Theoretical bound of the efficiency of learning with coarse-graining

Abstract

A thermodynamic formalism describing the efficiency of information learning is proposed, which is applicable for stochastic thermodynamic systems with multiple internal degree of freedom. The learning rate, entropy production rate (EPR), and entropy flow from the system to the environment under coarse-grained dynamics are derived. The Cauchy-Schwarz inequality has been applied to demonstrate the lower bound on the EPR of an internal state. The inequality of EPR is tighter than the Clausius inequality, leading to the derivative of the upper bound on the efficiency of learning. The results are verified in cellular networks with information processes.