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Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

Overview of attention for article published in PLoS Computational Biology, December 2008
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Title
Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression
Published in
PLoS Computational Biology, December 2008
DOI 10.1371/journal.pcbi.1000248
Pubmed ID
Authors

Claudia Clopath, Lorric Ziegler, Eleni Vasilaki, Lars Büsing, Wulfram Gerstner

Abstract

Changes in synaptic efficacies need to be long-lasting in order to serve as a substrate for memory. Experimentally, synaptic plasticity exhibits phases covering the induction of long-term potentiation and depression (LTP/LTD) during the early phase of synaptic plasticity, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation during the late phase of synaptic plasticity. We present a mathematical model that describes these different phases of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency. The stabilization of potentiated synapses during the transition from early to late LTP occurs by protein synthesis dynamics that are shared by groups of synapses. The functional consequence of this shared process is that previously stabilized patterns of strong or weak synapses onto the same postsynaptic neuron are well protected against later changes induced by LTP/LTD protocols at individual synapses.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 221 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 9 4%
Switzerland 7 3%
Germany 4 2%
United States 3 1%
France 3 1%
Greece 2 <1%
Brazil 1 <1%
Sweden 1 <1%
India 1 <1%
Other 3 1%
Unknown 187 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 79 36%
Researcher 45 20%
Student > Master 20 9%
Student > Bachelor 11 5%
Professor > Associate Professor 11 5%
Other 36 16%
Unknown 19 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 58 26%
Neuroscience 44 20%
Computer Science 27 12%
Physics and Astronomy 19 9%
Psychology 15 7%
Other 32 14%
Unknown 26 12%