↓ Skip to main content

PLOS

Non-Additive Coupling Enables Propagation of Synchronous Spiking Activity in Purely Random Networks

Overview of attention for article published in PLoS Computational Biology, April 2012
Altmetric Badge

Mentioned by

twitter
2 X users
facebook
1 Facebook page
googleplus
1 Google+ user

Readers on

mendeley
75 Mendeley
citeulike
1 CiteULike
Title
Non-Additive Coupling Enables Propagation of Synchronous Spiking Activity in Purely Random Networks
Published in
PLoS Computational Biology, April 2012
DOI 10.1371/journal.pcbi.1002384
Pubmed ID
Authors

Raoul-Martin Memmesheimer, Marc Timme

Abstract

Despite the current debate about the computational role of experimentally observed precise spike patterns it is still theoretically unclear under which conditions and how they may emerge in neural circuits. Here, we study spiking neural networks with non-additive dendritic interactions that were recently uncovered in single-neuron experiments. We show that supra-additive dendritic interactions enable the persistent propagation of synchronous activity already in purely random networks without superimposed structures and explain the mechanism underlying it. This study adds a novel perspective on the dynamics of networks with nonlinear interactions in general and presents a new viable mechanism for the occurrence of patterns of precisely timed spikes in recurrent networks.

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

Geographical breakdown

Country Count As %
United Kingdom 2 3%
United States 2 3%
Germany 2 3%
Canada 1 1%
Unknown 68 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 35%
Student > Ph. D. Student 23 31%
Professor 5 7%
Professor > Associate Professor 5 7%
Student > Master 4 5%
Other 8 11%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 27%
Physics and Astronomy 11 15%
Computer Science 11 15%
Neuroscience 10 13%
Mathematics 6 8%
Other 9 12%
Unknown 8 11%