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| Research article summary (published 5 Oct 2002): |
Modelling large scale neuronal networks using 'average neurones'.
Full Abstract
Large scale neuronal network models have become important tools in studying the information transmission within the CNS. In most cases, these models use simplifying assumptions because of unavailable data (e.g. unknown exact network connectivity), and for technical reasons (to preserve numerical stability of the model). Here, we present a novel approach, based on a probabilistic connectivity principle, to this modelling problem for which no knowledge of the exact network connectivity is required. This principle makes it sufficient to compute only the typical neuronal behaviour, represented by 'average neurones', in the network. As a consequence, detailed neurone models can be employed without seriously compromising computational efficiency. Our model thus provides a viable alternative to deterministic models.Copyright 2002 Lippincott Williams & Wilkins
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Author information
Author/s: Tóth, Tibor I (TI); Crunelli, Vincenzo (V);
Affiliation: School of Biosciences, Cardiff University, UK. ttoth(-atsign-)bolyai.phyl.cf.ac.uk
Journal and publication information
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal: Neuroreport (Neuroreport), published in England. (Language: eng)
Reference: 2002-Oct; vol 13 (issue 14) : pp 1785-8
Dates: Created 2002/10/23; Completed 2003/01/15; Revised 2006/11/15;
PMID: 12395123, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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