Power spectra and sensitivity measure in microcircuit model (Bos 2016)¶

These examples reproduce results by Bos et al. [2016].

power_spectra.py: plots the power spectra predicted by mean-field theory and compares it with simulation results (Fig 1E in Bos et al. [2016])

Power Spectra of the Microcircuit Model (Fig. 1E in :cite:t:`bos2016`)

eigenvalue_trajectories.py: calculates the eigenvalues of the effective connectivity matrix across all analysis frequencies for the whole circuit and for the isolated layers (Fig. 4 in Bos et al. [2016])

Eigenvalue Trajectories (Fig. 4 in :cite:t:`bos2016`)

sensitivity_measure_high_gamma.py: computes the sensitivity measure for each eigenmode to reveal the anatomical origin of peaks in the power spectra (Fig. 6 in Bos et al. [2016])

$Sensitivity Measure - High-$\gamma$ frequencies (Fig. 6 in :cite:t:`bos2016`)$

sensitivity_measure_low_gamma.py: computes the sensitivity measure and changes connectivity to reduce peak in power spectra accordingly (parts of Fig. 5 and Fig .8 in Bos et al. [2016])

$Sensitivity Measure - Low-$\gamma$ frequencies (parts of Fig. 5 and Fig.8 in :cite:t:`bos2016`)$

power_spectra_of_subcircuits.py: confirms the results of the sensitivity measure for the low-$gamma$ oscillations by plotting the power spectra of the relevant subcircuits (Fig. 9 in Bos et al. [2016])

Power Spectra of Subcircuits (Fig. 9 in :cite:t:`bos2016`)

All Python scripts use the parameter files Bos2016_network_params.yaml and Bos2016_analysis_params.yaml.