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DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction

Name: DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction
Creator: Feng Xiong

Research datakeyboard_double_arrow_right Dataset 26 Jul 2023Publisher:Zenodo

Authors: Feng Xiong;

doi: 10.5281/zenodo.8186904 , 10.5281/zenodo.8188431 , 10.5281/zenodo.10258371 , 10.5281/zenodo.8186905

DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction

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Abstract

This is an open-source repository for hosting codes and data files from research, "DSM: Deep Sequential Model for Complete Neuronal Morphology Representation and Feature Extraction". We also provided a web service based on our methods, please go to http://114.117.165.134:8501/. (1)raw_dataset.zip: 1,282 neuron reconstructions from SEU-Allen dataset; 1,002 neuron reconstructions from Janelia dataset; 1,100 neuron reconstructions from ION dataset. (2)Supplementary.zip: Supplementary information, including tables and figures; (3)DSM-tools.zip: A python package for converting neuron morphology into sequences and implementing DSM models. NeuronSequenceDataset class: to transform SWC files to sequence dataframes by binary tree traversals, and prepare for the input of DSM networks. DSMDataConverter class: a helper to convert the sequence dataframes for classification and clustering. DSMHierarchicalAttentionNetwork class: classification model, giving a pre-trained DSM-HAN model by default. DSMAutoencoder class: clustering model, giving a pre-trained DSM-AE model by default. (4)neuron2seq_for_developer.zip: A repository for further development of the models, including source codes and all data files.

Related Organizations

Southeast University
China (People's Republic of)

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