Home LiteratureArticle Details
PMID: 8872701 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S.

Dissection and reduction of a modeled bursting neuron.

Journal of computational neuroscience ·Vol. 3 ·No. 3 ·1996-09-00 ·Pages 199-223

Butera RJ, Clark JW, Byrne JH

Abstract

An 11-variable Hodgkin-Huxley type model of a bursting neuron was investigated using numerical bifurcation analysis and computer simulations. The results were applied to develop a reduced model of the underlying subthreshold oscillations (slow-wave) in membrane potential. Two different low-order models were developed: one 3-variable model, which mimicked the slow-wave of the full model in the absence of action potentials and a second 4-variable model, which included expressions accounting for the perturbational effects of action potentials on the slow-wave. The 4-variable model predicted more accurately the activity mode (bursting, beating, or silence) in response to application of extrinsic stimulus current or modulatory agents. The 4-variable model also possessed a phase-response curve that was very similar to that of the original 11-variable model. The results suggest that low-order models of bursting cells that do not consider the effects of action potentials may erroneously predict modes of activity and transient responses of the full model on which the reductions are based. These results also show that it is possible to develop low-order models that retain many of the characteristics of the activity of the higher-order system.

MeSH Terms
Animals Aplysia Membrane Potentials/physiology Neural Networks, Computer Neurons/physiology
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Butera R J
Dept. of Electrical and Computer Engineering, Rice University, Houston, TX 77251-1892, USA.rbutera@ece.rice.edu
Clark J W
Byrne J H
References (22)
22 references, click to expand
  1. Analysis of the effects of modulatory agents on a modeled bursting neuron: dynamic interactions between voltage and calcium dependent systems.
    J Comput Neurosci. 1995 Mar;2(1):19-44 PMID: 8521278
  2. Modeling the gastric mill central pattern generator of the lobster with a relaxation-oscillator network.
    J Neurophysiol. 1993 Sep;70(3):1030-53 PMID: 8229158
  3. Topological and phenomenological classification of bursting oscillations.
    Bull Math Biol. 1995 May;57(3):413-39 PMID: 7728115
  4. Aplysia bursting neurons as endogenous oscillators. I. Phase-response curves for pulsed inhibitory synaptic input.
    J Neurophysiol. 1977 May;40(3):527-43 PMID: 889594
  5. Dissection of a model for neuronal parabolic bursting.
    J Math Biol. 1987;25(6):653-75 PMID: 3437231
  6. Characteristics of pacemaker oscillations in Aplysia neurons.
    Can J Physiol Pharmacol. 1971 Sep;49(9):787-95 PMID: 5143671
  7. ATP-sensitive potassium channel and bursting in the pancreatic beta cell. A theoretical study.
    Biophys J. 1989 Aug;56(2):229-42 PMID: 2673420
  8. Cyclic variation of potassium conductance in a burst-generating neurone in Aplysia.
    J Physiol. 1973 Nov;235(1):155-81 PMID: 4778133
  9. Nonlinear dynamics in a model neuron provide a novel mechanism for transient synaptic inputs to produce long-term alterations of postsynaptic activity.
    J Neurophysiol. 1993 Jun;69(6):2252-7 PMID: 8350142
  10. The cellular basis of behavior in Aplysia.
    J Psychiatr Res. 1971 Aug;8(3):237-57 PMID: 4939375
  11. Analysis of metabolic systems with complex slow and fast dynamics.
    Bull Math Biol. 1989;51(2):255-74 PMID: 2647171
  12. Analysis of an autonomous phase model for neuronal parabolic bursting.
    J Math Biol. 1995;33(3):309-33 PMID: 7897331
  13. Emergence of organized bursting in clusters of pancreatic beta-cells by channel sharing.
    Biophys J. 1988 Sep;54(3):411-25 PMID: 2850029
  14. Simulation of the bursting activity of neuron R15 in Aplysia: role of ionic currents, calcium balance, and modulatory transmitters.
    J Neurophysiol. 1991 Dec;66(6):2107-24 PMID: 1725879
  15. A quantitative description of membrane current and its application to conduction and excitation in nerve.
    J Physiol. 1952 Aug;117(4):500-44 PMID: 12991237
  16. Analysis of bursting in a thalamic neuron model.
    Biol Cybern. 1994;71(4):281-91 PMID: 7948220
  17. Voltage oscillations in the barnacle giant muscle fiber.
    Biophys J. 1981 Jul;35(1):193-213 PMID: 7260316
  18. Mathematical description of a bursting pacemaker neuron by a modification of the Hodgkin-Huxley equations.
    Biophys J. 1976 Mar;16(3):227-44 PMID: 1252578
  19. Mechanisms for oscillation and frequency control in reciprocally inhibitory model neural networks.
    J Comput Neurosci. 1994 Jun;1(1-2):69-87 PMID: 8792226
  20. Reduction of conductance-based neuron models.
    Biol Cybern. 1992;66(5):381-7 PMID: 1562643
  21. Bursting excitable cell models by a slow Ca2+ current.
    J Theor Biol. 1990 Feb 9;142(3):305-15 PMID: 2160027
  22. Minimal model for membrane oscillations in the pancreatic beta-cell.
    Biophys J. 1983 May;42(2):181-90 PMID: 6305437
Article Info
Journal
Journal of computational neuroscience
Abbr.
J Comput Neurosci
ISSN
0929-5313
Published
1996-09-00
Pages
199-223
Language
English
Region
United States
NLM ID
9439510
Subset
IM
Grants
NIMH NIH HHS · K05 MH 00649 · United States
Analysis Services
Analysis Services

Contact

No. 2 Wenbo Road, Zhangqiu District, Jinan, Shandong

Qilu Normal University · Genelibs Bioinformatics Lab

750 Shunhua Rd, Jinan

2F, Bldg F, University Science Park

Tel: 0531-88819269

WeChat Official Account

Follow our WeChat subscription account for real-time updates and the latest in medical and biological research.


Business Email

E-mail: product@genelibs.com