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 . We discuss the conditions for optimal learning and analyze Hebbian learning in the thermodynamic limit.
- Received 11 July 2016
DOI:https://doi.org/10.1103/PhysRevLett.118.010601
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