Deep learning models for atypical serotonergic cells recognition

• Main Author: Corradetti, Daniele
• Publication Date: 2024
• Other Authors: Bernardi, Alessandro, Corradetti, Renato
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.1/25883
• topic: Deep learning models; Serotonergic neurons; Convolutional neural networks; Dorsal raphe nucleus; Spike recognition

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. New 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. Comparison with existing 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. Conclusion: 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.