Combinatorial Threshold-Linear Networks (CTLNs) are a simplified way to represent complex neural networks and can help simulate brain activity. The CTLN model consists of a set of ordinary differential equations, and their solutions give the firing rates of each neuron. When you plot these firing rates, you often see a pattern of neurons firing in sequence. The goal of this research is to predict this firing sequence based on the network's structure. We make these predictions by analyzing the network's graph and studying how the nodes (neurons) are related to each other.
