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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

March 2009, Vol. 21, No. 3, Pages 741-761
(doi: 10.1162/neco.2008.02-08-715)
© 2008 Massachusetts Institute of Technology
An Oscillatory Hebbian Network Model of Short-Term Memory
Article PDF (1.22 MB)
Abstract

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.