HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Breen, B. J. Articles by Butera, R. J. Search for Related Content PubMed PubMed Citation Articles by Breen, B. J. Articles by Butera, R. J., Jr.

Hybrid Integrate-and-Fire Model of a Bursting Neuron

bbreen{at}ece.gatech.edu, Laboratory for Neuroengineering, Schools of Physics, Electrical and Computer Engineering, and Biomedical Engineering, Georga Institute of Technology, Atlanta, GA 30332, U.S.A.

William C. Gerken

wgerken{at}ece.gatech.edu, Laboratory for Neuroengineering, Schools of Physics, Electrical and Computer Engineering, and Biomedical Engineering, Georga Institute of Technology, Atlanta, GA 30332, U.S.A.

Robert J. Butera, Jr.

rbutera{at}ece.gatech.edu, Laboratory for Neuroengineering, Schools of Physics, Electrical and Computer Engineering, and Biomedical Engineering, Georga Institute of Technology, Atlanta, GA 30332, U.S.A.

We present a reduction of a Hodgkin-Huxley (HH)–style bursting model to a hybridized integrate-and-fire (IF) formalism based on a thorough bifurcation analysis of the neuron's dynamics. The model incorporates HH-style equations to evolve the subthreshold currents and includes IF mechanisms to characterize spike events and mediate interactions between the subthreshold and spiking currents. The hybrid IF model successfully reproduces the dynamic behavior and temporal characteristics of the full model over a wide range of activity, including bursting and tonic firing. Comparisons of timed computer simulations of the reduced model and the original model for both single neurons and moderately sized networks (n 500) show that this model offers improvement in computational speed over the HH-style bursting model.