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The computational power of astrocyte mediated synaptic plasticity

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2012
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Title
The computational power of astrocyte mediated synaptic plasticity
Published in
Frontiers in Computational Neuroscience, January 2012
DOI 10.3389/fncom.2012.00093
Pubmed ID
Authors

Rogier Min, Mirko Santello, Thomas Nevian

Abstract

Research in the last two decades has made clear that astrocytes play a crucial role in the brain beyond their functions in energy metabolism and homeostasis. Many studies have shown that astrocytes can dynamically modulate neuronal excitability and synaptic plasticity, and might participate in higher brain functions like learning and memory. With the plethora of astrocyte mediated signaling processes described in the literature today, the current challenge is to identify, which of these processes happen under what physiological condition, and how this shapes information processing and, ultimately, behavior. To answer these questions will require a combination of advanced physiological, genetical, and behavioral experiments. Additionally, mathematical modeling will prove crucial for testing predictions on the possible functions of astrocytes in neuronal networks, and to generate novel ideas as to how astrocytes can contribute to the complexity of the brain. Here, we aim to provide an outline of how astrocytes can interact with neurons. We do this by reviewing recent experimental literature on astrocyte-neuron interactions, discussing the dynamic effects of astrocytes on neuronal excitability and short- and long-term synaptic plasticity. Finally, we will outline the potential computational functions that astrocyte-neuron interactions can serve in the brain. We will discuss how astrocytes could govern metaplasticity in the brain, how they might organize the clustering of synaptic inputs, and how they could function as memory elements for neuronal activity. We conclude that astrocytes can enhance the computational power of neuronal networks in previously unexpected ways.

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 226 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
United Kingdom 2 <1%
Switzerland 1 <1%
France 1 <1%
Colombia 1 <1%
Germany 1 <1%
Israel 1 <1%
Iran, Islamic Republic of 1 <1%
South Africa 1 <1%
Other 4 2%
Unknown 208 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 63 28%
Researcher 36 16%
Student > Bachelor 25 11%
Student > Master 24 11%
Professor 13 6%
Other 42 19%
Unknown 23 10%
Readers by discipline Count As %
Neuroscience 71 31%
Agricultural and Biological Sciences 58 26%
Computer Science 15 7%
Biochemistry, Genetics and Molecular Biology 13 6%
Medicine and Dentistry 10 4%
Other 31 14%
Unknown 28 12%
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 28 May 2013.
All research outputs
#13,371,661
of 22,685,926 outputs
Outputs from Frontiers in Computational Neuroscience
#570
of 1,336 outputs
Outputs of similar age
#146,695
of 244,123 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#29
of 69 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,336 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has gotten more attention than average, scoring higher than 54% of its peers.
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 244,123 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.