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Efficient Partitioning of Memory Systems and Its Importance for Memory Consolidation

Overview of attention for article published in PLoS Computational Biology, July 2013
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Title
Efficient Partitioning of Memory Systems and Its Importance for Memory Consolidation
Published in
PLoS Computational Biology, July 2013
DOI 10.1371/journal.pcbi.1003146
Pubmed ID
Authors

Alex Roxin, Stefano Fusi

Abstract

Long-term memories are likely stored in the synaptic weights of neuronal networks in the brain. The storage capacity of such networks depends on the degree of plasticity of their synapses. Highly plastic synapses allow for strong memories, but these are quickly overwritten. On the other hand, less labile synapses result in long-lasting but weak memories. Here we show that the trade-off between memory strength and memory lifetime can be overcome by partitioning the memory system into multiple regions characterized by different levels of synaptic plasticity and transferring memory information from the more to less plastic region. The improvement in memory lifetime is proportional to the number of memory regions, and the initial memory strength can be orders of magnitude larger than in a non-partitioned memory system. This model provides a fundamental computational reason for memory consolidation processes at the systems level.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
United Kingdom 2 2%
Hungary 1 <1%
France 1 <1%
Switzerland 1 <1%
Norway 1 <1%
Belarus 1 <1%
Unknown 114 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 30%
Researcher 28 23%
Student > Master 18 15%
Student > Bachelor 7 6%
Student > Doctoral Student 6 5%
Other 15 12%
Unknown 12 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 24%
Neuroscience 23 19%
Computer Science 15 12%
Physics and Astronomy 10 8%
Psychology 9 7%
Other 23 19%
Unknown 14 11%