title: Stochastic Thermodynamics of Learning.
abstract: Virtually every organism gathers information about its noisy environment and builds models from those data, mostly using neural networks. Here, we use stochastic thermodynamics to analyze the learning of a classification rule by a neural network. We show that the information acquired by the network is bounded by the thermodynamic cost of learning and introduce a learning efficiency η≤1. We discuss the conditions for optimal learning and analyze Hebbian learning in the thermodynamic limit.
authors.affiliation: II. Institut für Theoretische Physik, Universität Stuttgart, 70550 Stuttgart, Germany.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Goldt
authors.affiliation: II. Institut für Theoretische Physik, Universität Stuttgart, 70550 Stuttgart, Germany.
authors.forename: Udo
authors.initials: U
authors.lastname: Seifert