.. DO NOT EDIT.
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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/run_mean_field_theory.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_run_mean_field_theory.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_run_mean_field_theory.py:

Run mean-field theory
------------------------

This example script shows a mean-field theoretical analysis using for network
parameters using
NNMT - the Neuronal Network Meanfield Toolbox (https://github.com/INM-6/nnmt).

.. GENERATED FROM PYTHON SOURCE LINES 8-16

.. code-block:: Python


    import os
    import numpy as np
    from mesocircuit import mesocircuit_framework as mesoframe
    import parametersets
    import nnmt
    import mesocircuit.plotting.figures as figures


.. GENERATED FROM PYTHON SOURCE LINES 17-19

We instantiate a MesocircuitExperiment with one circuit and use its parameters
for creating an NNMT network object.

.. GENERATED FROM PYTHON SOURCE LINES 19-35

.. code-block:: Python


    name = 'microcircuit_MAMV1'  # reference model
    # name = 'microcircuit_PD'  # Potjans & Diesmann (2014) microcircuit

    # upscaled model does not work well because theory does not account for space
    # name = 'mesocircuit_MAMV1'
    custom_params = parametersets.ps_dicts[name]
    meso_exp = mesoframe.MesocircuitExperiment(name, custom_params)
    circuit = meso_exp.circuits[0]

    # NNMT network object of type Microcircuit
    nw = nnmt.models.Microcircuit(
        network_params=os.path.join(
            circuit.data_dir_circuit, 'parameters', 'nnmt_dict.yaml'),
        analysis_params='nnmt_analysis_params.yaml')


.. GENERATED FROM PYTHON SOURCE LINES 36-38

We calculate the working point, the transfer function, power spectra, and
the sensitivity measure.

.. GENERATED FROM PYTHON SOURCE LINES 38-56

.. code-block:: Python


    # working point for exponentially shaped post synaptic currents
    wp = nnmt.lif.exp.working_point(nw)
    print(wp)

    # transfer function
    nnmt.lif.exp.transfer_function(nw)
    # delay distribution matrix
    nnmt.network_properties.delay_dist_matrix(nw)
    # effective connectivity matrix
    nnmt.lif.exp.effective_connectivity(nw)
    # power spectra
    power = nnmt.lif.exp.power_spectra(nw)
    freqs = nw.analysis_params['omegas'] / (2. * np.pi)

    # sensitivity measure
    sensitivity_dict = nnmt.lif.exp.sensitivity_measure_all_eigenmodes(nw)


.. GENERATED FROM PYTHON SOURCE LINES 57-58

The results are plotted as an overview figure.

.. GENERATED FROM PYTHON SOURCE LINES 58-65

.. code-block:: Python


    figures.theory_overview(
        circuit=circuit,
        working_point=wp,
        frequencies=freqs,
        power=power,
        sensitivity=sensitivity_dict)


.. _sphx_glr_download_auto_examples_run_mean_field_theory.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: run_mean_field_theory.ipynb <run_mean_field_theory.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: run_mean_field_theory.py <run_mean_field_theory.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: run_mean_field_theory.zip <run_mean_field_theory.zip>`


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 .. rst-class:: sphx-glr-signature

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