Evidence map›Paper›PMID 22514532›Full record

ArticleFrontiers in computational neuroscience2012

Shortest Loops are Pacemakers in Random Networks of Electrically Coupled Axons.

Nikita Vladimirov, Yuhai Tu, Roger D Traub

Open access · goldAbstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Nikita VladimirovIBM T. J. Watson Research Center Yorktown Heights, NY, USA.
Yuhai Tu
Roger D Traub
IBM Research - Thomas J. Watson Research Center · US

Funding

Detailed Models of Network Bursts and OscillationsR01NS044133 · NINDS · SUNY DOWNSTATE MEDICAL CENTER · PI TRAUB, ROGER D · 2002 to 2017
$3.9M
Quantitative Modeling of Signal Transduction in Bacterial ChemotaxisR01GM081747 · NIGMS · IBM THOMAS J. WATSON RESEARCH CENTER · PI TU, YUHAI · 2007 to 2018
$2.6M
High-Frequency Rhythms of the Neocortex: Mechanisms and InteractionsR01NS062955 · NINDS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI KOPELL, NANCY · 2009 to 2010
$648k
NIGMS NIH HHS R01 GM081747NINDS NIH HHS R01 NS044133NINDS NIH HHS R01 NS062955
6 · The paper itself

Abstract

High-frequency oscillations (HFOs) are an important part of brain activity in health and disease. However, their origins remain obscure and controversial. One possible mechanism depends on the presence of sparsely distributed gap junctions that electrically couple the axons of principal cells. A plexus of electrically coupled axons is modeled as a random network with bi-directional connections between its nodes. Under certain conditions the network can demonstrate one of two types of oscillatory activity. Type I oscillations (100-200 Hz) are predicted to be caused by spontaneously spiking axons in a network with strong (high conductance) gap junctions. Type II oscillations (200-300 Hz) require no spontaneous spiking and relatively weak (low-conductance) gap junctions, across which spike propagation failures occur. The type II oscillations are reentrant and self-sustained. Here we examine what determines the frequency of type II oscillations. Using simulations we show that the distribution of loop lengths is the key factor for determining frequency in type II network oscillations. We first analyze spike failure between two electrically coupled cells using a model of anatomically reconstructed CA1 pyramidal neuron. Then network oscillations are studied by a cellular automaton model with random network connectivity, in which we control loop statistics. We show that oscillation periods can be predicted from the network's loop statistics. The shortest loop, around which a spike can travel, is the most likely pacemaker candidate. The principle of one loop as a pacemaker is remarkable, because random networks contain a large number of loops juxtaposed and superimposed, and their number rapidly grows with network size. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We finally propose that type I oscillations may correspond to ripples, while type II oscillations correspond to so-called fast ripples.

Indexed as

axongap junctionloopnetworkpacemakerpyramidal neuron

Identifiers

PMID22514532
PMCPMC3324298
OpenAlexW2033634248

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.