Synaptic and Non-Synaptic Control of Membrane Potential Fluctuations in Bulbar Respiratory Neurons of Cats

  • Ryuji Takeda
  • Akira Haji

Abstract

Bulbar respiratory neurons display rhythmic fluctuations of membrane potential in synchrony with the respiratory cycle. These undulatory potentials are largely dependent upon the periodically arriving synaptic activities. Intrinsic membrane properties also play a certain important role in the generation or modulation of these potentials. Several types of ionic conductance have been demonstrated in the presumed respiration-related neurons in the brainstem slices2, 3. However, since these neurons in tissue slices usually lack the spontaneous rhythmic modulation in membrane potential, the identification of the neuron type based upon the spontaneous patterns of firing and membrane potential fluctuations is yet uncertain. Moreover, it is hard to know at what timing any specific ionic conductance becomes active in the respiratory cycle change in membrane potential observed in different types of the respiratory neuron. Nonetheless, an intact brainstem preparation has also an inherent drawback to exclude the contamination of ionic currents mediated by action potentials and postsynaptic potentials4, 5. The coaxial multi-barrelled microelectrode technique6 can partly overcome these difficulties as it allows an in vivo intracellular recording of membrane potential in conjunction with an extracellular iontophoresis of drugs which could block action potentials and synaptic waves in the recorded neuron. The present study was aimed at elucidating the possible synaptic and non-synaptic mechanisms by which the periodic fluctuations of membrane potential are shaped in bulbar respiratory neurons of the ventral respiratory group.

Keywords

Respiratory Cycle Respiratory Neuron Ventral Respiratory Group IntrInsic Membrane Property Membrane Potential Fluctuation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Ryuji Takeda
    • 1
  • Akira Haji
    • 1
  1. 1.Department of Pharmacology, Faculty of MedicineToyama Medical and Pharmaceutical UniversityToyamaJapan

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