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Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks

Overview of attention for article published in Frontiers in Neural Circuits, April 2016
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Title
Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks
Published in
Frontiers in Neural Circuits, April 2016
DOI 10.3389/fncir.2016.00032
Pubmed ID
Authors

Thomas B. DeMarse, Liangbin Pan, Sankaraleengam Alagapan, Gregory J. Brewer, Bruce C. Wheeler

Abstract

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 1%
Unknown 82 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 30%
Student > Master 14 17%
Researcher 12 14%
Student > Doctoral Student 5 6%
Student > Bachelor 4 5%
Other 11 13%
Unknown 12 14%
Readers by discipline Count As %
Neuroscience 27 33%
Agricultural and Biological Sciences 11 13%
Engineering 9 11%
Computer Science 4 5%
Biochemistry, Genetics and Molecular Biology 4 5%
Other 10 12%
Unknown 18 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 22 April 2016.
All research outputs
#20,322,106
of 22,865,319 outputs
Outputs from Frontiers in Neural Circuits
#1,032
of 1,217 outputs
Outputs of similar age
#253,331
of 298,997 outputs
Outputs of similar age from Frontiers in Neural Circuits
#29
of 31 outputs
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