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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 [Carnevale2004] and NEST [Gewaltig2007] simulators, 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 provides NEURON and NEST interfaces so that they can be simulated directly.

Documentation

Documentation for an older version of NEAT (0.9.2) available here Note that a new documentation website for the current version (1.0-rc1) is currently under construction. Please see the changelog (changelog/v.1.0.md) for an overview of the changes.

Installation

Install

NEAT can be installed with pip.

pip install nest-neat

NEAT can also be installed from the source repository. 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.

git clone git@github.com:nest/NEAT.git
cd NEAT
pip install .

Post-Install

Note that if you install NEAT with pip, as above, NEURON will automatically be installed as well. To use NEAT with NEST, you need to manually install NEST and nestml on your system, by following the detailed installation instructions.

Testing the installation

NEAT makes the shell command neatmodels available to compile NEAT-defined ion channels into for NEURON or NEST, so that they can be simulated. You can test whether this command is available by installing the default ion channels of NEAT:

neatmodels install default

This installs the default channels for NEURON. To install them for NEST, use:

neatmodels install default -s nest

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]
  1. 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

[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

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The following attestation bundles were made for nest_neat-1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: publish.yml on nest/NEAT

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The following attestation bundles were made for nest_neat-1.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl:

Publisher: publish.yml on nest/NEAT

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Details for the file nest_neat-1.0-cp39-cp39-macosx_11_0_arm64.whl.

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The following attestation bundles were made for nest_neat-1.0-cp39-cp39-macosx_11_0_arm64.whl:

Publisher: publish.yml on nest/NEAT

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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