Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Associative memory with dynamic synapses
Neural Computation
A Cortical Mechanism for Binding in Visual Working Memory
Journal of Cognitive Neuroscience
Parallel activation of memories in an oscillatory neural network
Neural Computation
Emergent synchrony in locally coupled neural oscillators
IEEE Transactions on Neural Networks
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Recurrent neural architectures having oscillatory dynamics use rhythmic network activity to represent patterns stored in short-term memory. Multiple stored patterns can be retained in memory over the same neural substrate because the network's state persistently switches between them. Here we present a simple oscillatory memory that extends the dynamic threshold approach of Horn and Usher (1991) by including weight decay. The modified model is able to match behavioral data from human subjects performing a running memory span task simply by assuming appropriate weight decay rates. The results suggest that simple oscillatory memories incorporating weight decay capture at least some key properties of human short-term memory. We examine the implications of the results for theories about the relative role of interference and decay in forgetting, and hypothesize that adjustments of activity decay rate may be an important aspect of human attentional mechanisms.