GitHub - ModelDBRepository/267598: Adaptive Generalized Leaky Integrate-and-Fire Model (AGLIF) (Marasco et al., 2023)

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Adaptive Generalized Leaky Integrate-and-Fire Model (AGLIF) (Marasco et al., 2023)

modeldb.science/267598

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
optimization		optimization
AGLIF_model_nest.patch		AGLIF_model_nest.patch
Fig7_ModelDB.py		Fig7_ModelDB.py
Fig7_ModelDB_nest.py		Fig7_ModelDB_nest.py
Fig8_ModelDB.py		Fig8_ModelDB.py
Fig8_ModelDB_nest.py		Fig8_ModelDB_nest.py
Model_traces_for_constant_current_injections.png		Model_traces_for_constant_current_injections.png
Model_traces_for_constant_current_injections_NEST.png		Model_traces_for_constant_current_injections_NEST.png
Model_traces_for_piecewise_currents.png		Model_traces_for_piecewise_currents.png
Model_traces_for_piecewise_currents_nest.png		Model_traces_for_piecewise_currents_nest.png
README.html		README.html
mds_python_model.py		mds_python_model.py
run_mds_nest.py		run_mds_nest.py

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<html>
<p>NEST patch and python files for the model:
<a href="">
Addolorata Marasco, Emiliano Spera, Vittorio De
Falco, Annalisa Iuorio, Carmen Alina Lupascu, Sergio
Solinas and Michele Migliore. An Adaptive Generalized Leaky
Integrate-and-Fire model for hippocampal
CA1 pyramidal neurons and interneurons.</a>
</p>
<p>We propose an adaptive generalized leaky integrate-and-fire model,
for hippocampal CA1 neurons and interneurons, in which the nonlinear
nature of the firing dynamics was successfully reproduced by linear
ordinary equations equipped with nonlinear and more realistic initial
and update conditions after each spike event, which strictly depends
on the external stimulation current.
</p>
<p>Dependencies and installation instructions: follow instructions below.<br/>
</p>
<p>In order to reproduce Fig. 7 and 8 of the paper run:<br/>
python -i Fig7_ModelDB.py<br/>
or<br/>
python -i Fig8_ModelDB.py
</p>
<p>To reproduce the same figures with the NEST model run:<br/>
  python -i Fig7_ModelDB_nest.py<br/>
  or<br/>
  python -i Fig8_ModelDB_nest.py<br/>
</p>
<p>
  The scripts will produce the following images:<br/>
  Fig7 from python code<br/>
  <img src="Model_traces_for_constant_current_injections.png" alt="Figure 7">
</p>
<p>
  Fig8 from python code<br/>
  <img src="Model_traces_for_piecewise_currents.png" alt="Figure 8">
</p>
<p>
  Fig7  from python-NEST code<br/>
  <img src="Model_traces_for_constant_current_injections_NEST.png" alt="Figure 7 NEST">
</p>
<p>
  Fig8  from python-nest code<br/>
  <img src="Model_traces_for_piecewise_currents_nest.png" alt="Figure 8">
</p>
<p>Under Ubuntu unix systems:<br/>
  install cmake and build-essential:<br/>
  sudo apt install cmake<br/>
  sudo apt-get install build-essential<br/>

  install anaconda3 follwoing instructions from:<br/>
  ...<br/>
<p>

</p>Create a Conda Enviroment following instructions at https://github.com/nest/nest-simulator/blob/master/environment.yml
  OR<br/>
  create a conda environment:<br/>
  conda create -n nest python=3.9<br/>
  conda activate nest<br/>
<br/>
  install the following packages:<br/>
  conda install -c anaconda numpy<br/>
  conda install -c forge matplotlib<br/>
  conda install -c conda-forge openmpi<br/>
  conda install -c anaconda gsl<br/>
  conda install -c anaconda cython<br/>
<br/>
  to compile the NEST simulator with our AGLIF model:<br/>
  download the correct nest version using:<br/>
  git clone https://github.com/nest/nest-simulator.git<br/>
  git fetch<br/>
  git checkout 01f6e5a763906a3b7cf6713b887366af7bc20a44<br/>
<br/>
  Apply the patch:<br/>
  git apply AGLIF_model_nest.patch<br/>
<br/>
  mkdir ../nest-build<br/>
  cd ../nest-build<br/>
  cmake ../nest-simulator<br/>
  make<br/>
  make install<br/>
</p>

<p>Questions on how to use this model<br/>
should be directed to michele.migliore at pa.ibf.cnr.it<br/>
Questions on how to modifiy or run this<br/>
model should be addressed to smgsolinas at uniss.it<br/>
</p>

</body>
</html>