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 [Wybo2021]. 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 is available here
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:
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!):
git clone git@github.com:unibe-cns/NEAT.git
cd NEAT
pip install .Post-Install
To use NEAT with NEURON, make sure NEURON is properly installed with its Python interface. The easiest way to install NEURON on Linux and macOS platform is via pip:
pip install neuronSee detailed install instructions here.
You can test it by compiling and installing the default NEURON mechanisms by running
compilechannels defaultTest the installation
pytest| [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] |
|
| [Wybo2019] | Wybo, Willem A.M. et al. (2019) Electrical Compartmentalization in Neurons, Cell Reports, 26(7), pp. 1759--1773 |
| [Wybo2021] | Wybo, Willem A.M. et al. (2021) Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses, eLife, 10:e60936, pp. 1--26 |
| [Gewaltig2007] | Gewaltig, Marc-Oliver and Diesmann, Markus. (2007) NEST (NEural Simulation Tool), Scholarpedia, 2(4), pp. 1430 |