Deep Learning Models for Atypical Serotonergic Cells Recognition

TL;DR

This study develops deep learning models, specifically CNNs, to accurately distinguish atypical serotonergic neurons from non-serotonergic cells using electrophysiological data, addressing a gap in neuron classification.

Contribution

The paper introduces a novel application of deep learning to classify serotonergic neuron subtypes with atypical features, improving identification accuracy.

Findings

High classification accuracy achieved

Deep learning outperforms traditional methods

Effective discrimination of atypical serotonergic neurons

Abstract

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.