osc_regime.py: Script for analyzing the oscillatory regime¶

Two figures for identifying the oscillatory regimes¶

The figures are invisibly generated and saved under the current working directory and under the sub-directory ~/dynaregime/

1. Figure 1¶

Figure 1 for each disinhibition experiment

+---------------------+-----------+-----------+
|                     |           |           |
|      subplot 1      | subplot 2 | subplot 3 |
|                     |           |           |
+---------------------+-----------+-----------+
|                     |                       |
|      subplot 4      |       subplot 5       |
|                     |                       |
+---------------------+-----------------------+

Figure 1 contains five subplots such that for each disinhibition experiment it plots:

Subplot	Content	Interpretation
1	raster of all the neurons	synchrony by visual detection `analyseur.cbgtc.visual.markerplot.plot_raster_in_ax()`
2	CV distribution of all the neurons	low means regular firing, high means irregular `analyseur.cbgtc.visual.variation.plotCV_in_ax()`
3	autocorrelation of all the neurons	distinguish SI vs AI `analyseur.cbgtc.stats.compute_shared.autocorr()`
4	power spectrum of the population rate	detect oscillation even with noise `analyseur.cbgtc.stats.psd.PowerSpectrum.compute_for_rate()`
5	time-series of population rate	reveals oscillations and variability `analyseur.cbgtc.stats.rate.Rate.mean_rate()`

2. Figure 2¶

Figure 2 shows plots across all disinhibition experiments

+---------------------+-----------+-----------+
|                     |           |           |
|      subplot 1      | subplot 2 | subplot 3 |
|                     |           |           |
+---------------------+-----------+-----------+
|                     |           |           |
|      subplot 4      | subplot 5 | subplot 6 |
|                     |           |           |
+---------------------+-----------+-----------+

Figure 2 contains six subplots such that across all disinhibition experiments it plots:

Subplot	Content	Interpretation
1	time-series of population rate	rule out averaging washing out of rates
2	pooled CV histogram (CV vs Density)	low means regular firing, high means irregular
3	phase space (CV vs frequency)	compare dynamical states
4	power spectrum of the population rate	rule out phase cancellation issues
5	peak frequency vs disinhibition	rule out averaging washing out of rates
6	autocorrelation of all the neurons	distinguish SI vs AI

cbgtc.osc_regime.main()[source]¶