Intrinsic dendritic filtering gives low-pass power spectra of local field potentials
- 1.3k Downloads
The local field potential (LFP) is among the most important experimental measures when probing neural population activity, but a proper understanding of the link between the underlying neural activity and the LFP signal is still missing. Here we investigate this link by mathematical modeling of contributions to the LFP from a single layer-5 pyramidal neuron and a single layer-4 stellate neuron receiving synaptic input. An intrinsic dendritic low-pass filtering effect of the LFP signal, previously demonstrated for extracellular signatures of action potentials, is seen to strongly affect the LFP power spectra, even for frequencies as low as 10 Hz for the example pyramidal neuron. Further, the LFP signal is found to depend sensitively on both the recording position and the position of the synaptic input: the LFP power spectra recorded close to the active synapse are typically found to be less low-pass filtered than spectra recorded further away. Some recording positions display striking band-pass characteristics of the LFP. The frequency dependence of the properties of the current dipole moment set up by the synaptic input current is found to qualitatively account for several salient features of the observed LFP. Two approximate schemes for calculating the LFP, the dipole approximation and the two-monopole approximation, are tested and found to be potentially useful for translating results from large-scale neural network models into predictions for results from electroencephalographic (EEG) or electrocorticographic (ECoG) recordings.
KeywordsLocal field potential Single neuron Forward modeling Frequency dependence EEG
This work was supported by the Research Council of Norway (eScience, NOTUR). We thank one of the reviewers for bringing relevant literature on human depth-resolved LFP recordings to our attention.
- Arieli, A. (1992). Novel strategies to unravel mechanisms of cortical function: From macro- to micro-electrophysiological recordings. In A. Aertsen, & V. Braitenberg (Eds.), Information processing in the cortex. New York: Springer.Google Scholar
- Berens, P., Keliris, G. A., Ecker, A. S., Logothetis, N., & Tolias, A. S. (2008). Comparing the feature selectivity of the gamma-band of the local field potential and the underlying spiking activity in primate visual cortex. Frontiers in Systems Neuroscience, 2(2). doi: 10.3389/neuro.06/002.2008.PubMedGoogle Scholar
- Jackson, J. (1998). Classical Electrodynamics. NJ: Wiley, Hoboken.Google Scholar
- Johnston, D., & Wu, S. M.-S. (1995) Foundations of cellular neurophysiology, (Chapter 14). Cambridge, MA: MIT Press.Google Scholar
- Koch, C. (1998). Biophysics of computation. New York, NY: Oxford.Google Scholar
- Lindén, H., Potjans, T. C., Einevoll, G. T., Grün, S., & Diesmann, M. (2009b). Modeling the local field potential by a large-scale layered cortical network model. Frontiers in Neuroinformatics, Conference Abstract: 2nd INCF Congress of Neuroinformatics. doi: 10.3389/conf.neuro.11.2009.08.046.
- Pettersen, K. H., Lindén, H., Dale, A. M., & Einevoll, G. T. (2010). Extracellular spikes and current-source density. In R. Brette, & A. Destexhe (Eds.), Handbook of neural activity measurements. Cambridge, UK: Cambridge University Press.Google Scholar
- Plonsey, R. (1969). Bioelectric phenomena. New York: McGraw-Hill.Google Scholar
- Plonsey, R., & Barr, R. C. (2007). Bioelectricity: A quantitative approach. New York: Springer.Google Scholar