2018
DOI: 10.3389/fninf.2018.00092
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Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0

Abstract: Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the m… Show more

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Cited by 123 publications
(172 citation statements)
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“…Since LFPy 2.0 (Hagen et al, 2018), the tool has been extended to also support forward-model predictions accounting for anisotropic media, that is, with different conductivity in different directions (Goto et al, 2010), and discontinuous media where the conductivity is piecewise constant in a single direction. The latter can for example be used to mimic in vitro experimental setups using microelectrode arrays (MEAs) , or to mimic in vivo conditions where a jump in conductivity can be expected such as at the boundary between the brain and CSF.…”
Section: Applicationmentioning
confidence: 99%
See 3 more Smart Citations
“…Since LFPy 2.0 (Hagen et al, 2018), the tool has been extended to also support forward-model predictions accounting for anisotropic media, that is, with different conductivity in different directions (Goto et al, 2010), and discontinuous media where the conductivity is piecewise constant in a single direction. The latter can for example be used to mimic in vitro experimental setups using microelectrode arrays (MEAs) , or to mimic in vivo conditions where a jump in conductivity can be expected such as at the boundary between the brain and CSF.…”
Section: Applicationmentioning
confidence: 99%
“…Finally, in contrast to its first release where the tool only supported simulations with a single cell instantiation at the time, LFPy also supports networks of synaptically interconnected neurons. Such network simulations can be executed on a single physical machine, but larger network simulations is better executed in parallel on highperformance computing (HPC) facilities (Hagen et al, 2018).…”
Section: Applicationmentioning
confidence: 99%
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“…Hagen et al developed ViSAPy [19], a Python-based simulator that uses multi-compartment simulation of single neurons to generate spikes, network modeling of point-neurons in NEST [10] to generate synaptic inputs onto the spiking neurons, and experimentally fitted noise. ViSAPy runs a full network simulation in NEURON [8] and computes the extracellular potentials using LFPy [30,18]. ViSAPy implements a Python application programming interface (API) which allows the user to set multiple parameters for the network simulation providing the synaptic input, the probe design, and the noise model generator.…”
Section: Introductionmentioning
confidence: 99%