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Electric Brain Signals
- Foundations and Applications of Biophysical Modeling
- Geir Halnes, Torbjørn V. Ness, Solveig Næss, Espen Hagen, Klas H. Pettersen, Gaute T. Einevoll
- Coming soon
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- Expected online publication date:
- May 2024
- Print publication:
- 06 June 2024
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It is common to study the electric activity of neurons by measuring the electric potential in the extracellular space of the brain. However, interpreting such measurements requires knowledge of the biophysics underlying the electric signals. Written by leading experts in the field, this volume presents the biophysical foundations of the signals as well as results from long-term research into biophysics-based forward-modeling of extracellular brain signals. This includes applications using the open-source simulation tool LFPy, developed and provided by the authors. Starting with the physical theory of electricity in the brain, this book explains how this theory is used to simulate neuronal activity and the resulting extracellular potentials. Example applications of the theory to model representations of real neural systems are included throughout, making this an invaluable resource for students and scientists who wish to understand the brain through analysis of electric brain signals, using biophysics-based modeling.
4 - Extracellular spikes and CSD
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- By Klas H. Pettersen, Norwegian University of Life Sciences, Norway, Henrik Lindén, Norwegian University of Life Sciences, Norway, Anders M. Dale, University of California San Diego, USA, Gaute T. Einevoll, Norwegian University of Life Sciences, Norway
- Edited by Romain Brette, Ecole Normale Supérieure, Paris, Alain Destexhe, Centre National de la Recherche Scientifique (CNRS), Paris
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- Book:
- Handbook of Neural Activity Measurement
- Published online:
- 05 October 2012
- Print publication:
- 06 September 2012, pp 92-135
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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).