A working memory model based on fast Hebbian learning

Recent models of the oculomotor delayed response task have been based on the assumption that working memory is stored as a persistent activity state (a 'bump' state). The delay activity is maintained by a finely tuned synaptic weight matrix producing a line attractor. Here we present an al...

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Bibliographic Details
Published inNetwork (Bristol) Vol. 14; no. 4; p. 789
Main Authors Sandberg, A., Tegner, J., Lansner, Anders
Format Journal Article
LanguageEnglish
Published 2003
Subjects
associative memory
attractor network
dynamics
long-term potentiation
mechanisms
Medicin och hälsovetenskap
prefrontal cortex
recurrent network
spiking neurons
synapses
TECHNOLOGY
TEKNIKVETENSKAP
visual-cortex
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Summary:Recent models of the oculomotor delayed response task have been based on the assumption that working memory is stored as a persistent activity state (a 'bump' state). The delay activity is maintained by a finely tuned synaptic weight matrix producing a line attractor. Here we present an alternative hypothesis, that fast Hebbian synaptic plasticity is the mechanism underlying working memory. A computational model demonstrates a working memory function that is more resistant to distractors and network inhomogeneity compared to previous models, and that is also capable of storing multiple memories.
ISSN:0954-898X
1361-6536
DOI:10.1088/0954-898X/14/4/309