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Power spectra and sensitivity measure in microcircuit model (Bos 2016)
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These examples reproduce results by :cite:t:`bos2016`.

- :doc:`power_spectra.py <auto_examples/bos2016/power_spectra>`:
  plots the power spectra predicted by mean-field theory and compares it with
  simulation results (Fig 1E in :cite:t:`bos2016`)

.. image:: ../../examples/bos2016/figures/power_spectra_Bos2016.png
  :width: 400
  :alt: Power Spectra of the Microcircuit Model (Fig. 1E in :cite:t:`bos2016`)

- :doc:`eigenvalue_trajectories.py <auto_examples/bos2016/eigenvalue_trajectories>`:
  calculates the eigenvalues of the effective connectivity matrix across all
  analysis frequencies for the whole circuit and for the isolated layers
  (Fig. 4 in :cite:t:`bos2016`)

.. image:: ../../examples/bos2016/figures/eigenvalue_trajectories_Bos2016.png
  :width: 400
  :alt: Eigenvalue Trajectories (Fig. 4 in :cite:t:`bos2016`)

- :doc:`sensitivity_measure.py <auto_examples/bos2016/sensitivity_measure>`:
  computes the sensitivity measure for each eigenmode to reveal the anatomical origin
  of peaks in the power spectra (Fig. 6 in :cite:t:`bos2016`)

.. image:: ../../examples/bos2016/figures/sensitivity_measure_high_gamma_Bos2016.png
  :width: 400
  :alt: Sensitivity Measure - High-$\gamma$ frequencies (Fig. 6 in :cite:t:`bos2016`)

- :doc:`power_spectra_of_subcircuits.py <auto_examples/bos2016/power_spectra_of_subcircuits>`:
  confirms the results of the sensitivity measure for the low-$\gamma$ oscillations by
  plotting the power spectra of the relevant subcircuits
  (Fig. 9 in :cite:t:`bos2016`)

.. image:: ../../examples/bos2016/figures/power_spectra_of_subcircuits_Bos2016.png
  :width: 400
  :alt: Power Spectra of Subcircuits (Fig. 9 in :cite:t:`bos2016`)

All Python scripts use the parameter files
:download:`Bos2016_network_params.yaml <../../tests/fixtures/integration/config/Bos2016_network_params.yaml>`
and
:download:`Bos2016_analysis_params.yaml <../../tests/fixtures/integration/config/Bos2016_network_params.yaml>`.