| Index | index by Group | index by Distribution | index by Vendor | index by creation date | index by Name | Mirrors | Help |
The search service can find package by either name (apache), provides(webserver), absolute file names (/usr/bin/apache), binaries (gprof) or shared libraries (libXm.so.2) in standard path. It does not support multiple arguments yet...
The System and Arch are optional added filters, for example System could be "redhat", "redhat-7.2", "mandrake" or "gnome", Arch could be "i386" or "src", etc. depending on your system.
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
Generated by rpm2html 1.6