This title appears in the Scientific Report :
2014
LFPy and hybrid scheme for local field potentials. CNS2014 tutorial T4: Modeling and analysis of extracellular potentials
LFPy and hybrid scheme for local field potentials. CNS2014 tutorial T4: Modeling and analysis of extracellular potentials
T4: Modeling and analysis of extracellular potentialsGaute Einevoll, Norwegian University of Life Sciences, Ås, NorwaySzymon Łęski (Nencki Institute of Experimental Biology, Warsaw)Espen Hagen (Norwegian University of Life Sciences, Ås)While extracellular electrical recordings have been the main wo...
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Personal Name(s): | Hagen, Espen (Corresponding Author) |
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Contributing Institute: |
Computational and Systems Neuroscience; INM-6 |
Published in: | 2014 |
Imprint: |
2014
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Conference: | CNS 2014 Québec City: July 26-31, 2014, Quebec City (Candada), 2014-07-26 - 2014-07-31 |
Document Type: |
Conference Presentation |
Research Program: |
Supercomputing and Modelling for the Human Brain Helmholtz Alliance on Systems Biology The Human Brain Project Brain-inspired multiscale computation in neuromorphic hybrid systems Signalling Pathways and Mechanisms in the Nervous System Theory, modelling and simulation |
Publikationsportal JuSER |
T4: Modeling and analysis of extracellular potentialsGaute Einevoll, Norwegian University of Life Sciences, Ås, NorwaySzymon Łęski (Nencki Institute of Experimental Biology, Warsaw)Espen Hagen (Norwegian University of Life Sciences, Ås)While extracellular electrical recordings have been the main workhorse in electrophysiology, the interpretation of such recordings is not trivial [1,2,3]. The recorded extracellular potentials in general stem from a complicated sum of contributions from all transmembrane currents of the neurons in the vicinity of the electrode contact. The duration of spikes, the extracellular signatures of neuronal action potentials, is so short that the high-frequency part of the recorded signal, the multi-unit activity (MUA), often can be sorted into spiking contributions from the individual neurons surrounding the electrode [4]. No such simplifying feature aids us in the interpretation of the low-frequency part, the local field potential (LFP). To take a full advantage of the new generation of silicon-based multielectrodes recording from tens, hundreds or thousands of positions simultaneously, we thus need to develop new data analysis methods grounded in the underlying biophysics [1,3,4]. This is the topic of the present tutorial.In the first part of this tutorial we will go throughthe biophysics of extracellular recordings in the brain,a scheme for biophysically detailed modeling of extracellular potentials and the application to modeling single spikes [5-7], MUAs [8] and LFPs, both from single neurons [9] and populations of neurons [8,10,11], andmethods forestimation of current source density from LFP data, such as the iCSD [12-14] and kCSD methods [15], anddecomposition of recorded signals in cortex into contributions from various laminar populations, i.e., (i) laminar population analysis (LPA) [16,17] based on joint modeling of LFP and MUA, and (ii) a scheme using LFP and known constraints on the synaptic connections [18]In the second part, the participants will get demonstrations and, if wanted, hands-on experience withLFPy (software.incf.org/software/LFPy) [19], a versatile tool based on Python and the simulation program NEURON [20] (www.neuron.yale.edu/) for calculation of extracellular potentials around neurons, andtools for iCSD analysis, in particular,CSDplotter (for linear multielectrodes [8]) (software.incf.org/software/csdplotter)iCSD 2D (for 2D multishank electrodes [14]) (software.incf.org/software/icsd-2d)Further, new results from applying the biophysical forward-modelling scheme to predict LFPs from comprehensive structured network models, in particularthe Traub-model for thalamocortical activity [21], andthe Potjans-Diesmann microcircuit model for a visual cortical column [22,23],will be presented.[1] KH Pettersen et al, “Extracellular spikes and CSD” in Handbook of Neural Activity Measurement, Cambridge (2012)[2] G Buzsaki et al, Nature Reviews Neuroscience 13:407 (2012)[3] GT Einevoll et al, Nature Reviews Neuroscience 14:770 (2013)[4] GT Einevoll et al, Current Opin Neurobiol 22:11 (2012)[5] G Holt, C Koch, J Comp Neurosci 6:169 (1999)[6] J Gold et al, J Neurophysiol 95:3113 (2006)[7] KH Pettersen and GT Einevoll, Biophys J 94:784 (2008)[8] KH Pettersen et al, J Comp Neurosci 24:291 (2008)[9] H Lindén et al, J Comp Neurosci 29: 423 (2010)[10] H Lindén et al, Neuron 72:859 (2011)[11] S Łęski et al, PLoS Comp Biol 9:e1003137 (2013)[12] KH Pettersen et al, J Neurosci Meth 154:116 (2006)[13] S Łęski et al, Neuroinform 5:207 (2007)[14] S Łęski et al, Neuroinform 9:401 (2011)[15] J Potworowski et al, Neural Comp 24:541 (2012)[16] GT Einevoll et al, J Neurophysiol 97:2174 (2007)[17] P Blomquist et al, PLoS Comp Biol 5:e1000328 (2009)[18] SL Gratiy et al, Front Neuroinf 5:32 (2011)[19] H Lindén et al, Front Neuroinf 7:41 (2014)[20] ML Hines et al, Front Neuroinf 3:1 (2009)[21] R Traub et al, J Neurophysiol 93:2194 (2005)[22] TC Potjans and M Diesmann, Cereb Cort 24:785 (2014)[23] E Hagen et al, BMC Neuroscience 14(Suppl 1):P119 (2013) |