Information Rate

Information rate quantifies the amount of information transmitted or processed within a system, a crucial concept across diverse fields like machine learning and neuroscience. Current research focuses on refining information rate measures for complex systems, including developing new metrics for temporal data and addressing limitations in existing models like the "oversquashing" problem in neural networks. These advancements are improving the analysis of information flow in various contexts, from causal inference in stochastic processes to evaluating the performance of brain-computer interfaces and optimizing machine learning algorithms. Ultimately, a better understanding of information rate leads to more efficient and effective systems in both theoretical and applied settings.