COMPUTATIONAL NEUROSCIENCEModelling large scale neuronal networks using ‘average neurones’Tóth, Tibor I.CA; Crunelli, VincenzoAuthor Information School of Biosciences, Cardiff University, P.O. Box 911, Cardiff CF10 3US, UK CACorresponding Author: [email protected] Received 24 June 2002; accepted 29 July 2002 NeuroReport: October 7th, 2002 - Volume 13 - Issue 14 - p 1785-1788 Buy 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. © 2002 Lippincott Williams & Wilkins, Inc.