| Name | NMODL-nightly JSON |
| Version |
0.6.387
JSON |
| download |
| home_page | None |
| Summary | NEURON Modeling Language Source-to-Source Compiler Framework |
| upload_time | 2024-12-18 00:18:37 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
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The NMODL Framework
===================
WARNING
_______
**NMODL has been fully integrated into the NEURON repository.**
There will be no further development efforts on NMODL as an independent project.
All future development will happen at:
`https://github.com/neuronsimulator/nrn <https://github.com/neuronsimulator/nrn>`_.
---------------------
The NMODL Framework is a code generation engine for **N**\ EURON
**MOD**\ eling **L**\ anguage
(`NMODL <https://www.neuron.yale.edu/neuron/static/py_doc/modelspec/programmatic/mechanisms/nmodl.html>`__).
It is designed with modern compiler and code generation techniques to:
- Provide **modular tools** for parsing, analysing and transforming
NMODL
- Provide **easy to use**, high level Python API
- Generate **optimised code** for modern compute architectures
including CPUs, GPUs
- **Flexibility** to implement new simulator backends
- Support for **full** NMODL specification
About NMODL
-----------
Simulators like `NEURON <https://www.neuron.yale.edu/neuron/>`__ use
NMODL as a domain specific language (DSL) to describe a wide range of
membrane and intracellular submodels. Here is an example of exponential
synapse specified in NMODL:
.. code::
NEURON {
POINT_PROCESS ExpSyn
RANGE tau, e, i
NONSPECIFIC_CURRENT i
}
UNITS {
(nA) = (nanoamp)
(mV) = (millivolt)
(uS) = (microsiemens)
}
PARAMETER {
tau = 0.1 (ms) <1e-9,1e9>
e = 0 (mV)
}
ASSIGNED {
v (mV)
i (nA)
}
STATE {
g (uS)
}
INITIAL {
g = 0
}
BREAKPOINT {
SOLVE state METHOD cnexp
i = g*(v - e)
}
DERIVATIVE state {
g' = -g/tau
}
NET_RECEIVE(weight (uS)) {
g = g + weight
}
Installation
------------
See
`INSTALL.rst <https://github.com/BlueBrain/nmodl/blob/master/INSTALL.rst>`__
for detailed instructions to build the NMODL from source.
Try NMODL with Docker
---------------------
To quickly test the NMODL Framework’s analysis capabilities we provide a
`docker <https://www.docker.com>`__ image, which includes the NMODL
Framework python library and a fully functional Jupyter notebook
environment. After installing
`docker <https://docs.docker.com/compose/install/>`__ and
`docker-compose <https://docs.docker.com/compose/install/>`__ you can
pull and run the NMODL image from your terminal.
To try Python interface directly from CLI, you can run docker image as:
::
docker run -it --entrypoint=/bin/sh bluebrain/nmodl
And try NMODL Python API discussed later in this README as:
::
$ python3
Python 3.6.8 (default, Apr 8 2019, 18:17:52)
>>> from nmodl import dsl
>>> import os
>>> examples = dsl.list_examples()
>>> nmodl_string = dsl.load_example(examples[-1])
...
To try Jupyter notebooks you can download docker compose file and run it
as:
.. code:: sh
wget "https://raw.githubusercontent.com/BlueBrain/nmodl/master/docker/docker-compose.yml"
DUID=$(id -u) DGID=$(id -g) HOSTNAME=$(hostname) docker-compose up
If all goes well you should see at the end status messages similar to
these:
::
[I 09:49:53.923 NotebookApp] The Jupyter Notebook is running at:
[I 09:49:53.923 NotebookApp] http://(4c8edabe52e1 or 127.0.0.1):8888/?token=a7902983bad430a11935
[I 09:49:53.923 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
To access the notebook, open this file in a browser:
file:///root/.local/share/jupyter/runtime/nbserver-1-open.html
Or copy and paste one of these URLs:
http://(4c8edabe52e1 or 127.0.0.1):8888/?token=a7902983bad430a11935
Based on the example above you should then open your browser and
navigate to the URL
``http://127.0.0.1:8888/?token=a7902983bad430a11935``.
You can open and run all example notebooks provided in the ``examples``
folder. You can also create new notebooks in ``my_notebooks``, which
will be stored in a subfolder ``notebooks`` at your current working
directory.
Using the Python API
--------------------
Once the NMODL Framework is installed, you can use the Python parsing
API to load NMOD file as:
.. code:: python
from nmodl import dsl
examples = dsl.list_examples()
nmodl_string = dsl.load_example(examples[-1])
driver = dsl.NmodlDriver()
modast = driver.parse_string(nmodl_string)
The ``parse_file`` API returns Abstract Syntax Tree
(`AST <https://en.wikipedia.org/wiki/Abstract_syntax_tree>`__)
representation of input NMODL file. One can look at the AST by
converting to JSON form as:
.. code:: python
>>> print (dsl.to_json(modast))
{
"Program": [
{
"NeuronBlock": [
{
"StatementBlock": [
{
"Suffix": [
{
"Name": [
{
"String": [
{
"name": "POINT_PROCESS"
}
...
Every key in the JSON form represent a node in the AST. You can also use
visualization API to look at the details of AST as:
::
from nmodl import ast
ast.view(modast)
which will open AST view in web browser:
.. figure::
https://user-images.githubusercontent.com/666852/57329449-12c9a400-7114-11e9-8da5-0042590044ec.gif
:alt: ast_viz
Vizualisation of the AST in the NMODL Framework
The central *Program* node represents the whole MOD file and each of
it’s children represent the block in the input NMODL file. Note that
this requires X-forwarding if you are using the Docker image.
Once the AST is created, one can use exisiting visitors to perform
various analysis/optimisations. One can also easily write his own custom
visitor using Python Visitor API. See `Python API
tutorial <docs/notebooks/nmodl-python-tutorial.ipynb>`__ for details.
The NMODL Framework also allows us to transform the AST representation back to
NMODL form as:
.. code:: python
>>> print (dsl.to_nmodl(modast))
NEURON {
POINT_PROCESS ExpSyn
RANGE tau, e, i
NONSPECIFIC_CURRENT i
}
UNITS {
(nA) = (nanoamp)
(mV) = (millivolt)
(uS) = (microsiemens)
}
PARAMETER {
tau = 0.1 (ms) <1e-09,1000000000>
e = 0 (mV)
}
...
High Level Analysis and Code Generation
---------------------------------------
The NMODL Framework provides rich model introspection and analysis
capabilities using `various
visitors <https://bluebrain.github.io/nmodl/html/doxygen/group__visitor__classes.html>`__.
Here is an example of theoretical performance characterisation of
channels and synapses from rat neocortical column microcircuit
`published in
2015 <https://www.cell.com/cell/fulltext/S0092-8674%2815%2901191-5>`__:
.. figure::
https://user-images.githubusercontent.com/666852/57336711-2cc0b200-7127-11e9-8053-8f662e2ec191.png
:alt: nmodl-perf-stats
Performance results of the NMODL Framework
To understand how you can write your own introspection and analysis
tool, see `this
tutorial <docs/notebooks/nmodl-python-tutorial.ipynb>`__.
Once analysis and optimization passes are performed, the NMODL Framework
can generate optimised code for modern compute architectures including
CPUs (Intel, AMD, ARM) and GPUs (NVIDIA, AMD) platforms. For example,
C++, OpenACC and OpenMP backends are implemented and one can choose
these backends on command line as:
::
$ nmodl expsyn.mod sympy --analytic
To know more about code generation backends, `see
here <https://bluebrain.github.io/nmodl/html/doxygen/group__codegen__backends.html>`__.
NMODL Framework provides number of options (for code generation,
optimization passes and ODE solver) which can be listed as:
::
$ nmodl -H
NMODL : Source-to-Source Code Generation Framework [version]
Usage: /path/<>/nmodl [OPTIONS] file... [SUBCOMMAND]
Positionals:
file TEXT:FILE ... REQUIRED One or more MOD files to process
Options:
-h,--help Print this help message and exit
-H,--help-all Print this help message including all sub-commands
--verbose=info Verbose logger output (trace, debug, info, warning, error, critical, off)
-o,--output TEXT=. Directory for backend code output
--scratch TEXT=tmp Directory for intermediate code output
--units TEXT=/path/<>/nrnunits.lib
Directory of units lib file
Subcommands:
host
HOST/CPU code backends
Options:
--c C/C++ backend (true)
acc
Accelerator code backends
Options:
--oacc C/C++ backend with OpenACC (false)
sympy
SymPy based analysis and optimizations
Options:
--analytic Solve ODEs using SymPy analytic integration (false)
--pade Pade approximation in SymPy analytic integration (false)
--cse CSE (Common Subexpression Elimination) in SymPy analytic integration (false)
--conductance Add CONDUCTANCE keyword in BREAKPOINT (false)
passes
Analyse/Optimization passes
Options:
--inline Perform inlining at NMODL level (false)
--unroll Perform loop unroll at NMODL level (false)
--const-folding Perform constant folding at NMODL level (false)
--localize Convert RANGE variables to LOCAL (false)
--global-to-range Convert GLOBAL variables to RANGE (false)
--localize-verbatim Convert RANGE variables to LOCAL even if verbatim block exist (false)
--local-rename Rename LOCAL variable if variable of same name exist in global scope (false)
--verbatim-inline Inline even if verbatim block exist (false)
--verbatim-rename Rename variables in verbatim block (true)
--json-ast Write AST to JSON file (false)
--nmodl-ast Write AST to NMODL file (false)
--json-perf Write performance statistics to JSON file (false)
--show-symtab Write symbol table to stdout (false)
codegen
Code generation options
Options:
--layout TEXT:{aos,soa}=soa Memory layout for code generation
--datatype TEXT:{float,double}=soa Data type for floating point variables
--force Force code generation even if there is any incompatibility
--only-check-compatibility Check compatibility and return without generating code
--opt-ionvar-copy Optimize copies of ion variables (false)
Documentation
-------------
We are working on user documentation, you can find current drafts of :
- `User Documentation <https://bluebrain.github.io/nmodl/>`__
- `Developer / API
Documentation <https://bluebrain.github.io/nmodl/html/doxygen/index.html>`__
Citation
--------
If you would like to know more about the the NMODL Framework, see
the following paper:
- Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Blanco Alonso, James King,
Michael Hines, and Felix Schürmann. 2020. An optimizing multi-platform
source-to-source compiler framework for the NEURON MODeling Language.
In *Computational Science – ICCS 2020*, Springer, Cham, 45–58.
DOI: `10.1007/978-3-030-50371-0_4 <https://doi.org/10.1007/978-3-030-50371-0_4>`__
Some additional details are covered in the pre-print:
- Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Alonso, James King,
Michael Hines and Felix Schürmann. 2019. An optimizing multi-platform
source-to-source compiler framework for the NEURON MODeling Language.
In Eprint :
`arXiv:1905.02241 <https://arxiv.org/pdf/1905.02241.pdf>`__
Support / Contribuition
-----------------------
If you see any issue, feel free to `raise a
ticket <https://github.com/BlueBrain/nmodl/issues/new>`__. If you would
like to improve this framework, see `open
issues <https://github.com/BlueBrain/nmodl/issues>`__ and `contribution
guidelines <CONTRIBUTING.rst>`__.
Examples / Benchmarks
---------------------
The benchmarks used to test the performance and parsing capabilities of
NMODL Framework are currently being migrated to GitHub. These benchmarks
will be published soon in following repositories:
- `NMODL Benchmark <https://github.com/BlueBrain/nmodlbench>`__
- `NMODL Database <https://github.com/BlueBrain/nmodldb>`__
Funding & Acknowledgment
------------------------
The development of this software was supported by funding to the Blue
Brain Project, a research center of the École polytechnique fédérale de
Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss
Federal Institutes of Technology. In addition, the development was
supported by funding from the National Institutes of Health (NIH) under
the Grant Number R01NS11613 (Yale University) and the European Union’s
Horizon 2020 Framework Programme for Research and Innovation under the
Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
Copyright © 2017-2024 Blue Brain Project, EPFL
Raw data
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"description": "The NMODL Framework\n===================\n\nWARNING\n_______\n\n**NMODL has been fully integrated into the NEURON repository.**\nThere will be no further development efforts on NMODL as an independent project.\n\nAll future development will happen at:\n`https://github.com/neuronsimulator/nrn <https://github.com/neuronsimulator/nrn>`_.\n\n---------------------\n\nThe NMODL Framework is a code generation engine for **N**\\ EURON\n**MOD**\\ eling **L**\\ anguage\n(`NMODL <https://www.neuron.yale.edu/neuron/static/py_doc/modelspec/programmatic/mechanisms/nmodl.html>`__).\nIt is designed with modern compiler and code generation techniques to:\n\n- Provide **modular tools** for parsing, analysing and transforming\n NMODL\n- Provide **easy to use**, high level Python API\n- Generate **optimised code** for modern compute architectures\n including CPUs, GPUs\n- **Flexibility** to implement new simulator backends\n- Support for **full** NMODL specification\n\nAbout NMODL\n-----------\n\nSimulators like `NEURON <https://www.neuron.yale.edu/neuron/>`__ use\nNMODL as a domain specific language (DSL) to describe a wide range of\nmembrane and intracellular submodels. Here is an example of exponential\nsynapse specified in NMODL:\n\n.. code::\n\n NEURON {\n POINT_PROCESS ExpSyn\n RANGE tau, e, i\n NONSPECIFIC_CURRENT i\n }\n UNITS {\n (nA) = (nanoamp)\n (mV) = (millivolt)\n (uS) = (microsiemens)\n }\n PARAMETER {\n tau = 0.1 (ms) <1e-9,1e9>\n e = 0 (mV)\n }\n ASSIGNED {\n v (mV)\n i (nA)\n }\n STATE {\n g (uS)\n }\n INITIAL {\n g = 0\n }\n BREAKPOINT {\n SOLVE state METHOD cnexp\n i = g*(v - e)\n }\n DERIVATIVE state {\n g' = -g/tau\n }\n NET_RECEIVE(weight (uS)) {\n g = g + weight\n }\n\nInstallation\n------------\n\nSee\n`INSTALL.rst <https://github.com/BlueBrain/nmodl/blob/master/INSTALL.rst>`__\nfor detailed instructions to build the NMODL from source.\n\nTry NMODL with Docker\n---------------------\n\nTo quickly test the NMODL Framework\u2019s analysis capabilities we provide a\n`docker <https://www.docker.com>`__ image, which includes the NMODL\nFramework python library and a fully functional Jupyter notebook\nenvironment. After installing\n`docker <https://docs.docker.com/compose/install/>`__ and\n`docker-compose <https://docs.docker.com/compose/install/>`__ you can\npull and run the NMODL image from your terminal.\n\nTo try Python interface directly from CLI, you can run docker image as:\n\n::\n\n docker run -it --entrypoint=/bin/sh bluebrain/nmodl\n\nAnd try NMODL Python API discussed later in this README as:\n\n::\n\n $ python3\n Python 3.6.8 (default, Apr 8 2019, 18:17:52)\n >>> from nmodl import dsl\n >>> import os\n >>> examples = dsl.list_examples()\n >>> nmodl_string = dsl.load_example(examples[-1])\n ...\n\nTo try Jupyter notebooks you can download docker compose file and run it\nas:\n\n.. code:: sh\n\n wget \"https://raw.githubusercontent.com/BlueBrain/nmodl/master/docker/docker-compose.yml\"\n DUID=$(id -u) DGID=$(id -g) HOSTNAME=$(hostname) docker-compose up\n\nIf all goes well you should see at the end status messages similar to\nthese:\n\n::\n\n [I 09:49:53.923 NotebookApp] The Jupyter Notebook is running at:\n [I 09:49:53.923 NotebookApp] http://(4c8edabe52e1 or 127.0.0.1):8888/?token=a7902983bad430a11935\n [I 09:49:53.923 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n To access the notebook, open this file in a browser:\n file:///root/.local/share/jupyter/runtime/nbserver-1-open.html\n Or copy and paste one of these URLs:\n http://(4c8edabe52e1 or 127.0.0.1):8888/?token=a7902983bad430a11935\n\nBased on the example above you should then open your browser and\nnavigate to the URL\n``http://127.0.0.1:8888/?token=a7902983bad430a11935``.\n\nYou can open and run all example notebooks provided in the ``examples``\nfolder. You can also create new notebooks in ``my_notebooks``, which\nwill be stored in a subfolder ``notebooks`` at your current working\ndirectory.\n\nUsing the Python API\n--------------------\n\nOnce the NMODL Framework is installed, you can use the Python parsing\nAPI to load NMOD file as:\n\n.. code:: python\n\n from nmodl import dsl\n\n examples = dsl.list_examples()\n nmodl_string = dsl.load_example(examples[-1])\n driver = dsl.NmodlDriver()\n modast = driver.parse_string(nmodl_string)\n\nThe ``parse_file`` API returns Abstract Syntax Tree\n(`AST <https://en.wikipedia.org/wiki/Abstract_syntax_tree>`__)\nrepresentation of input NMODL file. One can look at the AST by\nconverting to JSON form as:\n\n.. code:: python\n\n >>> print (dsl.to_json(modast))\n {\n \"Program\": [\n {\n \"NeuronBlock\": [\n {\n \"StatementBlock\": [\n {\n \"Suffix\": [\n {\n \"Name\": [\n {\n \"String\": [\n {\n \"name\": \"POINT_PROCESS\"\n }\n ...\n\nEvery key in the JSON form represent a node in the AST. You can also use\nvisualization API to look at the details of AST as:\n\n::\n\n from nmodl import ast\n ast.view(modast)\n\nwhich will open AST view in web browser:\n\n.. figure::\n https://user-images.githubusercontent.com/666852/57329449-12c9a400-7114-11e9-8da5-0042590044ec.gif\n :alt: ast_viz\n\n Vizualisation of the AST in the NMODL Framework\n\nThe central *Program* node represents the whole MOD file and each of\nit\u2019s children represent the block in the input NMODL file. Note that\nthis requires X-forwarding if you are using the Docker image.\n\nOnce the AST is created, one can use exisiting visitors to perform\nvarious analysis/optimisations. One can also easily write his own custom\nvisitor using Python Visitor API. See `Python API\ntutorial <docs/notebooks/nmodl-python-tutorial.ipynb>`__ for details.\n\nThe NMODL Framework also allows us to transform the AST representation back to\nNMODL form as:\n\n.. code:: python\n\n >>> print (dsl.to_nmodl(modast))\n NEURON {\n POINT_PROCESS ExpSyn\n RANGE tau, e, i\n NONSPECIFIC_CURRENT i\n }\n\n UNITS {\n (nA) = (nanoamp)\n (mV) = (millivolt)\n (uS) = (microsiemens)\n }\n\n PARAMETER {\n tau = 0.1 (ms) <1e-09,1000000000>\n e = 0 (mV)\n }\n ...\n\nHigh Level Analysis and Code Generation\n---------------------------------------\n\nThe NMODL Framework provides rich model introspection and analysis\ncapabilities using `various\nvisitors <https://bluebrain.github.io/nmodl/html/doxygen/group__visitor__classes.html>`__.\nHere is an example of theoretical performance characterisation of\nchannels and synapses from rat neocortical column microcircuit\n`published in\n2015 <https://www.cell.com/cell/fulltext/S0092-8674%2815%2901191-5>`__:\n\n.. figure::\n https://user-images.githubusercontent.com/666852/57336711-2cc0b200-7127-11e9-8053-8f662e2ec191.png\n :alt: nmodl-perf-stats\n\n Performance results of the NMODL Framework\n\nTo understand how you can write your own introspection and analysis\ntool, see `this\ntutorial <docs/notebooks/nmodl-python-tutorial.ipynb>`__.\n\nOnce analysis and optimization passes are performed, the NMODL Framework\ncan generate optimised code for modern compute architectures including\nCPUs (Intel, AMD, ARM) and GPUs (NVIDIA, AMD) platforms. For example,\nC++, OpenACC and OpenMP backends are implemented and one can choose\nthese backends on command line as:\n\n::\n\n $ nmodl expsyn.mod sympy --analytic\n\nTo know more about code generation backends, `see\nhere <https://bluebrain.github.io/nmodl/html/doxygen/group__codegen__backends.html>`__.\nNMODL Framework provides number of options (for code generation,\noptimization passes and ODE solver) which can be listed as:\n\n::\n\n $ nmodl -H\n NMODL : Source-to-Source Code Generation Framework [version]\n Usage: /path/<>/nmodl [OPTIONS] file... [SUBCOMMAND]\n\n Positionals:\n file TEXT:FILE ... REQUIRED One or more MOD files to process\n\n Options:\n -h,--help Print this help message and exit\n -H,--help-all Print this help message including all sub-commands\n --verbose=info Verbose logger output (trace, debug, info, warning, error, critical, off)\n -o,--output TEXT=. Directory for backend code output\n --scratch TEXT=tmp Directory for intermediate code output\n --units TEXT=/path/<>/nrnunits.lib\n Directory of units lib file\n\n Subcommands:\n host\n HOST/CPU code backends\n Options:\n --c C/C++ backend (true)\n\n acc\n Accelerator code backends\n Options:\n --oacc C/C++ backend with OpenACC (false)\n\n sympy\n SymPy based analysis and optimizations\n Options:\n --analytic Solve ODEs using SymPy analytic integration (false)\n --pade Pade approximation in SymPy analytic integration (false)\n --cse CSE (Common Subexpression Elimination) in SymPy analytic integration (false)\n --conductance Add CONDUCTANCE keyword in BREAKPOINT (false)\n\n passes\n Analyse/Optimization passes\n Options:\n --inline Perform inlining at NMODL level (false)\n --unroll Perform loop unroll at NMODL level (false)\n --const-folding Perform constant folding at NMODL level (false)\n --localize Convert RANGE variables to LOCAL (false)\n --global-to-range Convert GLOBAL variables to RANGE (false)\n --localize-verbatim Convert RANGE variables to LOCAL even if verbatim block exist (false)\n --local-rename Rename LOCAL variable if variable of same name exist in global scope (false)\n --verbatim-inline Inline even if verbatim block exist (false)\n --verbatim-rename Rename variables in verbatim block (true)\n --json-ast Write AST to JSON file (false)\n --nmodl-ast Write AST to NMODL file (false)\n --json-perf Write performance statistics to JSON file (false)\n --show-symtab Write symbol table to stdout (false)\n\n codegen\n Code generation options\n Options:\n --layout TEXT:{aos,soa}=soa Memory layout for code generation\n --datatype TEXT:{float,double}=soa Data type for floating point variables\n --force Force code generation even if there is any incompatibility\n --only-check-compatibility Check compatibility and return without generating code\n --opt-ionvar-copy Optimize copies of ion variables (false)\n\nDocumentation\n-------------\n\nWe are working on user documentation, you can find current drafts of :\n\n- `User Documentation <https://bluebrain.github.io/nmodl/>`__\n- `Developer / API\n Documentation <https://bluebrain.github.io/nmodl/html/doxygen/index.html>`__\n\nCitation\n--------\n\nIf you would like to know more about the the NMODL Framework, see\nthe following paper:\n\n- Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Blanco Alonso, James King,\n Michael Hines, and Felix Sch\u00fcrmann. 2020. An optimizing multi-platform\n source-to-source compiler framework for the NEURON MODeling Language.\n In *Computational Science \u2013 ICCS 2020*, Springer, Cham, 45\u201358.\n DOI: `10.1007/978-3-030-50371-0_4 <https://doi.org/10.1007/978-3-030-50371-0_4>`__\n\nSome additional details are covered in the pre-print:\n\n- Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Alonso, James King,\n Michael Hines and Felix Sch\u00fcrmann. 2019. An optimizing multi-platform\n source-to-source compiler framework for the NEURON MODeling Language.\n In Eprint :\n `arXiv:1905.02241 <https://arxiv.org/pdf/1905.02241.pdf>`__\n\nSupport / Contribuition\n-----------------------\n\nIf you see any issue, feel free to `raise a\nticket <https://github.com/BlueBrain/nmodl/issues/new>`__. If you would\nlike to improve this framework, see `open\nissues <https://github.com/BlueBrain/nmodl/issues>`__ and `contribution\nguidelines <CONTRIBUTING.rst>`__.\n\nExamples / Benchmarks\n---------------------\n\nThe benchmarks used to test the performance and parsing capabilities of\nNMODL Framework are currently being migrated to GitHub. These benchmarks\nwill be published soon in following repositories:\n\n- `NMODL Benchmark <https://github.com/BlueBrain/nmodlbench>`__\n- `NMODL Database <https://github.com/BlueBrain/nmodldb>`__\n\nFunding & Acknowledgment\n------------------------\n\nThe development of this software was supported by funding to the Blue\nBrain Project, a research center of the \u00c9cole polytechnique f\u00e9d\u00e9rale de\nLausanne (EPFL), from the Swiss government\u2019s ETH Board of the Swiss\nFederal Institutes of Technology. In addition, the development was\nsupported by funding from the National Institutes of Health (NIH) under\nthe Grant Number R01NS11613 (Yale University) and the European Union\u2019s\nHorizon 2020 Framework Programme for Research and Innovation under the\nSpecific Grant Agreement No.\u00a0785907 (Human Brain Project SGA2).\n\nCopyright \u00a9 2017-2024 Blue Brain Project, EPFL\n",
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