Equilibrium is Inference - Lessons from Liquid-Liquid Phase Separation Models

Watch a thought-provoking lecture exploring the relationship between equilibrium behaviors and information processing in biological systems, delivered at IPAM's Naturalistic Approaches to Artificial Intelligence Workshop. Delve into how equilibrium models of neural computation demonstrate the capacity of physical systems to perform complex information processing tasks, including probability distribution representation and conditional probability inference. Examine the application of these principles to programmable molecular systems at equilibrium, focusing on well-mixed chemical reaction networks and liquid-liquid phase separation. Through this 53-minute presentation, gain insights into the underappreciated role of equilibrium behaviors in living systems while understanding their scope in relation to non-equilibrium dynamics. Learn from California Institute of Technology expert Erik Winfree as he bridges the gap between physical equilibrium states and computational inference in biological systems.