Figure 1.

Schematic depiction of our workflow. After collecting the raw MEA data, we feed them into three different workflows: we post-process the data either into the form of spikes or averaged signal (over 30 or 100 time bins). We follow by training the neural network specific to each of the inputs (these models are referred to as spikes30, signal30, and signal100) to output key parameters for the MaxInterval method: maximum interval to start and end the reverberations and minimal time between the reverberations. These parameters predicted by each machine learning model are then used for MaxInterval method that predicts the reverberations that are combined into bursts. An example of the plate type from Multichannel Systems, MCS GmbH, Reutlingen, Germany, used for experiments included in this study, is depicted in this figure.