Evidence map›Paper›PMID 20544264›Full record

ArticleJournal of computational neuroscience2011

Dual oscillator model of the respiratory neuronal network generating quantal slowing of respiratory rhythm.

Amit Lal, Yoshitaka Oku, Swen Hülsmann, Yasumasa Okada, Fumikazu Miwakeichi, Shigeharu Kawai, Yoshiyasu Tamura, Makio Ishiguro

Abstract read
In one paragraph

Article in Journal of computational neuroscience, 2011. 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
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Computational models of the neural control of breathing.Wiley interdisciplinary reviews. Systems biology and medicine · 2017
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Integrative approaches for modeling regulation and function of the respiratory system.Wiley interdisciplinary reviews. Systems biology and medicine
    Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Amit LalDepartment of Physiology, Hyogo College of Medicine, Nishinomiya, Hyogo, 663-8501, Japan.
Yoshitaka Oku
Swen Hülsmann
Yasumasa Okada
Fumikazu Miwakeichi
Shigeharu Kawai
Yoshiyasu Tamura
Makio Ishiguro

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We developed a dual oscillator model to facilitate the understanding of dynamic interactions between the parafacial respiratory group (pFRG) and the preBötzinger complex (preBötC) neurons in the respiratory rhythm generation. Both neuronal groups were modeled as groups of 81 interconnected pacemaker neurons; the bursting cell model described by Butera and others [model 1 in Butera et al. (J Neurophysiol 81:382-397, 1999a)] were used to model the pacemaker neurons. We assumed (1) both pFRG and preBötC networks are rhythm generators, (2) preBötC receives excitatory inputs from pFRG, and pFRG receives inhibitory inputs from preBötC, and (3) persistent Na(+) current conductance and synaptic current conductances are randomly distributed within each population. Our model could reproduce 1:1 coupling of bursting rhythms between pFRG and preBötC with the characteristic biphasic firing pattern of pFRG neurons, i.e., firings during pre-inspiratory and post-inspiratory phases. Compatible with experimental results, the model predicted the changes in firing pattern of pFRG neurons from biphasic expiratory to monophasic inspiratory, synchronous with preBötC neurons. Quantal slowing, a phenomena of prolonged respiratory period that jumps non-deterministically to integer multiples of the control period, was observed when the excitability of preBötC network decreased while strengths of synaptic connections between the two groups remained unchanged, suggesting that, in contrast to the earlier suggestions (Mellen et al., Neuron 37:821-826, 2003; Wittmeier et al., Proc Natl Acad Sci USA 105(46):18000-18005, 2008), quantal slowing could occur without suppressed or stochastic excitatory synaptic transmission. With a reduced excitability of preBötC network, the breakdown of synchronous bursting of preBötC neurons was predicted by simulation. We suggest that quantal slowing could result from a breakdown of synchronized bursting within the preBötC.

Indexed as

Models, NeurologicalPeriodicityAnimalsCell RespirationComputer SimulationRespiratory Center

Identifiers

PMID20544264
PMCPMC3058346

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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.