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Hybrid scheme for modeling LFPs from spiking cortical network models

Espen Hagen1*, Maria L. Stavrinou1, Henrik Lindén2, David Dahmen3, Tom Tetzlaff3, Sacha J. Van Albada3, Sonja Grün3, Markus Diesmann3 and Gaute T. Einevoll1
  • 1 Norwegian University of Life Sciences, Dept. of Mathematical Sciences and Technology, Norway
  • 2 KTH Royal Institute of Technology, Dept. of Computational Biology, Sweden
  • 3 Jülich Research Center and JARA, Inst. of Neuroscience and Medicine (INM-6) and Inst. for Advanced Simulation (IAS-6), Germany

While recordings of extracellular potentials remain a common method for experimentally measuring neural activity, the interpretation of the low-frequency part, the local field potential (LFP), is not straightforward. Cortical LFPs seem to mainly stem from synaptic inputs, but the net LFP signal from several contributing laminar populations is difficult to assess, as the individual contributions will depend on their locations, the morphologies of the postsynaptic neurons, the spatial distribution of active synapses, and the level of correlations in synaptic inputs [1]. While most comprehensive cortical-network simulations are done with single-compartment models [2], multicompartmental neuronal modeling is in general required to calculate LFPs [1]. Here we present a hybrid LFP modeling approach where a network of single-compartment LIF neurons generates the spiking activity (Fig. 1A), while detailed multicompartment neuronal models are used to calculate the accompanying LFP (Fig. 1B-C). Our model describes a 1mm2 patch of cat V1, and we incorporate spatially specific pre- to post-synaptic inter- and intra-layer connectivity constrained by experimental observations [3] using reconstructed neuron morphologies of excitatory and inhibitory neurons in layers L2/3-L6 with passive membrane properties. Model specifications of neuron and synapse numbers within populations are taken from [2], while spatial connectivity profiles are based on [3]. Our hybrid simulation framework allows detailed analysis of how the LFP correlates with network activity and connectivity, and how spatially specific synapse distributions influence the LFP. Spiking network simulations [2] were implemented in NEST (www.nest-initiative.org), while simulations of LFPs from morphologically realistic neurons used LFPy (http://compneuro.umb.no/LFPy) along with NEURON [4].

Figure 1: Schematic illustration of the hybrid scheme. (A) Spiking activity generated in network simulations using single-compartment neurons [2] are used as input to multicompartmental neuron models to generate LFPs (B). LFP contributions from each postsynaptic population are calculated and superimposed (C).

Figure 1
Image

Acknowledgements

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no.269921(BrainScaleS) and the Research Council of Norway (eNeuro, Notur).

References

1. Linden H., Tetzlaff T., Potjans TC., Pettersen KH., Gruen S., Diesmann M., Einevoll GT. (2011). Modeling the spatial reach of the LFP. Neuron. 72:859-872. doi: 10.1016/j.neuron.2011.11.006
2. Potjans TC., Diesmann M. The Cell-Type Specific Cortical Microcircuit: Relating Structure and Activity in a Full-Scale Spiking Network Model. Cereb Cortex (2012). doi: 10.1093/cercor/bhs358.
3. Binzegger T., Douglas RJ., Martin KA. (2004). A quantitative map of the circuit of cat primary visual cortex. J Neurosci (2004) 24(39):8441-8453. doi: 10.1523/JNEUROSCI.1400-04.2004
4. Hines ML., Davison PA., Muller E. Neuron and Python. Front NeuroInf (2009) 3:1-12. doi: 10.3389/neuro.11.001.2009

Keywords: Spiking Neural network, Visual Cortex, multicompartmental modeling, forward model, local field potential (LFP)

Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.

Presentation Type: Poster

Topic: Computational neuroscience

Citation: Hagen E, Stavrinou ML, Lindén H, Dahmen D, Tetzlaff T, Van Albada SJ, Grün S, Diesmann M and Einevoll GT (2013). Hybrid scheme for modeling LFPs from spiking cortical network models. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00117

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Received: 08 Apr 2013; Published Online: 11 Jul 2013.

* Correspondence: Mr. Espen Hagen, Norwegian University of Life Sciences, Dept. of Mathematical Sciences and Technology, Ås, 1432, Norway, espen.hagen@fys.uio.no