In feedback neural networks, especially for static pattern learning, a reliable method of settling is required. Simulated annealing has been used but it is often difficult to determine how to set the annealing schedule. Often the specific heat is used as a measure of when to slow down the annealing process, but this is difficult to measure. We propose another measure, volatility, which is easy to measure and related to the Edwards-Anderson model in spin-glass physics. This paper presents the concept of volatility, an argument for its similarity to specific heat, simulations of dynamics in Boltzmann and mean-field networks, and a method of using it to speed up learning.