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This article is part of the supplement: Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access Poster presentation

Modeling the coupling of single neuron activity to local field potentials

Serafim Rodrigues1* and Peter beim Graben2

Author Affiliations

1 Integrative and Computational Neuroscience Unit (UNIC), CNRS, Paris, 1 Avenue de la Terrasse, 91198 Gif-sur-Yvette, France

2 School of Psychology, University of Reading, Reading, Whiteknights, PO Box 217, UK

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BMC Neuroscience 2009, 10(Suppl 1):P291  doi:10.1186/1471-2202-10-S1-P291


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/10/S1/P291


Published:13 July 2009

© 2009 Rodrigues and Graben; licensee BioMed Central Ltd.

Poster presentation

This work presents a first step towards a modeling paradigm that enables to link mesoscopic neurodynamics with single-cell activity. A common approach to describe large-scale activity, such as local field potentials (LFP), is via the so called neural field equations [1,2]. At the neuronal scale, spiking models, such the Hodgkin-Huxley [3] and leaky-integrate neurons, can be employed [4]. However, explaining the link between these levels of descriptions, which are ubiquitous for understanding the coupling of single unit activity to the electromagnetic mean-field, are still unresolved and very much a topic of intense debate and research. We approach this problem by developing a dynamic network model for the interaction of pyramidal and inhibitory cells by adding two observable equations to the dynamical evolution law of the network. One observable accounts for the intracellular activity (i.e. spiking activity) and the other one for LFP. In particular, the LFP observable is made possible by monitoring the evolution of the dipole dynamics of each pyramidal cell characterized by in-flow and out-flow of currents in the apical and basal dendrites. In addition, following [5], we link single cell activity and their electrotonic properties to mesoscopic neurodynamics and their corresponding parameters by deriving an equivalent Amari neural field equation with mean-field coupling [6]. We also show the validity of this approach by large-scale computations for various connectivity topologies and demonstrate how this description could further our understanding of LFP.

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