LFPy – multimodal modeling of extracellular neuronal recordings in Python

Hagen, Espen; Næss, Solveig; Ness, Torbjørn V.; Einevoll, Gaute T.

doi:10.1101/620286

Cited by 11 publications

(16 citation statements)

References 54 publications

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“…Nevertheless, our results support applicability of the computationally less expensive GLIF network models (approximately >8,000 times faster), although ultimately, the level of resolution to use must be based on the scientific question under investigation. For instance, computing the extracellular field potential requires spatially extended neurons (Rall and Shepherd, 1968;Lindé n et al, 2011;Einevoll et al, 2013;Reimann et al, 2013;Hagen et al, 2019). Developing our V1 simulacra at two levels of resolution enables a larger spectrum of possible studies.…”

Section: Discussionmentioning

confidence: 99%

Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex

Billeh

Cai

Gratiy

et al. 2020

Neuron

210

597

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Section: Discussionmentioning

confidence: 99%

Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex

Billeh

Cai

Gratiy

et al. 2020

Neuron

210

597

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show abstract

“…However, electrical activity of neural circuits forms the basis of cognitive, sensory, and motor functions. Current electrophysiological techniques, either based on glass micropipettes [29] or on substrate-integrated electrodes [30] (figure 2 [31] and extracellular potentials were simulated using LFPy 2.0 [32] and NEURON [33] with hydrogel conductivity of 1.46 S m −1 . Under such conditions, extracellular amplitudes exceeding a threshold of 10 µV are reached only within 15 µm of the soma.…”

Section: Measuring 3d Neuronal Activity With Cmementioning

confidence: 99%

A multimodal 3D neuro-microphysiological system with neurite-trapping microelectrodes

et al. 2022

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Three-dimensional cell technologies as pre-clinical models are emerging tools for mimicking the structural and functional complexity of the nervous system. The accurate exploration of phenotypes in engineered 3D neuronal cultures, however, demands morphological, molecular and especially functional measurements. Particularly crucial is measurement of electrical activity of individual neurons with millisecond resolution. Current techniques rely on customized electrophysiological recording set-ups, characterized by limited throughput and poor integration with other readout modalities. Here we describe a novel approach, using multiwell glass microfluidic microelectrode arrays, allowing non-invasive electrical recording from engineered 3D neural tissues. We demonstrate parallelized studies with reference compounds, calcium imaging and optogenetic stimulation. Additionally, we show how microplate compatibility allows automated handling and high-content analysis of human induced pluripotent stem cell–derived neurons. This microphysiological platform opens up new avenues for high-throughput studies on the functional, morphological and molecular details of neurological diseases and their potential treatment by therapeutic compounds.

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“…In the following sections we shall review previous modeling works, and insights from simulating electric potentials at these different scales. We use the software LFPy [Lindén et al, 2014;Hagen et al, 2018Hagen et al, , 2019, which has volume conductor theory incorporated and can in principle be used to compute extracellular potentials on arbitrarily large spatial scales, surrounding arbitrarily large neuronal populations.…”

Section: No Effects From Ion Diffusionmentioning

confidence: 99%

Computing extracellular electric potentials from neuronal simulations

Ness¹,

Halnes²,

Næss³

et al. 2020

Preprint

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Measurements of electric potentials from neural activity have played a key role in neuroscience for almost a century, and simulations of neural activity is an important tool for understanding such measurements. Volume conductor (VC) theory is used to compute extracellular electric potentials such as extracellular spikes, MUA, LFP, ECoG and EEG surrounding neurons, and also inversely, to reconstruct neuronal current source distributions from recorded potentials through current source density methods. In this book chapter, we show how VC theory can be derived from a detailed electrodiffusive theory for ion concentration dynamics in the extracellular medium, and show what assumptions that must be introduced to get the VC theory on the simplified form that is commonly used by neuroscientists. Furthermore, we provide examples of how the theory is applied to compute spikes, LFP signals and EEG signals generated by neurons and neuronal populations.

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LFPy – multimodal modeling of extracellular neuronal recordings in Python

Cited by 11 publications

References 54 publications

Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex

Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex

A multimodal 3D neuro-microphysiological system with neurite-trapping microelectrodes

Computing extracellular electric potentials from neuronal simulations

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