python3-lfpykit Subpackage of python-lfpykit

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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