Power Function Forgetting Curves as an Emergent Property of Biologically Plausible Neural Network Models

Empirical forgetting curve data have been shown to follow a power function. In contrast, many connectionist models predict either an exponential decay or flat forgetting curves. This paper simulates power function forgetting curves in a Hopfield network modified to incorporate the more biologically realistic assumptions of bounded weights and a distribution of learning rates. The modified model produces power function forgetting curves. The bounded weights introduce exponential decay for individual weights, and a power function forgetting curve when summing exponential decays with different learning rates. Because these assumptions are biologically reasonable, power function forgetting curves may be an emergent property of biological networks. The results fit empirical data and indicate that forgetting curves restrict possible implementation of models of memory.