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 Feng, J. Articles by Li, G. Search for Related Content PubMed PubMed Citation Articles by Feng, J. Articles by Li, G.

Impact of Geometrical Structures on the Output of Neuronal Models: A Theoretical and Numerical Analysis

jf218{at}cam.ac.uk, Computational Neuroscience Laboratory, Babraham Institute, Cambridge CB2 4AT, U.K., and COGS, University of Sussex at Brighton, BN1 9QH, U.K.

Guibin Li

gl218{at}cam.ac.uk, Computational Neuroscience Laboratory, Babraham Institute, Cambridge CB2 4AT, U.K.

What is the difference between the efferent spike train of a neuron with a large soma versus that of a neuron with a small soma? We propose an analytical method called the decoupling approach to tackle the problem. Two limiting cases—the soma is much smaller than the dendrite or vica versa—are theoretically investigated. For both the two-compartment integrate-and-fire model and Pinsky-Rinzel model, we show, both theoretically and numerically, that the smaller the soma is, the faster and the more irregularly the neuron fires. We further conclude, in terms of numerical simulations, that cells falling in between the two limiting cases form a continuum with respect to their firing properties (mean firing time and coefficient of variation of inter-spike intervals).