In a general case, it may occur that there are multiple re-entrant circuits present in a single tissue. To test our methods for this scenario we repeated the procedure outlined at the start of this section with a simple change that two re-entrant circuits are randomly placed in the tissue (with a minimum separation of 10 cells vertically to avoid overlap). We then adjust the search algorithm to start looking for a second re-entrant circuit after the first is found. However, note that the machine learning models used are still only trained on simulations with a single re-entrant circuit – the flexibility of our model to search for multiple drivers without explicitly learning to do so is one of the major benefits of our approach. The adaptation to multiple drivers is possible because of the limited interference in the electrical activity between competing drivers. In a system with two stable drivers, tissue closer to the first driver exhibits electrical activity which can be closely approximated by the activity one would expect if the second driver was not present. This principle can be implemented in our algorithm as outlined in Fig. \[fig:multisearch\] where the extension to tissues with multiple drivers is 