Boolean Network

Boolean networks are mathematical models representing systems with discrete, interacting components, often used to model gene regulatory networks or other complex systems. Current research focuses on improving the efficiency and accuracy of Boolean network models, including developing novel architectures like Boolean product graph neural networks and enhancing deep Boolean networks through techniques such as skip connections and optimized sampling. These advancements aim to bridge the performance gap between Boolean networks and traditional neural networks, offering computationally efficient alternatives for applications in AI and biological modeling, particularly in resource-constrained environments like edge AI. The ability to effectively analyze and manipulate Boolean networks also holds significant promise for advancing the understanding and control of complex systems.