Synthetic Data for Discriminating Serotonergic Neurons using Convolutional Neural Networks

TL;DR

This paper introduces a synthetic data generation method to improve CNN-based classification of serotonergic neurons, achieving high accuracy even with limited real data and diverse experimental conditions.

Contribution

The study presents a novel synthetic data augmentation technique that enhances CNN training for neuron classification, addressing data scarcity and variability issues.

Findings

CNN achieved 96.2% true positive rate

CNN achieved 88.8% true negative rate

Effective in diverse experimental conditions

Abstract

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.