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Modeling the local field potential by a large-scale layered cortical network model

Henrik Lindén1*, Tobias C. Potjans2, Gaute T. Einevoll1, Sonja Grün3 and Markus Diesmann3
  • 1 Norwegian University of Life Sciences, Norway
  • 2 Research Center Jülich, Germany
  • 3 RIKEN Brain Science Institute, Japan

The local field potential (LFP) is nowadays routinely recorded in electrophysiological experiments. However, there is yet very limited understanding about the origin of this signal, in particular with respect to the spatial arrangement of nearby neurons and the dynamics of the underlying spiking activity. The LFP is the low-frequency part of extracellular recordings and is often attributed to synaptic activity of a large number of cells near the recording electrode, whereas the high-frequency part reflects spiking activity of close by neurons. Laminar electrodes are used to record these signals layer-specifically across the whole depth of cortex [1]. In order to study how the LFP and its dynamics evolve from the cortical network we developed a simulation setup that allows to combine large-scale simulations of a layered point-neuron network (using NEST [2]) with multi-compartment single-cell simulations of reconstructed cortical cells (using NEURON [3]).

The layered cortical network model consists of 80000 integrate-and-fire neurons and accounts for about 90% of the synapses constituting the local cortical microcircuit. The layer-specific connections are based on an integrated data set [4] and result in layer-specific firing rates comparable to experimental data. The LFP model is based on simulations of reconstructed cell morphologies receiving synaptic input: We extract the transmembrane currents from all compartments of a cell and calculate the LFP as a weighted sum of these currents. The LFP of a population can then be obtained by summing over all contributing cells [5].

We combine the two models by using the layer-specific spiking activity generated by the network model as input for each multi-compartment neuron. For the same realization of network activity, this process is repeated for neurons with different spatial locations and in different layers. In this way, the LFP is constructed from the contributions of the individual neurons respecting the typical morphology of different neuron types and the correlation structure of the network dynamics. Finally, we provide a consistent simulation framework by applying a hierarchical representation of parameters and equivalent wiring algorithms [6] to both models.

Our joint model enables us to study the spatial contributions to the LFP and its relationship to the spiking activity of the network. We use the network-generated LFP to assess the number of neurons that contribute to the LFP signal and to identify the core populations that generate the LFP in a certain layer. Furthermore, we discuss the impact of different activity states on the LFP.

Partially funded by EU Grant 15879 (FACETS), BMBF Grant 01GQ0420 to BCCN Freiburg, Next-Generation Supercomputer Project of MEXT, Japan, The Helmholtz Alliance on Systems Biology and The Research Council of Norway (eScience Programme).

References

1. Einevoll GT et al. (2007) J Neurophysiol 97(3):2174-2190

2. Gewaltig M-O, Diesmann M (2007) Scholarpedia, 2 (4):1430

3. Carnevale NT, Hines ML (2006) The NEURON Book. Cambridge University Press

4. Potjans TC, Diesmann M (2008) 38th Soc for Neurosci Meeting, 16.1

5. Pettersen KH et al. (2008) J Comput Neurosci 24(3):291-313

6. Potjans TC, Diesmann M (2008) Neuroinformatics 2008. doi: 10.3389/conf.neuro.11.2008.01.087

Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009.

Presentation Type: Poster Presentation

Topic: Large scale modeling

Citation: Lindén H, Potjans TC, Einevoll GT, Grün S and Diesmann M (2019). Modeling the local field potential by a large-scale layered cortical network model. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.046

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Received: 22 May 2009; Published Online: 09 May 2019.

* Correspondence: Henrik Lindén, Norwegian University of Life Sciences, Ås, Norway, henrik.linden@umb.no