diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 96a61e24311037d2c22488385fcb878f1bfe6c4b..7ad71e44b893ea4ca56e31bd87c3cd1ce3490e28 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -172,7 +172,7 @@
"3. Replace non-simulated areas (`replace_non_simulated_areas`) <br>\n",
"Replace non-simulated areas (replace_non_simulated_areas) defines how non-simulated areas will be replaced. <br>\n",
"4. Simulation areas (`simulation_areas`) <br>\n",
- "Simulation areas (simulation_areas) specifies the cortical areas included in the simulation process."
+ "Simulation areas (simulation_areas) specify the cortical areas included in the simulation process."
]
},
{
@@ -554,14 +554,6 @@
"rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)"
]
},
- {
- "cell_type": "markdown",
- "id": "ca603daf",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
{
"cell_type": "markdown",
"id": "b1320ab1",
@@ -612,6 +604,167 @@
"ax.legend()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "ae19bcc3",
+ "metadata": {},
+ "source": [
+ "### 4.2 Resting state for single area\n",
+ "Raster plot of spiking activity of 3% of the neurons in area V1 (A), V2 (B), and FEF (C). Blue: excitatory neurons, red: inhibitory neurons. (D-F) Spiking statistics across all 32 areas for the respective populations shown as area-averaged box plots. Crosses: medians, boxes: interquartile range (IQR), whiskers extend to the most extremeobservat ions within 1.5×IQR beyond the IQR."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1da18fee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "Create raster display of a single area with populations stacked\n",
+ "onto each other. Excitatory neurons in blue, inhibitory\n",
+ "neurons in red.\n",
+ "\n",
+ "Parameters\n",
+ "----------\n",
+ "area : string {area}\n",
+ " Area to be plotted.\n",
+ "frac_neurons : float, [0,1]\n",
+ " Fraction of cells to be considered.\n",
+ "t_min : float, optional\n",
+ " Minimal time in ms of spikes to be shown. Defaults to 0 ms.\n",
+ "t_max : float, optional\n",
+ " Minimal time in ms of spikes to be shown. Defaults to simulation time.\n",
+ "output : {'pdf', 'png', 'eps'}, optional\n",
+ " If given, the function stores the plot to a file of the given format.\n",
+ "\n",
+ "\"\"\"\n",
+ "area = 'V1'\n",
+ "frac_neurons = 0.03\n",
+ "M.analysis.single_dot_display(area, frac_neurons, t_min=500., t_max='T')\n",
+ "\n",
+ "area = 'V2'\n",
+ "frac_neurons = 0.03\n",
+ "M.analysis.single_dot_display(area, frac_neurons, t_min=500., t_max='T')\n",
+ "\n",
+ "area = 'FEF'\n",
+ "frac_neurons = 0.03\n",
+ "M.analysis.single_dot_display(area, frac_neurons, t_min=500., t_max='T')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "019d805e",
+ "metadata": {},
+ "source": [
+ "### 4.3 Firing rates for the whole population\n",
+ "Population-averaged firing rates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "95c57114",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "Calculate time-averaged population rates and store them in member pop_rates.\n",
+ "If the rates had previously been stored with the same\n",
+ "parameters, they are loaded from file.\n",
+ "\n",
+ "Parameters\n",
+ "----------\n",
+ "t_min : float, optional\n",
+ " Minimal time in ms of the simulation to take into account\n",
+ " for the calculation. Defaults to 500 ms.\n",
+ "t_max : float, optional\n",
+ " Maximal time in ms of the simulation to take into account\n",
+ " for the calculation. Defaults to the simulation time.\n",
+ "compute_stat : bool, optional\n",
+ " If set to true, the mean and variance of the population rate\n",
+ " is calculated. Defaults to False.\n",
+ " Caution: Setting to True slows down the computation.\n",
+ "areas : list, optional\n",
+ " Which areas to include in the calculcation.\n",
+ " Defaults to all loaded areas.\n",
+ "pops : list or {'complete'}, optional\n",
+ " Which populations to include in the calculation.\n",
+ " If set to 'complete', all populations the respective areas\n",
+ " are included. Defaults to 'complete'.\n",
+ "\"\"\"\n",
+ "M.analysis.create_pop_rates(t_min=1000.)\n",
+ "# M.analysis.save()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "06a595de",
+ "metadata": {},
+ "source": [
+ "### 4.4 Average pairwise correlation coefficients of spiking activity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a3847e67",
+ "metadata": {},
+ "source": [
+ "### 4.5 Irregularity measured by revised local variation LvR averaged across neurons"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "90ae8f4c",
+ "metadata": {},
+ "source": [
+ "### 4.6 Time series of population- and area-averaged firing rates.\n",
+ "Area-averaged firing rates, shown as raw binned spike histograms with 1ms bin width (gray) and convolved histograms, with aGaussian kernel (black) of optimal width"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd9d4912",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "Calculate time series of population- and area-averaged firing rates.\n",
+ "Uses ah.pop_rate_time_series.\n",
+ "If the rates have previously been stored with the\n",
+ "same parameters, they are loaded from file.\n",
+ "\n",
+ "\n",
+ "Parameters\n",
+ "----------\n",
+ "t_min : float, optional\n",
+ " Minimal time in ms of the simulation to take into account\n",
+ " for the calculation. Defaults to 500 ms.\n",
+ "t_max : float, optional\n",
+ " Maximal time in ms of the simulation to take into account\n",
+ " for the calculation. Defaults to the simulation time.\n",
+ "areas : list, optional\n",
+ " Which areas to include in the calculcation.\n",
+ " Defaults to all loaded areas.\n",
+ "pops : list or {'complete'}, optional\n",
+ " Which populations to include in the calculation.\n",
+ " If set to 'complete', all populations the respective areas\n",
+ " are included. Defaults to 'complete'.\n",
+ "kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional\n",
+ " Specifies the kernel to be convolved with the spike histogram.\n",
+ " Defaults to 'binned', which corresponds to no convolution.\n",
+ "resolution: float, optional\n",
+ " Width of the convolution kernel. Specifically it correponds to:\n",
+ " - 'binned' : bin width of the histogram\n",
+ " - 'gauss_time_window' : sigma\n",
+ " - 'alpha_time_window' : time constant of the alpha function\n",
+ " - 'rect_time_window' : width of the moving rectangular function\n",
+ "\"\"\"\n",
+ "M.analysisi.create_rate_time_series(t_max=1000.)\n",
+ "# M.analysis.save()"
+ ]
+ },
{
"cell_type": "markdown",
"id": "ef74ca3e-98dc-49c9-a4a0-2c640e29b1d9",