↓ Skip to main content

PLOS

Poisson-Like Spiking in Circuits with Probabilistic Synapses

Overview of attention for article published in PLoS Computational Biology, July 2014
Altmetric Badge

Mentioned by

twitter
6 X users

Readers on

mendeley
98 Mendeley
citeulike
1 CiteULike
Title
Poisson-Like Spiking in Circuits with Probabilistic Synapses
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003522
Pubmed ID
Authors

Rubén Moreno-Bote

Abstract

Neuronal activity in cortex is variable both spontaneously and during stimulation, and it has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms underlying cortical-like spiking variability over such a broad continuum of rates are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generate Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. Other sources of variability, such as random synaptic delays or spike generation jittering, do not lead to Poisson-like variability at high rates because they cannot be sufficiently amplified by recurrent neuronal networks. We also show that probabilistic synapses predict Fano factor constancy of synaptic conductances. Our results suggest that synaptic noise is a robust and sufficient mechanism for the type of variability found in cortex.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 3 3%
Germany 2 2%
United States 1 1%
Switzerland 1 1%
Unknown 91 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 31%
Researcher 21 21%
Student > Master 12 12%
Student > Bachelor 12 12%
Professor 6 6%
Other 13 13%
Unknown 4 4%
Readers by discipline Count As %
Neuroscience 30 31%
Agricultural and Biological Sciences 19 19%
Engineering 16 16%
Computer Science 10 10%
Mathematics 5 5%
Other 11 11%
Unknown 7 7%