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Soft-bound Synaptic Plasticity Increases Storage Capacity

Overview of attention for article published in PLoS Computational Biology, December 2012
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63 Mendeley
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Title
Soft-bound Synaptic Plasticity Increases Storage Capacity
Published in
PLoS Computational Biology, December 2012
DOI 10.1371/journal.pcbi.1002836
Pubmed ID
Authors

Mark C. W. van Rossum, Maria Shippi, Adam B. Barrett

Abstract

Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of the connection itself. Namely, weaker synapses are more easily strengthened than already strong ones. This so called soft-bound plasticity automatically constrains the synaptic strengths. It is known that this has important consequences for the dynamics of plasticity and the synaptic weight distribution, but its impact on information storage is unknown. In this modeling study we introduce an information theoretic framework to analyse memory storage in an online learning setting. We show that soft-bound plasticity increases a variety of performance criteria by about 18% over hard-bound plasticity, and likely maximizes the storage capacity of synapses.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Germany 2 3%
United Kingdom 1 2%
Switzerland 1 2%
Unknown 57 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 22%
Student > Ph. D. Student 12 19%
Student > Doctoral Student 6 10%
Student > Master 6 10%
Professor 5 8%
Other 14 22%
Unknown 6 10%
Readers by discipline Count As %
Computer Science 13 21%
Agricultural and Biological Sciences 11 17%
Neuroscience 7 11%
Psychology 5 8%
Physics and Astronomy 5 8%
Other 14 22%
Unknown 8 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 January 2013.
All research outputs
#17,302,400
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#7,481
of 8,964 outputs
Outputs of similar age
#193,530
of 288,572 outputs
Outputs of similar age from PLoS Computational Biology
#94
of 122 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.