Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-f554764f5-qhdkw Total loading time: 0 Render date: 2025-04-10T18:26:40.994Z Has data issue: false hasContentIssue false

4 - Extracellular spikes and CSD

Published online by Cambridge University Press:  05 October 2012

By
Klas H. Pettersen ,
Henrik Lindén ,
Anders M. Dale  and
Gaute T. Einevoll
Edited by
Romain Brette  and
Alain Destexhe
Klas H. Pettersen
Affiliation:
Norwegian University of Life Sciences, Norway
Henrik Lindén
Affiliation:
Norwegian University of Life Sciences, Norway
Anders M. Dale
Affiliation:
University of California San Diego, USA
Gaute T. Einevoll
Affiliation:
Norwegian University of Life Sciences, Norway
Romain Brette
Affiliation:
Ecole Normale Supérieure, Paris
Alain Destexhe
Affiliation:
Centre National de la Recherche Scientifique (CNRS), Paris
Get access

Summary

Introduction

Extracellular recordings have been, and still are, the main workhorse when measuring neural activity in vivo. In single-unit recordings sharp electrodes are positioned close to a neuronal soma, and the firing rate of this particular neuron is measured by counting spikes, that is, the standardized extracellular signatures of action potentials (Gold et al., 2006). For such recordings the interpretation of the measurements is straightforward, but complications arise when more than one neuron contributes to the recorded extracellular potential. For example, if two firing neurons of the same type are at about the same distance from their somas to the tip of the recording electrode, it may be very difficult to sort the spikes according to from which neuron they originate.

The use of two (stereotrode (McNaughton et al., 1983)), four (tetrode (Recce and O'Keefe, 1989;Wilson andMcNaughton, 1993; Gray et al., 1995; Jog et al., 2002)) or more (Buzsáki, 2004) close-neighbored recording sites allows for improved spike sorting, since the different distances from the electrode tips or contacts allow for triangulation. With present recording techniques and clustering methods one can sort out spike trains from tens of neurons from single tetrodes and from hundreds of neurons with multi-shank electrodes (Buzsáki, 2004).

Information about spiking is typically extracted from the high-frequency band (≳500 Hz) of extracellular potentials. Since these high-frequency signals generally stem from an unknown number of spiking neurons in the immediate vicinity of the electrode contact, this is called multi-unit activity (MUA).

Type
Chapter
Information
Handbook of Neural Activity Measurement , pp. 92 - 135
Publisher: Cambridge University Press
Print publication year: 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Bédard, C. and Destexhe, A. (2009). Macroscopic models of local field potentials and the apparent 1/f noise in brain activity. Biophys. J., 96, 2589–2603.CrossRefGoogle ScholarPubMed
Bédard, C., Kröger, H. and Destexhe, A. (2004). Modeling extracellular field potentials and the frequency-filtering properties of extracellular space. Biophys. J., 86, 1829–1842.CrossRefGoogle ScholarPubMed
Bédard, C., Kröger, H. and Destexhe, A. (2006a). Does the 1/f frequency scaling of brain signals reflect self-organized critical states?Phys. Rev. Lett., 97, 118102.CrossRefGoogle ScholarPubMed
Bédard, C., Kröger, H. and Destexhe, A. (2006b). Model of low-pass filtering of local field potentials in brain tissue. Phys. Rev. E, 73, 051911.CrossRefGoogle ScholarPubMed
Berens, P., Keliris, G. A., Ecker, A. S., Logothetis, N. and 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. Front. Syst. Neurosci., 10.3389/neuro.06/002.2008Google ScholarPubMed
Blomquist, P., Devor, A., Indahl, U. G., Ulbert, I., Einevoll, G. T. and Dale, A. M. (2009). Estimation of thalamocortical and intracortical network models from joint thalamic single-electrode and cortical laminar-electrode recordings in the rat barrel system. PLOS Comput. Biol., 5 (3), e1000328.CrossRefGoogle ScholarPubMed
Bower, J. M. and Beeman, D. (1998). The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System (2nd edition). New York: Springer.CrossRefGoogle Scholar
Buzsáki, G. (2004). Large-scale recording of neuronal ensembles. Nature Neurosci., 7, 446–451.CrossRefGoogle ScholarPubMed
Buzsáki, G. (2006). Rhythms of the Brain. New York: Oxford University Press.CrossRefGoogle Scholar
Carnevale, N. T. and Hines, M. L. (2006). The NEURON Book. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Csicsvari, J., Hirase, H., Czurkó, A., Mamiya, A. and Buzsáki, G. (1999). Oscillatory coupling of hippocampal pyramidal cells and interneurons in the behaving rat. J. Neurosci., 19, 274–287.CrossRefGoogle ScholarPubMed
Dayan, P. and Abbott, L. F. (2001). Theoretical Neuroscience. Cambridge, MA: MIT Press.Google Scholar
Di, S., Baumgartner, C. and Barth, D. S. (1990). Laminar analysis of extracellular field potentials in rat vibrissa/barrel cortex. J. Neurophysiol., 63, 832–840.CrossRefGoogle ScholarPubMed
Einevoll, G. T., Pettersen, K. H., Devor, A., Ulbert, I., Halgren, E. and Dale, A. M. (2007). Laminar population analysis: estimating firing rates and evoked synaptic activity from multielectrode recordings in rat barrel cortex. J. Neurophysiol., 97, 2174–2190.CrossRefGoogle ScholarPubMed
Fee, M. S., Mitra, P. M. and Kleinfeld, D. (1996). Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability. J. Neurosci. Methods, 69, 175–188.CrossRefGoogle ScholarPubMed
Franke, F., Natora, M., Boucsein, C., Munk, M. H. J. and Obermayer, K. (2010). An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes. J. Comput. Neurosci., 29, 127–148.CrossRefGoogle ScholarPubMed
Freeman, J. A. and Nicholson, C. (1975). Experimental optimization of current sourcedensity technique for anuran cerebellum. J. Neurophysiol., 38, 369–382.CrossRefGoogle ScholarPubMed
Freeman, W. J. and Zhai, J. (2009). Simulated power spectral density (PSD) of background electrocorticogram (ECoG). Cogn. Neurodyn., 3, 97–103.CrossRefGoogle Scholar
Freeman, W. J., Holmes, M. D., Burke, B. C. and Vanthalo, S. (2003). Spatial spectra of scalp EEG and EMB from awake humans. Clin. Neurophysiol., 114, 1053–1068.CrossRefGoogle Scholar
Gabriel, S., Lau, R. W. and Gabriel, C. (1996). The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. Phys. Med. Biol., 41, 2271–2293.CrossRefGoogle ScholarPubMed
Gold, C., Henze, D. A., Koch, C. and Buzsáki, G. (2006). On the origin of the extracellular action potential waveform: a modeling study. J. Neurophysiol., 95, 3113–3128.CrossRefGoogle ScholarPubMed
Gold, C., Henze, D. A. and Koch, C. (2007). Using extracellular action potential recordings to constrain compartmental models. J. Comput. Neurosci., 23, 39–58.CrossRefGoogle ScholarPubMed
Goto, T., Hatanaka, R., Ogawa, T., Sumiyoshi, A., Riera, J. and Kawashima, R. (2010). An evaluation of the conductivity profile in the somatosensory barrel cortex of Wistar rats. J. Neurophysiol., 104, 3388–3412.CrossRefGoogle ScholarPubMed
Gray, C.M., Maldonado, P. E., Wilson, M. and McNaughton, B. (1995). Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex. J. Neurosci. Methods, 63, 43–54.CrossRefGoogle ScholarPubMed
Grimnes, S. and Martinsen, ø. G. (2008). Bioimpedance and Bioelectricity Basics (2nd edition). Academic Press.Google Scholar
Hämäläinen, M., Hari, R., Ilmoniemi, R., Knuutila, J. and Lounasmaa, O. (1993). Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys., 65, 413–497.CrossRefGoogle Scholar
Harris, K. D., Henze, D. A., Csicsvari, J., Hirase, H. and Buzsaki, G. (2000). Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol., 84, 401–414.CrossRefGoogle ScholarPubMed
He, B. J., Zempel, J. M., Snyder, A. Z. and Raichle, M. E. (2010). The temporal structures and functional significance of scale-free brain activity. Neuron, 66, 353–369.CrossRefGoogle ScholarPubMed
Henze, D. A., Borhegyi, Z., Csicsvari, J., Mamiya, A., Harris, K. D. and Buzsaki, G. (2000). Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysiol., 84, 390–400.CrossRefGoogle ScholarPubMed
Holt, G. R. and Koch, C. (1999). Electrical interactions via the extracellular potential near cell bodies. J. Comput. Neurosci., 6, 169–184.CrossRefGoogle ScholarPubMed
Hubel, D. H. (1957). Tungsten microelectrode for recording from single units. Science, 125, 549–550.CrossRefGoogle ScholarPubMed
Jackson, J. (1998). Classical Electrodynamics. Hoboken, NJ: Wiley.Google Scholar
Jog, M. S., Connolly, C. I., Kubota, Y., Iyengar, D. R., Garrido, L., Harlan, R. and Graybiel, A. M. (2002). Tetrode technology: advances in implantable hardware, neuroimaging, and data analysis techniques. J. Neurosci. Methods, 117, 141–152.CrossRefGoogle ScholarPubMed
Johnston, D. and Wu, S. M.-S. (1994). Foundation of Cellular Neurophysiology. Cambridge, MA: MIT Press.Google Scholar
Katzner, S., Nauhaus, I., Benucci, A., Bonin, V., Ringach, D. L. and Carandini, M. (2009). Local origin of field potentials in visual cortex. Neuron, 61, 35–41.CrossRefGoogle ScholarPubMed
Kim, S. and McNames, J. (2007). Automatic spike detection based on adaptive templage matching for extracellular neural recordings. J. Neurosci. Methods, 165, 165–174.CrossRefGoogle ScholarPubMed
Kreiman, G., Hung, C. P., Kraskov, A., Quiroga, R. Q., Poggio, T. and DiCarlo, J. J. (2006). Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex. Neuron, 49, 433–445.CrossRefGoogle ScholarPubMed
Leski, S., Wojcik, D. K., Tereszczuk, J., Swiejkowski, D. A., Kublik, E. and Wrobel, A. (2007). Inverse current-source density method in 3D: reconstruction fidelity, boundary effects, and influence of distant sources. Neuroinformatics, 5, 207–222.CrossRefGoogle ScholarPubMed
Leski, S., Pettersen, K. H., Tunstall, B., Einevoll, G. T., Gigg, J. and Wojcik, D. K. (2011). Inverse current source density method in two dimensions: inferring neural activation from multielectrode recordings. Neuroinformatics, 9 (4), 401–425.CrossRefGoogle ScholarPubMed
Levina, A., Herrmann, J. M. and Geisel, T. (2007). Dynamical synapses causing selforganized criticality in neural networks. Nature Physics, 3, 857–860.CrossRefGoogle Scholar
Lewicki, M. S. (1998). A review of methods for spike sorting: the detection and classification on neural action potentials. Network: Comput. Neural Syst., 9, R53–R78.CrossRefGoogle ScholarPubMed
Lindén, H., Pettersen, K. H., Tetzlaff, T., Potjans, T., Denker, M., Diesmann, M., Grün, S. and Einevoll, G. T. (2009a). Estimating the spatial range of local field potentials in a cortical population model. BMC Neurosci., 10 (Supplement 1), 224.CrossRefGoogle Scholar
Lindén, H., Potjans, T., Einevoll, G. T., Denker, M., Grün, S., and Diesmann, M. (2009b). Modeling the local field potential by a large-scale layered cortical network model. Fronti. Neuroinf. Conference Abstract: Neuroinformatics. doi: 10.3389/conf.neuro.11.2009.08.046.Google Scholar
Lindén, H., Pettersen, K. H. and Einevoll, G. T. (2010). Intrinsic dendritic filtering gives low-pass power spectra of local field potentials. J. Comput. Neurosci., 29, 423–444.CrossRefGoogle ScholarPubMed
Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M. and Ilmoniemi, R. J. (2001). Longrange temporal correlations and scaling behavior in human brain oscillations. J. Neurosci., 21, 1370–1377.CrossRefGoogle Scholar
Liu, J. and Newsome, W. T. (2006). Local field potential in cortical area MT. Stimulus tuning and behavioral correlations. J. Neurosci., 26, 7779–7790.CrossRefGoogle ScholarPubMed
Logothetis, N. K., Kayser, C. and Oeltermann, A. (2007). In vivo measurement of cortical impedance spectrum in monkeys: implications for signal propagation. Neuron, 55, 809–823.CrossRefGoogle ScholarPubMed
López-Aguado, L., Ibarz, J. M. and Herreras, O. (2001). Acitivity-dependent changes of tissue resisitivity in the CA1 region in vivo are layer-specific: modulation of evoked potentials. Neuron, 108, 249–262.Google Scholar
Mainen, Z. F. and Sejnowski, T. J. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382, 363–366.CrossRefGoogle ScholarPubMed
McNaughton, B. L., O'Keefe, J. and Barnes, C. A. (1983). The stereotrode: a new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records. J. Neurosci. Methods, 8, 391–397.CrossRefGoogle ScholarPubMed
Miller, K. J., Sorensen, L. B., Ojemann, J. G. and den Nijs, M. (2009). Power-law scaling in the brain surface electric potential. PLoS Comp. Biol., 5, e1000609.CrossRefGoogle ScholarPubMed
Milstein, J. N. and Koch, C. (2008). Dynamic moment analysis of the extracellular electric field of a biologically realistic spiking neuron. Neural Comput., 20, 2070–2084.CrossRefGoogle ScholarPubMed
Milstein, J., Mormann, F., Fried, I. and Koch, C. (2009). Neuronal shot noise and Brownian 1/f2 behavior in the local field potential. PLoS ONE, 4, e4338.CrossRefGoogle ScholarPubMed
Mitzdorf, U. (1985). Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol. Rev., 65, 37–100.CrossRefGoogle ScholarPubMed
Moffitt, M. A. and McIntyre, C. C. (2005). Model-based analysis of cortical recording with silicon microelectrodes. Clin. Neurophysiol., 116, 2240–2250.CrossRefGoogle ScholarPubMed
Monto, S., Palva, S., Voipio, J. and Palva, J. M. (2008). Very slow EEG fluctuations predict the dynamics of stimulus detection and oscillation amplitudes in humans. J. Neurosci., 28, 8268–8272.CrossRefGoogle ScholarPubMed
Mountcastle, V. B. (1997). The columnar organization of the neocortex. Brain, 120, 701–722.CrossRefGoogle ScholarPubMed
Nadasdy, Z., Csicsvari, J., Penttonen, M., Hetke, J., Wise, K. and Buzsaki, G. (1998). Extracellular recording and analysis of neuronal activity: from single cells to ensembles. In: H., Eichenbaum and J. L., Davis (editors), Neuronal Ensembles. New York: Wiley.Google Scholar
Nelson, M. J. and Pouget, P. (2010). Do electrode properties create a problem in interpreting local field potential recordings?J. Neurophysiol., 103, 2325–2317.CrossRefGoogle ScholarPubMed
Nelson, M. J., Pouget, P., Nilsen, E. A., Patten, C. D. and Schall, J. D. (2008). Review of signal distortion through metal microelectrode recording circuits and filters. J. Neurosci. Methods, 169, 141–157.CrossRefGoogle ScholarPubMed
Nicholson, C. and Freeman, J. A. (1975). Theory of current source-density analysis and determination of conductivity tensor for anuran cerebellum. J. Neurophysiol., 38, 356–368.CrossRefGoogle ScholarPubMed
Nicholson, C. and Llinas, R. (1971). Field potentials in the alligator cerebellum and theory of their relationship to Purkinje cell dendritic spikes. J. Neurophysiol., 34, 509–531.CrossRefGoogle ScholarPubMed
Nicholson, C. and Llinás, R. (1975). Real time current source-density analysis using multielectrode array in cat cerebellum. Brain Res., 100, 418–424.CrossRefGoogle Scholar
Normann, R. A., Maynard, E. M., Rousche, P. J. and Warren, D. J. (1999). A neural interface for a cortical vision prosthesis. Vision Res., 39, 2577–2587.CrossRefGoogle ScholarPubMed
Nunez, P. L. and Srinivasan, R. (2006). Electric Fields of the Brain: The Neurophysics of EEG. Oxford: Oxford University Press.CrossRefGoogle Scholar
Pettersen, K. H. and Einevoll, G. T. (2008). Amplitude variability and extracellular lowpass filtering of neuronal spikes. Biophys. J., 94, 784–802.CrossRefGoogle Scholar
Pettersen, K. H. and Einevoll, G. T. (2009). Neurophysics: what the telegrapher's equation has taught us about the brain. In: Ø., Martinsen and ø., Jensen (editors), An Anthology of Developments in Clinical Engineering and Bioimpedance: Festschrift for Sverre Grimnes. Unpublished, Oslo.Google Scholar
Pettersen, K. H., Devor, A., Ulbert, I., Dale, A. M. and Einevoll, G. T. (2006). Currentsource density estimation based on inversion of electrostatic forward solution: effects of finite extent of neuronal activity and conductivity discontinuities. J. Neurosci. Methods, 154, 116–133.CrossRefGoogle ScholarPubMed
Pettersen, K. H., Hagen, E. and Einevoll, G. T. (2008). Estimation of population firing rates and current source densities from laminar electrode recordings. J. Comput. Neurosci., 24, 291–313.CrossRefGoogle ScholarPubMed
Pouzat, C. and Chaffiol, A. (2009). Automatic spike train analysis and report generation. An implementation with R, R2HTML and STAR. J. Neurosci. Methods, 181, 119–144.CrossRefGoogle Scholar
Pritchard, W. S. (1992). The brain in fractal time: 1/f-like power spectrum scaling of the human electroencephalogram. Int. J. Neurosci., 66, 119–129.CrossRefGoogle ScholarPubMed
Quian Quiroga, R. (2007). Spike sorting. Scholarpedia, 2, 3583.CrossRefGoogle Scholar
Quian Quiroga, R., Nadasdy, R. and Ben-Shaul, Y. (2004). Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput., 16, 1661–1687.Google Scholar
Rall, W. (1962). Electrophysiology of a dendritic neuron model. Biophys. J., 2, 145–167.CrossRefGoogle ScholarPubMed
Rall, W. and Shepherd, G. M. (1968). Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. J. Neurophysiol., 31, 884–915.CrossRefGoogle ScholarPubMed
Rappelsberger, P., Pockberger, H. and Petsche, H. (1981). Current source density analysis: methods and application to simultaneously recorded field potentials of the rabbit's visual cortex. Pflügers Arch., 389, 159–170.CrossRefGoogle ScholarPubMed
Recce, M. and O'Keefe, J. (1989). The tetrode: a new technique for multi-unit extracellular recording. Soc. Neurosci. Abstr., 15: 1250.Google Scholar
Rutishauser, U., Schuman, E. M. and Mamelak, A. N. (2006). Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings in vivo. J. Neurosci. Methods, 154, 204–224.CrossRefGoogle ScholarPubMed
Schroeder, C. E., Lindsley, R. W., Specht, C., Marcovici, A., Smiley, J. F. and Javitt, D. C. (2001). Somatosensory input to auditory association cortex in the macaque monkey. J. Neurophysiol., 85, 1322–1327.CrossRefGoogle ScholarPubMed
Segev, I. and Burke, R. (1998). Compartmental model of complex neurons. In: C., Koch and I., Segev (editors), Methods in Neuronal Modeling: From Ions To Network. Cambridge, MA: MIT Press.Google Scholar
Shoham, S. and Nagarajan, S. S. (2004). The theory of CNS recording. In: K. W., Horch and G. S., Dhillon (editors), Neuroprosthetics: Theory and Applications. Singapore: World Scientific, pp. 448–471.Google Scholar
Shoham, S., O'Connor, D. H. and Segev, R. (2006). How silent is the brain: is there a “dark matter” problem in neuroscience?J. Comp. Physiol. A, 192, 777–784.CrossRefGoogle Scholar
Smith, L. S. and Mtetwa, N. (2007). A tool for synthesizing spike trains with realistic interference. J. Neurosci. Methods, 159, 170–180.CrossRefGoogle ScholarPubMed
Stuart, G., Spruston, N. and Häusser, M. (editors) (2007). Dendrites (2nd edition). Oxford: Oxford University Press.CrossRef
Sukov, W. and Barth, D. S. (1998). Three-dimensional analysis of spontaneous and thalamically evoked gamma oscillations in auditory cortex. J. Neurophysiol., 79, 2875–2884.CrossRefGoogle ScholarPubMed
Takekawa, T., Isomura, Y. and Fukai, T. (2010). Accurate spike sorting for multiunit recordings. Eur. J. Neurosci., 31, 263–272.CrossRefGoogle Scholar
Ulbert, I., Halgren, E., Heit, G. and Karmos, G. (2001). Multiple microelectrode-recording system for human intracortical applications. J. Neurosci. Methods, 106, 69–79.CrossRefGoogle ScholarPubMed
Vaknin, G., DiScenna, P. G. and Teyler, T. J. (1988). A method for calculating current source density (CSD) analysis without resorting to recording sites outside the sampling volume. J. Neurosci. Methods, 24, 131–135.CrossRefGoogle ScholarPubMed
Wehr, M., Pezaris, J. S. and Sahani, M. (1999). Simultaneous paired intracellular and tetrode recordings for evaluation the performance of spike sorting algorithms. Neurocomputing, 26–27, 1061–1068.Google Scholar
Wilson, M. A. and McNaughton, B. L. (1993). Dynamics of the hippocampal ensemble code for space. Science, 261, 1055–1058.CrossRefGoogle ScholarPubMed
Xing, D., Yeh, C.-I. and Shapley, R. M. (2009). Spatial spread of the local field potential and its laminar variation in visual cortex. J. Neurosci., 29, 11540–11549.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×