Synthetic Data for Discriminating Serotonergic Neurons using Convolutional Neural Networks

Autor:	Corradetti, Daniele, Bernardi, Alessandro, Corradetti, Renato
Rok vydání:	2024
Předmět:	Quantitative Biology - Neurons and Cognition Computer Science - Computational Engineering Finance and Science
Druh dokumentu:	Working Paper
Popis:	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. Comment: Contribution for EPIA 2024 Progress in Artificial Intelligence
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.05701 Zobrazit plný text záznamu View this record from Arxiv