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A Model of In vitro Plasticity at the Parallel Fiber—Molecular Layer Interneuron Synapses

Overview of attention for article published in Frontiers in Computational Neuroscience, December 2015
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Title
A Model of In vitro Plasticity at the Parallel Fiber—Molecular Layer Interneuron Synapses
Published in
Frontiers in Computational Neuroscience, December 2015
DOI 10.3389/fncom.2015.00150
Pubmed ID
Authors

William Lennon, Tadashi Yamazaki, Robert Hecht-Nielsen

Abstract

Theoretical and computational models of the cerebellum typically focus on the role of parallel fiber (PF)-Purkinje cell (PKJ) synapses for learned behavior, but few emphasize the role of the molecular layer interneurons (MLIs)-the stellate and basket cells. A number of recent experimental results suggest the role of MLIs is more important than previous models put forth. We investigate learning at PF-MLI synapses and propose a mathematical model to describe plasticity at this synapse. We perform computer simulations with this form of learning using a spiking neuron model of the MLI and show that it reproduces six in vitro experimental results in addition to simulating four novel protocols. Further, we show how this plasticity model can predict the results of other experimental protocols that are not simulated. Finally, we hypothesize what the biological mechanisms are for changes in synaptic efficacy that embody the phenomenological model proposed here.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 5%
Unknown 19 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 25%
Researcher 4 20%
Other 2 10%
Professor 1 5%
Student > Doctoral Student 1 5%
Other 2 10%
Unknown 5 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 20%
Neuroscience 4 20%
Medicine and Dentistry 2 10%
Nursing and Health Professions 1 5%
Social Sciences 1 5%
Other 3 15%
Unknown 5 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 January 2016.
All research outputs
#14,830,609
of 22,836,570 outputs
Outputs from Frontiers in Computational Neuroscience
#766
of 1,343 outputs
Outputs of similar age
#216,981
of 390,633 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#14
of 26 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,343 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 390,633 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.