Editing Spin glass (section)

== Applications ==
The formalism of replica mean-field theory has also been applied in the study of [[Artificial neural network|neural networks]], where it has enabled calculations of properties such as the storage capacity of simple neural network architectures without requiring a training algorithm (such as [[backpropagation]]) to be designed or implemented.<ref name="Gardner">{{cite journal |last1=Gardner |first1=E |last2=Deridda |first2=B |date=7 January 1988 |title=Optimal storage properties of neural network models |url=https://hal.archives-ouvertes.fr/hal-03285587/file/Optimal%20storage%20properties%20of%20neural%20network%20models.pdf |journal=J. Phys. A |volume=21 |pages=271 |bibcode=1988JPhA...21..271G |doi=10.1088/0305-4470/21/1/031 |number=1}}</ref>

More realistic spin glass models with short range frustrated interactions and disorder, like the Gaussian model where the couplings between neighboring spins follow a [[Gaussian distribution]], have been studied extensively as well, especially using [[Monte Carlo simulation]]s. These models display spin glass phases bordered by sharp [[phase transition]]s.

Besides its relevance in condensed matter physics, spin glass theory has acquired a strongly interdisciplinary character, with applications to [[Artificial neural network|neural network]] theory, computer science, theoretical biology, [[econophysics]] etc.

Spin glass models were adapted to the [[folding funnel]] model of [[protein folding]].