Metadata-Version: 2.1
Name: neatdend
Version: 0.9.0
Summary: UNKNOWN
Home-page: https://github.com/unibe-cns/NEAT
Author: Willem Wybo, Jakob Jordan, Benjamin Ellenberger
License: GPLv3
Description: NEAT (NEural Analysis Toolkit)
        ==============================
        
        Introduction
        ------------
        
        NEAT is a python library for the study, simulation and simplification of
        morphological neuron models. NEAT accepts morphologies in the *de facto*
        standard .swc format [Cannon1998]_, and implements high-level tools to interact
        with and analyze the morphologies.
        
        NEAT also allows for the convenient definition of morphological neuron models.
        These models can be simulated, through an interface with the NEURON simulator
        [Carnevale2004]_, or can be analyzed with two classical methods: *(i)* the
        separation of variables method [Major1993]_ to obtain impedance kernels as
        a superposition of exponentials and *(ii)* Koch's method to compute impedances
        with linearized ion channels analytically in the frequency domain [Koch1985]_.
        Furthermore, NEAT implements the neural evaluation tree framework [Wybo2019]_
        and an associated C++ simulator, to analyze subunit independence.
        
        Finally, NEAT implements a new and powerful method to simplify morphological
        neuron models into compartmental models with few compartments [Wybo2020]_. For
        these models, NEAT also provides a NEURON interface so that they can be
        simulated directly, and will soon also provide a NEST interface [Gewaltig2007]_.
        
        Documentation
        -------------
        
        Documentation is available `here <https://neatdend.readthedocs.io>`_
        
        Installation
        ------------
        
        **Install**
        
        Note: The following instructions are for Linux and Max OSX systems and only use
        command line tools. Please follow the appropriate manuals for Windows systems or
        tools with graphical interfaces.
        
        You can install the latest release via pip:
        
           .. code-block:: shell
        
              pip install neatdend
        
        The adventurous can install the most recent development version directly from our master branch (don't use this in production unless there are good reasons!):
        
        .. code-block:: shell
        
           git clone git@github.com:unibe-cns/NEAT.git
           cd NEAT
           pip install .
        
        **Post-Install**
        
        To use NEAT with `NEURON <https://neuron.yale.edu/neuron/>`_, make sure NEURON
        is properly installed with its Python interface, and compile and install the
        default NEURON mechanisms by running
        
        .. code-block:: shell
        
            compilechannels default
        
        Test the installation
        
        .. code-block:: shell
        
            pytest
        
        References
        ----------
        
        .. [Cannon1998] Cannon et al. (1998) *An online archive of reconstructed hippocampal neurons*, J. Neurosci. methods.
        .. [Carnevale2004] Carnevale, Nicholas T. and Hines, Michael L. (2004) *The NEURON book*
        .. [Koch1985] Koch, C. and Poggio, T. (1985) *A simple algorithm for solving the cable equation in dendritic trees of arbitrary geometry*, Journal of neuroscience methods, 12(4), pp. 303–315.
        .. [Major1993] Major et al. (1993) *Solutions for transients in arbitrarily branching cables: I. Voltage recording with a somatic shunt*, Biophysical journal, 65(1), pp. 423–49.
        .. [Martelli03] A. Martelli (2003) *Python in a Nutshell*, O’Reilly Media Inc.
        .. [Wybo2019] Wybo, Willem A.M. et al. (2019) *Electrical Compartmentalization in Neurons*, Cell Reports, 26(7), pp. 1759--1773 shunt.*, Biophysical journal, 65(1), pp. 423–49.
        .. [Wybo2020] Wybo, Willem A.M. et al. (2020) TBA.
        .. [Gewaltig2007] Gewaltig, Marc-Oliver and Diesmann, Markus. (2007) *NEST (NEural Simulation Tool)*, Scholarpedia, 2(4), pp. 1430
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Description-Content-Type: text/x-rst
