Increased computational accuracy in multi-compartmental cable models by a novel approach for precise point process localization

Lindsay, A.E., Lindsay, K.A. and Rosenberg, J.R. (2005) Increased computational accuracy in multi-compartmental cable models by a novel approach for precise point process localization. Journal of Computational Neuroscience, 19(1), pp. 21-38. (doi: 10.1007/s10827-005-0192-7)

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Publisher's URL: http://dx.doi.org/10.1007/s10827-005-0192-7

Abstract

Compartmental models of dendrites are the most widely used tool for investigating their electrical behaviour. Traditional models assign a single potential to a compartment. This potential is associated with the membrane potential at the centre of the segment represented by the compartment. All input to that segment, independent of its location on the segment, is assumed to act at the centre of the segment with the potential of the compartment. By contrast, the compartmental model introduced in this article assigns a potential to each end of a segment, and takes into account the location of input to a segment on the model solution by partitioning the effect of this input between the axial currents at the proximal and distal boundaries of segments. For a given neuron, the new and traditional approaches to compartmental modelling use the same number of locations at which the membrane potential is to be determined, and lead to ordinary differential equations that are structurally identical. However, the solution achieved by the new approach gives an order of magnitude better accuracy and precision than that achieved by the latter in the presence of point process input.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Rosenberg, Prof Jay and Lindsay, Professor Kenneth
Authors: Lindsay, A.E., Lindsay, K.A., and Rosenberg, J.R.
Subjects:R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Q Science > QA Mathematics
College/School:College of Medical Veterinary and Life Sciences
Journal Name:Journal of Computational Neuroscience
ISSN:0929-5313
ISSN (Online):1573-6873
Published Online:17 August 2005

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