  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.
