Traditional morphology-based neuronal classification approaches largely rely on the feature extraction and selection techniques of neuronal spatial structures

a lot of useful information for neuronal classification may be lost. Using the adaptive projection algorithm to convert the three-dimensional neuron data without feature extraction

this paper proposes a neuronal morphology classification approach based on deep learning networks. The three-dimensional voxel reconstruction is used for the original neuron data

and the two-dimensional neuron data is generated through adaptive projection process. Then

the deep learning model of double convolutional gated recurrent neural networks is established to classify neurons. The proposed approach is successfully applied to three neuronal classification datasets

the experiment results show that the proposed method has higher classification accuracy and flexibility than the neuronal classification methods based on feature extraction.