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Simulation of developing human neuronal cell networks

Overview of attention for article published in BioMedical Engineering OnLine, August 2016
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Title
Simulation of developing human neuronal cell networks
Published in
BioMedical Engineering OnLine, August 2016
DOI 10.1186/s12938-016-0226-6
Pubmed ID
Authors

Kerstin Lenk, Barbara Priwitzer, Laura Ylä-Outinen, Lukas H. B. Tietz, Susanna Narkilahti, Jari A. K. Hyttinen

Abstract

Microelectrode array (MEA) is a widely used technique to study for example the functional properties of neuronal networks derived from human embryonic stem cells (hESC-NN). With hESC-NN, we can investigate the earliest developmental stages of neuronal network formation in the human brain. In this paper, we propose an in silico model of maturating hESC-NNs based on a phenomenological model called INEX. We focus on simulations of the development of bursts in hESC-NNs, which are the main feature of neuronal activation patterns. The model was developed with data from developing hESC-NN recordings on MEAs which showed increase in the neuronal activity during the investigated six measurement time points in the experimental and simulated data. Our simulations suggest that the maturation process of hESC-NN, resulting in the formation of bursts, can be explained by the development of synapses. Moreover, spike and burst rate both decreased at the last measurement time point suggesting a pruning of synapses as the weak ones are removed. To conclude, our model reflects the assumption that the interaction between excitatory and inhibitory neurons during the maturation of a neuronal network and the spontaneous emergence of bursts are due to increased connectivity caused by the forming of new synapses.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 34%
Student > Ph. D. Student 7 24%
Student > Master 4 14%
Student > Doctoral Student 2 7%
Professor > Associate Professor 2 7%
Other 1 3%
Unknown 3 10%
Readers by discipline Count As %
Neuroscience 6 21%
Engineering 4 14%
Medicine and Dentistry 4 14%
Agricultural and Biological Sciences 3 10%
Economics, Econometrics and Finance 2 7%
Other 8 28%
Unknown 2 7%
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 30 January 2017.
All research outputs
#17,813,370
of 22,884,315 outputs
Outputs from BioMedical Engineering OnLine
#529
of 822 outputs
Outputs of similar age
#243,677
of 336,882 outputs
Outputs of similar age from BioMedical Engineering OnLine
#9
of 12 outputs
Altmetric has tracked 22,884,315 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 31st percentile – i.e., 31% 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 336,882 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.