Self-refreshing Som as a Semantic Memory Model

Natural and artificial cognitive systems suffer from forgetting information. However, in natural systems forgetting is typically gradual whereas in artificial systems forgetting is often catastrophic. Catastrophic forgetting is also a problem for the Self-Organizing Map (SOM) when used as a semantic memory model in a continuous learning task in a nonstationary environment. Methods based on rehearsal and pseudorehearsal have been successfully applied in feedforward networks to avoid catastrophic interference. A novel method based on pseudorehearsal for avoiding catastrophic forgetting in the SOM is presented. Simulations comparing the performance of a self-refreshing SOM compared to a standard SOM are presented in the task of learning three separate sets of data adjacently with results showing that the use of pseudorehearsal can effectively decrease catastrophic forgetting.