Construction of a Neural Network with Temperature-Dependent Recall Patterns

Abstract

We present a simple model that recalls two different patterns depending on the temperature. To realize a change in recall pattern due to temperature change, we embed two patterns to different graphs: the first pattern into a fully connected graph and the second pattern into a sparse graph. Because a fully connected graph is more resistant to thermal fluctuations than a sparse graph, we can realize a change in recall pattern by tuning relative weights of the two patterns properly. We demonstrate by equilibrium Monte-Carlo simulations that such a temperature-dependent change in recall patterns does occur in our model. Simulation results strongly indicate that the system undergoes a first-order phase transition when the change in recall patterns occurs. It is also demonstrated by annealing simulations that the system fails to recall the pattern embedded in the sparse graph at low temperatures if the free-energy barrier is too high to overcome within the given simulation timescale.