Evidence map›Paper›PMID 18719938›Full record

ArticleBiological cybernetics2008

Predicting the activity phase of a follower neuron with A-current in an inhibitory network.

Yu Zhang, Amitabha Bose, Farzan Nadim

Abstract read
In one paragraph

Article in Biological cybernetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Yu ZhangDepartment of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA. yu.zhang@njit.edu
Amitabha Bose
Farzan Nadim

Funding

REGULATION OF NEURONAL OSCILLATIONS BY SYNAPTIC DYNAMICSR01MH060605 · NIMH · NEW JERSEY INSTITUTE OF TECHNOLOGY · PI Dirk Martin Bucher, Farzan Nadim · 2001 to 2026
$6.6M
NIMH NIH HHS MH-60605NIMH NIH HHS R01 MH060605
6 · The paper itself

Abstract

The transient potassium A-current is present in most neurons and plays an important role in determining the timing of action potentials. We examine the role of the A-current in the activity phase of a follower neuron in a rhythmic feed-forward inhibitory network with a reduced three-variable model and conduct experiments to verify the usefulness of our model. Using geometric analysis of dynamical systems, we explore the factors that determine the onset of activity in a follower neuron following release from inhibition. We first analyze the behavior of the follower neuron in a single cycle and find that the phase plane structure of the model can be used to predict the potential behaviors of the follower neuron following release from inhibition. We show that, depending on the relative scales of the inactivation time constant of the A-current and the time constant of the recovery variable, the follower neuron may or may not reach its active state following inhibition. Our simple model is used to derive a recursive set of equations to predict the contribution of the A-current parameters in determining the activity phase of a follower neuron as a function of the duration and frequency of the inhibitory input it receives. These equations can be used to demonstrate the dependence of activity phase on the period and duty cycle of the periodic inhibition, as seen by comparing the predictions of the model with the activity of the pyloric constrictor (PY) neurons in the crustacean pyloric network.

Indexed as

Nerve NetAnimalsCrustaceaGanglia, InvertebrateMembrane PotentialsModels, NeurologicalNeural InhibitionNeuronsPeriodicityPotassium ChannelsPotassium Channels

Identifiers

PMID18719938
PMCPMC2702225

What OpenQuestion holds

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Registered trials

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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.