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A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing

Overview of attention for article published in Frontiers in Computational Neuroscience, December 2014
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Title
A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing
Published in
Frontiers in Computational Neuroscience, December 2014
DOI 10.3389/fncom.2014.00157
Pubmed ID
Authors

William Lennon, Robert Hecht-Nielsen, Tadashi Yamazaki

Abstract

While the anatomy of the cerebellar microcircuit is well-studied, how it implements cerebellar function is not understood. A number of models have been proposed to describe this mechanism but few emphasize the role of the vast network Purkinje cells (PKJs) form with the molecular layer interneurons (MLIs)-the stellate and basket cells. We propose a model of the MLI-PKJ network composed of simple spiking neurons incorporating the major anatomical and physiological features. In computer simulations, the model reproduces the irregular firing patterns observed in PKJs and MLIs in vitro and a shift toward faster, more regular firing patterns when inhibitory synaptic currents are blocked. In the model, the time between PKJ spikes is shown to be proportional to the amount of feedforward inhibition from an MLI on average. The two key elements of the model are: (1) spontaneously active PKJs and MLIs due to an endogenous depolarizing current, and (2) adherence to known anatomical connectivity along a parasagittal strip of cerebellar cortex. We propose this model to extend previous spiking network models of the cerebellum and for further computational investigation into the role of irregular firing and MLIs in cerebellar learning and function.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Puerto Rico 1 2%
Brazil 1 2%
Unknown 57 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 22%
Researcher 10 17%
Student > Master 9 15%
Student > Bachelor 4 7%
Student > Postgraduate 4 7%
Other 9 15%
Unknown 11 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 28%
Neuroscience 13 22%
Engineering 3 5%
Computer Science 3 5%
Physics and Astronomy 3 5%
Other 8 13%
Unknown 13 22%
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 02 December 2014.
All research outputs
#18,383,471
of 22,770,070 outputs
Outputs from Frontiers in Computational Neuroscience
#1,051
of 1,340 outputs
Outputs of similar age
#261,716
of 361,396 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#24
of 28 outputs
Altmetric has tracked 22,770,070 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,340 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 13th percentile – i.e., 13% 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 361,396 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.