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Modelling large scale neuronal networks using 'average neurones'

Toth, Tibor I. and Crunelli, Vincenzo ORCID: 2002. Modelling large scale neuronal networks using 'average neurones'. Neuroreport 13 (14) , pp. 1785-1788.

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Biosciences
Neuroscience and Mental Health Research Institute (NMHRI)
Subjects: Q Science > Q Science (General)
Uncontrolled Keywords: Hyper-geometric distribution; random connectivity; self-excitatory network; statistical model.
Publisher: Lippincott, Williams & Wilkins
ISSN: 0959-4965
Last Modified: 25 Oct 2022 10:05

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