Bayesian supervised machine learning classification of neural networks with pathological perturbations
| Autor: | Axel Sandvig, Riccardo Levi, Salvatore Castelbuono, Vibeke Devold Valderhaug, Riccardo Barbieri, Ioanna Sandvig |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
business.industry Computer science Bayesian probability Pattern recognition Statistical model Bayes Theorem Neurophysiology multi electrode array Point process Random forest Machine Learning Statistical classification in vitro neural networks Feature (machine learning) Artificial intelligence Neural Networks Computer Supervised Machine Learning neurophysiology business General Nursing Algorithms point process |
| Zdroj: | Biomedical physicsengineering express. 7(6) |
| ISSN: | 2057-1976 |
| Popis: | Objective Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the classification of human in vitro neural networks with and without an underlying pathology, from electrophysiological recordings obtained using a microelectrode array (MEA) platform. Approach We developed a Dirichlet mixture (DM) Point Process statistical model able to extract temporal features related to neurons. We then applied a machine learning algorithm to discriminate between healthy control and pathologically perturbed in vitro neural networks. Main Results We found a high degree of separability between the classes using DM point process features (p-value |
| Databáze: | OpenAIRE |
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