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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| Published in | Network (Bristol) Vol. 14; no. 4; p. 789 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
2003
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Sandberg, A. Lansner, Anders Tegner, J. |
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| SubjectTerms | associative memory attractor network dynamics long-term potentiation mechanisms Medicin och hälsovetenskap prefrontal cortex recurrent network spiking neurons synapses TECHNOLOGY TEKNIKVETENSKAP visual-cortex |
| Title | A working memory model based on fast Hebbian learning |
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