The serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be investigated for their functional properties are commonly identified on the basis of "typical" features of their activity, i.e. slow regular firing and relatively long duration of action potentials. Thus, due to the lack of equally robust criteria for discriminating serotonergic neurons with "atypical" features from non-serotonergic cells, the physiological relevance of the diversity of serotonergic neuron activities results largely understudied. We propose deep learning models capable of discriminating typical and atypical serotonergic neurons from non-serotonergic cells with high accuracy. The research utilized electrophysiological in vitro recordings from serotonergic neurons identified by the expression of fluorescent proteins specific to the serotonergic system and non-serotonergic cells. These recordings formed the basis of the training, validation, and testing data for the deep learning models. The study employed convolutional neural networks (CNNs), known for their efficiency in pattern recognition, to classify neurons based on the specific characteristics of their action potentials.

翻译：血清素能系统通过功能不同的神经元亚群调节大脑过程，这些亚群具有异质性特征，包括其电生理活动。在细胞外记录中，用于研究功能特性的血清素能神经元通常基于其活动的"典型"特征（即缓慢规律性放电和相对较长的动作电位时程）进行识别。因此，由于缺乏同等稳健的标准来区分具有"非典型"特征的血清素能神经元与非血清素能细胞，血清素能神经元活动多样性的生理相关性在很大程度上仍未得到充分研究。我们提出了能够以高准确度区分典型与非典型血清素能神经元及非血清素能细胞的深度学习模型。该研究利用了通过表达血清素能系统特异性荧光蛋白所识别的血清素能神经元和非血清素能细胞的体外电生理记录。这些记录构成了深度学习模型训练、验证和测试数据的基础。研究采用了以模式识别效率著称的卷积神经网络（CNN），根据动作电位的特定特征对神经元进行分类。