基于深度学习网络的神经元自适应投影分类方法

A Neuronal Classification Approach with Adaptive Projection Using Deep Learning Networks

传统的基于几何形态的神经元分类方法依赖于神经元空间结构特征的提取与选择,会损失大量有用的神经元分类信息.应用自适应投影算法将三维神经元进行转换,不需要提取神经元的几何特征,提出了一种基于深度学习网络的神经元几何形态分类方法.该方法将原始神经元数据进行三维体素重建,经过自适应投影过程构成二维神经元图像数据,并构建了基于双卷积门限循环神经网络的深度学习模型对神经元进行分类.将该方法应用于三种神经元分类数据集,通过与基于特征提取的神经元分类方法相比,实验结果表明该方法具有更高的分类准确率和良好的适应能力.

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.