of utilizing anatomical,
physiological, or molecular characteristics of neurons is relatively low and
costly. With the advancements in electron microscopy imaging and analysis
techniques for brain tissue, we are able to obtain whole-brain connectome
consisting neuronal high-resolution morphology and connectivity information.
However, few models are built based on such data for automated neuron
classification. In this paper, we propose NeuNet, a framework that combines
morphological information of neurons obtained from skeleton and topological
information between neurons obtained from neural circuit. Specifically, NeuNet
consists of three components, namely Skeleton Encoder, Connectome Encoder, and
Readout Layer. Skeleton Encoder integrates the local information of neurons in
a bottom-up manner, with a one-dimensional convolution in neural skeleton's
point data; Connectome Encoder uses a graph neural network to capture the
topological information of neural circuit; finally, Readout Layer fuses the
above two information and outputs classification results. We reprocess and
release two new datasets for neuron classification task from volume electron
microscopy(VEM) images of human brain cortex and Drosophila brain. Experiments
on these two datasets demonstrated the effectiveness of our model with accuracy
of 0.9169 and 0.9363, respectively. Code and data are available at:
https://github.com/WHUminghui/NeuNet.
