| Name : python3-lfpykit
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| Version : 0.5.1
| Vendor : Fedora Project
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| Release : 3.fc40
| Date : 2024-01-26 07:18:47
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| Group : Unspecified
| Source RPM : python-lfpykit-0.5.1-3.fc40.src.rpm
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| Size : 0.89 MB
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| Packager : Fedora Project
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| Summary : Electrostatic models for multicompartment neuron models
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Description :
This Python module contain freestanding implementations of electrostatic
forward models incorporated in LFPy (https://github.com/LFPy/LFPy,
https://LFPy.readthedocs.io).
The aim of the LFPykit module is to provide electrostatic models in a manner
that facilitates forward-model predictions of extracellular potentials and
related measures from multicompartment neuron models, but without explicit
dependencies on neural simulation software such as NEURON
(https://neuron.yale.edu, https://github.com/neuronsimulator/nrn), Arbor
(https://arbor.readthedocs.io, https://github.com/arbor-sim/arbor), or even
LFPy. The LFPykit module can then be more easily incorporated with these
simulators, or in various projects that utilize them such as LFPy
(https://LFPy.rtfd.io, https://github.com/LFPy/LFPy). BMTK
(https://alleninstitute.github.io/bmtk/,
https://github.com/AllenInstitute/bmtk), etc.
Its main functionality is providing class methods that return two-dimensional
linear transformation matrices M between transmembrane currents I of
multicompartment neuron models and some measurement Y given by Y=MI.
The presently incorporated volume conductor models have been incorporated in
LFPy (https://LFPy.rtfd.io, https://github.com/LFPy/LFPy), as described in
various papers and books:
- Linden H, Hagen E, Leski S, Norheim ES, Pettersen KH, Einevoll GT (2014) LFPy:
a tool for biophysical simulation of extracellular potentials generated by
detailed model neurons. Front. Neuroinform. 7:41. doi: 10.3389/fninf.2013.00041
- Hagen E, Næss S, Ness TV and Einevoll GT (2018) Multimodal Modeling of Neural
Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0.
Front. Neuroinform. 12:92. doi: 10.3389/fninf.2018.00092
- Ness, T. V., Chintaluri, C., Potworowski, J., Leski, S., Glabska, H., Wójcik,
D. K., et al. (2015). Modelling and analysis of electrical potentials recorded
in microelectrode arrays (MEAs). Neuroinformatics 13:403–426. doi:
10.1007/s12021-015-9265-6
- Nunez and Srinivasan, Oxford University Press, 2006
- Næss S, Chintaluri C, Ness TV, Dale AM, Einevoll GT and Wójcik DK (2017).
Corrected Four-sphere Head Model for EEG Signals. Front. Hum. Neurosci. 11:490.
doi: 10.3389/fnhum.2017.00490
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RPM found in directory: /vol/rzm3/linux-fedora-buffet/fedora-secondary/releases/40/Everything/ppc64le/os/Packages/p |
