The paper describes a machine‑learning method for sorting lab‑grown brain cell networks into healthy or diseased groups based on their electrical activity. It doesn’t involve the peptide humanin or suggest any health‑related protocol, so it offers no practical guidance for biohackers or longevity enthusiasts.

<i>Objective.</i>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<i>in vitro</i>neural networks with and without an underlying pathology, from electrophysiological recordings obtained using a microelectrode array (MEA) platform.<i>Approach.</i>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<i>in vitro</i>neural networks.<i>Main Results.</i>We found a high degree of separability between the classes using DM point process features (p-value &lt;0.001 for all the features, paired t-test), which reaches 93.10 of accuracy (92.37 of ROC AUC) with the Random Forest classifier. In particular, results show a higher latency in firing for pathologically perturbed neurons (43&#xa0;&#xb1;&#xa0;16 ms versus 67&#xa0;&#xb1;&#xa0;31 ms,&#x3bc;IGfeature distribution).<i>Significance.</i>Our approach has been successful in extracting temporal features related to the neurons' behaviour, as well as distinguishing healthy from pathologically perturbed networks, including classification of responses to a transient induced perturbation.