Email updates

Keep up to date with the latest news and content from BMC Neuroscience and BioMed Central.

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

Open Access Open Badges 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

For all author emails, please log on.

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:

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.


  1. Amari SI: Dynamics of pattern formation in lateral-inhibition type neural fields.

    Biological Cybernetics 1997, 27:77-87. Publisher Full Text OpenURL

  2. Abbott LF: Lapique's introduction of the integrate-and-fire model neuron (1907).

    Brain Research Bulletin 1999, 50:303-304. PubMed Abstract | Publisher Full Text OpenURL

  3. beim Graben P, Kurths J: Simulating global properties of electroencephalograms with minimal random neural networks.

    Neurocomputing 2008, 71:999-1007. Publisher Full Text OpenURL

  4. Hodgkin A, Huxley A: A quantitative description of membrane current and its application to conduction and excitation in nerve.

    J Physiology 1952, 117:500-544. OpenURL

  5. Richardson KA, Schiff SJ, Gluckman BJ: Control of traveling waves in the mammalian cortex.

    Physical Review Letters 2005, 94:028103. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  6. Wilson HR, Cowan JD: A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue.

    Kybernetik 1973, 13:55-80. PubMed Abstract | Publisher Full Text OpenURL