## 5 Conclusions  
The classification of neurons based on their morphological structures is an important way to thoroughly understand brain information processing. This paper proposed a neuronal morphology classification approach based on the LCCDNNs. We first constructed two 3D neuron datasets including the C. elegans and zebrafish datasets, and 43 geometric features were extracted to classify the types of neurons. Then, by using the theory of the residually connected deep network model, we proposed two  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Classification Approach**} & \multicolumn{2}{c}{**Classification Accuracy**} \\ \cline{3-4}  & & **Training Set (\%)** & **Test Set (\%)** \\ \hline \multirow{8}{*}{C. elegans} & LR & 87.52 \(\pm\) 1.0 & 79.23 \(\pm\) 8.24 \\  & NB & 80.85 \(\pm\) 2.94 & 57.18 \(\pm\) 10.54 \\  & DT & 100 & 63.85 \(\pm\) 11.50 \\  & KNN & 88.04 \(\pm\) 1.2 & 78.72 \(\pm\) 4.53 \\  & SVM & 94.18 \(\pm\) 1.55 & 80.77 \(\pm\) 10.76 \\  & LSTM & 99.93 \(\pm\) 0.21 & 68.46 \(\pm\) 9.97 \\  & FCDNN-30 & 89.02 \(\pm\) 9.13 & 81.35 \(\pm\) 15.14 \\  & RCDNN-10 & 97.71 \(\pm\) 4.19 & 85.9 \(\pm\) 9.15 \\  & LCCDNN-30 & 98.03 \(\pm\) 2.89 & 87.44 \(\pm\) 4.09 \\  & FCCDNN-3 & 97.45 \(\pm\) 4.14 & 83.33 \(\pm\) 9.76 \\ \hline \multirow{8}{*}{Zebrafish} & LR & 93.14 \(\pm\) 1.12 & 74.65 \(\pm\) 8.52 \\  & NB & 87.63 \(\pm\) 1.38 & 46.28 \(\pm\) 16.09 \\ \cline{1-1}  & DT & 100 & 64.65 \(\pm\) 11.44 \\ \cline{1-1}  & KNN & 88.34 \(\pm\) 1.15 & 76.51 \(\pm\) 5.95 \\ \cline{1-1}  & SVM & 96.27 \(\pm\) 0.97 & 71.16 \(\pm\) 8.29 \\ \cline{1-1}  & LSTM & 97.81 \(\pm\) 0.74 & 70.47 \(\pm\) 0.74 \\ \cline{1-1}  & FCDNN-20 & 75.27 \(\pm\) 10.21 & 75.12 \(\pm\) 9.81 \\ \cline{1-1}  & RCDNN-40 & 93.79 \(\pm\) 6.32 & 78.14 \(\pm\) 8.15 \\ \cline{1-1}  & LCCDNN-25 & 98.16 \(\pm\) 1.88 & 81.16 \(\pm\) 9.2 \\ \cline{1-1}  & FCCDNN-50 & 94.02 \(\pm\) 6.06 & 72.33 \(\pm\) 6.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the accuracy of the classifiers.  
cumulative connected deep neural networks, the LCCDNN and FCCDNN. Finally, the proposed deep learning networks were applied to a 3D neuron classification problem. In the experiments, through the analysis of the influence of relevant parameters, the performance of the better model was obtained. We also compared the classification performance of the LCCDNN approach with other mainstream machine learning approaches, which can learn and classify effective features for two different animal neuron datasets. However, these morphology-based classification approaches largely rely on the feature extraction and selection techniques of neuronal spatial structures, so a lot of useful information for neuronal classification may be lost. Future research will be based on 3D neuron data without feature extraction to study the classification of neurons.  
Conceptualization, X.L. and J.Z.; Data Curation, J.Z.; Funding Acquisition, X.L.; Methodology, X.L.; Software, J.Z.; Writing-Original Draft, J.Z.; Writing-Review & Editing, X.L.  
The work is supported by the National Natural Science Foundation of China (grant no. 61762080); the Lanzhou Municipal Science and Technology Project (grant no. 2019-1-34), and the Program for Innovative Research Team in Northwest Normal University (grant no. 6008-01602).  
The authors declare no conflicts of interest.