A simple oscillatory short-term memory
Oscillatory neural network models have been an increasing focus of study over the last several years. These models consist of recurrent neural networks whose dynamics are characterized by persistent learned/designed rhythmic activity. Here we consider simple oscillatory memories for short-term retention of items occurring as temporal sequences. By incorporating decay as well as interference, we find that it is easy to match behavioral data from human subjects recalling temporal sequences under different situations by adjusting a single parameter in the model. These results suggest that simple oscillatory memories capture at least some key properties of human short-term memory, and might be used effectively in future biologically-inspired cognitive architectures.