Evidence map›Paper›PMID 26400658›Full record

ArticleJournal of computational neuroscience2015

Impact of sub and supra-threshold adaptation currents in networks of spiking neurons.

David Colliaux, Pierre Yger, Kunihiko Kaneko

Open access · hybridAbstract read
In one paragraph

Article in Journal of computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 35% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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 2 institutions in 2 countries.

David ColliauxInstitut des Systèmes Intelligents et de Robotique (ISIR), CNRS UMR 7222, UPMC University Paris, 4 Place Jussieu, 75005, Paris, France. colliaux@isir.upmc.fr.ORCID http://orcid.org/0000-0003-1898-4864
Pierre YgerInstitut d'Etudes de la Cognition, ENS, Paris, France.
Kunihiko KanekoDepartment of Basic Science, The University of Tokyo, 3-8-1, Komaba, Meguro-ku, Tokyo, 153-8902, Japan.
Centre National de la Recherche Scientifique · FRThe University of Tokyo · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuronal adaptation is the intrinsic capacity of the brain to change, by various mechanisms, its dynamical responses as a function of the context. Such a phenomena, widely observed in vivo and in vitro, is known to be crucial in homeostatic regulation of the activity and gain control. The effects of adaptation have already been studied at the single-cell level, resulting from either voltage or calcium gated channels both activated by the spiking activity and modulating the dynamical responses of the neurons. In this study, by disentangling those effects into a linear (sub-threshold) and a non-linear (supra-threshold) part, we focus on the the functional role of those two distinct components of adaptation onto the neuronal activity at various scales, starting from single-cell responses up to recurrent networks dynamics, and under stationary or non-stationary stimulations. The effects of slow currents on collective dynamics, like modulation of population oscillation and reliability of spike patterns, is quantified for various types of adaptation in sparse recurrent networks.

Indexed as

Computer SimulationModels, NeurologicalNeural Networks, ComputerAction PotentialsAdaptation, PhysiologicalAnimalsCalcium ChannelsCerebral CortexHumansNerve NetNerve Tissue ProteinsNeuronsNonlinear DynamicsPotassium Channels, Voltage-GatedSynapsesCalcium ChannelsNerve Tissue ProteinsPotassium Channels, Voltage-GatedAdaptationNeuronal dynamics

Identifiers

PMID26400658
PMCPMC4649064
OpenAlexW2097173513

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.