Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: The models were trained on a dataset comprising 27,108 original action potential samples, alongside an extensive set of 12 million synthetic action potential samples, designed to mitigate the risk of overfitting the background noise in the recordings, a potential source of bias. Results show that the models achieved high accuracy and were further validated on \"non-homogeneous\" data, i.e., data unknown to the model and collected on different days from those used for the training of the model, to confirm their robustness and reliability in real-world experimental conditions.
METHODS: Conventional methods for identifying serotonergic neurons allow recognition of serotonergic neurons defined as typical. Our model based on the analysis of the sole action potential reliably recognizes over 94% of serotonergic neurons including those with atypical features of spike and activity.
CONCLUSIONS: The model is ready for use in experiments conducted with the here described recording parameters. We release the codes and procedures allowing to adapt the model to different acquisition parameters or for identification of other classes of spontaneously active neurons.
METHODS: 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.
RESULTS: The models were trained on a dataset comprising 27,108 original action potential samples, alongside an extensive set of 12 million synthetic action potential samples, designed to mitigate the risk of overfitting the background noise in the recordings, a potential source of bias. Results show that the models achieved high accuracy and were further validated on \"non-homogeneous\" data, i.e., data unknown to the model and collected on different days from those used for the training of the model, to confirm their robustness and reliability in real-world experimental conditions.
METHODS: Conventional methods for identifying serotonergic neurons allow recognition of serotonergic neurons defined as typical. Our model based on the analysis of the sole action potential reliably recognizes over 94% of serotonergic neurons including those with atypical features of spike and activity.
CONCLUSIONS: The model is ready for use in experiments conducted with the here described recording parameters. We release the codes and procedures allowing to adapt the model to different acquisition parameters or for identification of other classes of spontaneously active neurons.
摘要：
背景：5-羟色胺能系统通过功能上不同的具有异质性的神经元亚群调节大脑过程，包括他们的电生理活动。在细胞外记录中，要研究其功能特性的血清素能神经元通常根据其活动的“典型”特征进行鉴定，即缓慢的定期发射和相对较长的动作电位持续时间。因此，由于缺乏同样强大的标准来区分具有“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未得到研究。
方法：我们提出了深度学习模型，能够以高精度区分典型和非典型的血清素能神经元与非血清素能细胞。该研究利用了通过对5-羟色胺能系统和非5-羟色胺能细胞特异的荧光蛋白的表达鉴定的5-羟色胺能神经元的电生理体外记录。这些录音构成了训练的基础，验证,和深度学习模型的测试数据。这项研究采用了卷积神经网络(CNN)，以模式识别的效率而闻名，根据其动作电位的具体特征对神经元进行分类。
结果：在包含27,108个原始动作电位样本的数据集上训练模型，除了大量的1200万个合成动作电位样本，旨在减轻录音中背景噪声过度拟合的风险，潜在的偏见来源。结果表明，该模型具有较高的准确性，并在“非均匀”数据上得到了进一步验证，即，模型未知的数据，并且在与用于训练模型的日期不同的日期收集，以确认它们在现实实验条件下的鲁棒性和可靠性。
方法:用于鉴定5-羟色胺能神经元的常规方法允许识别定义为典型的5-羟色胺能神经元。我们的模型基于对唯一动作电位的分析，可靠地识别了超过94％的5-羟色胺能神经元，包括具有尖峰和活动非典型特征的神经元。
结论：该模型已准备好用于使用此处描述的记录参数进行的实验。我们发布了代码和程序，可以使模型适应不同的采集参数或识别其他类型的自发活动神经元。
方法：我们提出了深度学习模型，能够以高精度区分典型和非典型的血清素能神经元与非血清素能细胞。该研究利用了通过对5-羟色胺能系统和非5-羟色胺能细胞特异的荧光蛋白的表达鉴定的5-羟色胺能神经元的电生理体外记录。这些录音构成了训练的基础，验证,和深度学习模型的测试数据。这项研究采用了卷积神经网络(CNN)，以模式识别的效率而闻名，根据其动作电位的具体特征对神经元进行分类。
结果：在包含27,108个原始动作电位样本的数据集上训练模型，除了大量的1200万个合成动作电位样本，旨在减轻录音中背景噪声过度拟合的风险，潜在的偏见来源。结果表明，该模型具有较高的准确性，并在“非均匀”数据上得到了进一步验证，即，模型未知的数据，并且在与用于训练模型的日期不同的日期收集，以确认它们在现实实验条件下的鲁棒性和可靠性。
方法:用于鉴定5-羟色胺能神经元的常规方法允许识别定义为典型的5-羟色胺能神经元。我们的模型基于对唯一动作电位的分析，可靠地识别了超过94％的5-羟色胺能神经元，包括具有尖峰和活动非典型特征的神经元。
结论：该模型已准备好用于使用此处描述的记录参数进行的实验。我们发布了代码和程序，可以使模型适应不同的采集参数或识别其他类型的自发活动神经元。
