Serotonergic neurons in the raphe nuclei exhibit diverse electrophysiological properties and functional roles, yet conventional identification methods rely on restrictive criteria that likely overlook atypical serotonergic cells. The use of convolutional neural network (CNN) for comprehensive classification of both typical and atypical serotonergic neurons is an interesting one, but the key challenge is often given by the limited experimental data available for training. This study presents a procedure for synthetic data generation that combines smoothed spike waveforms with heterogeneous noise masks from real recordings. This approach expanded the training set while mitigating overfitting of background noise signatures. CNN models trained on the augmented dataset achieved high accuracy (96.2% true positive rate, 88.8% true negative rate) on non-homogeneous test data collected under different experimental conditions than the training, validation and testing data.

翻译：中缝核中的血清素能神经元展现出多样化的电生理特性和功能作用，然而传统鉴定方法依赖的限制性标准很可能遗漏非典型血清素能细胞。使用卷积神经网络（CNN）对典型与非典型血清素能神经元进行综合分类具有重要价值，但关键挑战通常在于可用于训练的实验数据有限。本研究提出一种合成数据生成方法，将平滑的尖峰波形与来自真实记录的异质性噪声掩模相结合。该方法在扩展训练集的同时，缓解了背景噪声特征的过拟合问题。在增强数据集上训练的CNN模型，在不同于训练、验证和测试数据的实验条件下收集的非均匀测试数据上取得了高准确率（真阳性率96.2%，真阴性率88.8%）。