diff --git a/figures/MAM2EBRAINS/M2E_visualize_fc.py b/figures/MAM2EBRAINS/M2E_visualize_fc.py
index c01c41d6d464d927e07e665abf1abeff44f8f73e..f9a8caa0282137d838f6bd2a1cf9d5653c496786 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_fc.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_fc.py
@@ -90,7 +90,9 @@ def matrix_plot(M, ax, matrix, index, vlim, pos=None):
ax.set_ylabel('Cortical area', size=14)
-def visualize_fc(M, data_path, label):
+def visualize_fc(M, data_path):
+ label = M.simulation.label
+
# compute functional connectivity
compute_fc(M, data_path, label)
compute_communities(M, data_path, label)
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index eda7d934f443c487e17d9464f3102ba781af77e3..78acefdc3f3231b57be1d3045e61068409cf4377 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -53,11 +53,11 @@
" * [2.1. Configuring model parameters](#section_2_1)\n",
" * [2.2. Instantiate a multi-area model](#section_2_2)\n",
" * [2.3. Predict firing rates from theory](#section_2_3)\n",
- " * [2.4. Extract and visualize interareal connectivity](#section_2_3)\n",
+ " * [2.4. Extract and visualize inter-areal connectivity](#section_2_3)\n",
" * [2.5. Run a simulation](#section_2_5)\n",
"* [S3. Visualization of Network Dynamics](#section_3) \n",
" * [3.1. Mean firing rate over simulated populations](#section_3_1)\n",
- " * [3.2. Normalized instantaneous firing rate over simulated areas](#section_3_2)\n",
+ " * [3.2. Instantaneous firing rate over simulated areas](#section_3_2)\n",
" * [3.3. Time-averaged firing rate over all populations](#section_3_3)\n",
" * [3.4. Network dynamics](#section_3_4)\n",
" * [3.5. Functional connectivity](#section_3_5)\n",
@@ -161,6 +161,14 @@
"### 1.1. Parameters to tune <a class=\"anchor\" id=\"section_1_1\"></a>"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "9daf88e5-0d45-4529-a228-70c33900b05e",
+ "metadata": {},
+ "source": [
+ "The values assigned for the following parameters are kept the same as in the paper except for the `scale_down_to` which is set as 0.006 enabling to simulate a down-scaled multi-area model with 2GB RAM. By default, it is set to 1.0 for simulating the full-scale model."
+ ]
+ },
{
"cell_type": "markdown",
"id": "4f67c1ba",
@@ -169,7 +177,7 @@
"|Parameter |Default value|Value range/options|Value assigned|Description|\n",
"|:---------------:|:-----------:|:-----------------:|:------------:|:---------:|\n",
"|scale_down_to |$1.0$ |$(0, 1.0]$ |$0.006$ |$^1$ |\n",
- "|cc_weights_factor|$1.0$ |$[1.0, 2.5]$ |$1.9$ |$^2$ |\n",
+ "|cc_weights_factor|$1.9$ |$[1.0, 2.5]$ |$1.9$ |$^2$ |\n",
"|areas_simulated |complete_area_list|Sublists of complete_area_list|complete_area_list|$^3$|\n",
"|replace_non_simulated_areas|None|None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat'|'het_poisson_stat'|$^4$ |\n",
"|g |$-11.0$|$\\leq -1.0$ |$-11.0$ |$^5$ |\n",
@@ -183,7 +191,7 @@
"source": [
"1. `scale_down_to` is the down-scaling factor that defines the ratio by which the full-scale multi-area model is reduced to a model with fewer neurons and indegrees. This reduction is essential to enable simulation on machines with limited computational power, ensuring that simulation results can be obtained in a relatively shorter timeframe. <br> If the value is `scale_down_to = 1.`, the full-scale network will be simulated. <br> In the pre-set down-scaled version, `scale_down_to = 0.006`. This setting reduces the number of neurons and indegrees to 0.6 % of their full-scale counterparts, facilitating simulation on a typical local machine. <br> **Warning**: This may not yield reasonable results from the network dynamics and is only meant to demonstrate the simulation workflow! <br> \n",
"\n",
- "2. `cc_weights_factor` is the scaling factor that controls the cortico-cortical synaptic strength. <br> By default it is set to `1.0`, ensuring that inter- and intra-area synapses have equal strength. <br> **Important**: This factor plays a crucial role in transitioning the network activity from the ground to the metastable state. In the full-scale network, the ground state and metastable state activities are achieved when this parameter is set to `1.0` and `1.9`, respectively.<br>\n",
+ "2. `cc_weights_factor` is the scaling factor that controls the cortico-cortical synaptic strength. <br> By default it is set to `1.9`, keeping the same value for producing the metastable state as in the original paper. <br> **Important**: This factor plays a crucial role in transitioning the network activity from the ground to the metastable state. In the full-scale network, the ground state and metastable state activities are achieved when this parameter is set to `1.0` and `1.9`, respectively. In the down-scaled multi-area model, a similar metastable state may not be achieved or achieved with a different value. <br>\n",
"\n",
"3. `areas_simulated` specifies the cortical areas to be included in the simulation process. Its default value is `complete_area_list` meaning all the areas in the complete_area_list will be simulated.\n",
"```python\n",
@@ -195,7 +203,7 @@
"\n",
"5. `g` defines the relative inhibitory synaptic strength (in relative units to the excitatory synaptic strength). By default: `-11.0`, as used in the full-scale network. `g = -1.0` means equal excitatory and inhibitory strengths, and `g < -1.0` results in stronger inhibition than excitation.\n",
"\n",
- "6. `rate_ext` defines the rate of the Poissonian spike generator (in spikes/s), by default: `10.0`. It also serves as one of the input parameters of the model. When a larger value is assigned to `rate_ext`, the excitatory background noise is increased."
+ "6. `rate_ext` defines the rate of the Poissonian spike generator (in spikes/s), by default: `10.0`. It also serves as one of the input parameters of the model. When a larger value is assigned to `rate_ext`, the excitatory background noise is increased. Note that the external Poisson indegree onto 5E and 6E is increased by a factor of 1.125 and 1.41666667 respectively, and the external Poisson indegree onto 23E and 5E in area TH is increased by a factor of 1.2."
]
},
{
@@ -206,18 +214,17 @@
"outputs": [],
"source": [
"# Downscaling factor\n",
- "# Value range/options: (0, 1.], change it to 1. to run the full-scale network\n",
+ "# value range/options: (0, 1.], change it to 1. to simulate the full-scale network\n",
"scale_down_to = 0.006\n",
"\n",
"# Scaling factor for cortico-cortical connections (Chi) \n",
- "# Value range/options: [1., 2.5], \n",
- "# A weight factor of 1.0 produces Ground state activity.\n",
- "# A value larger than 1. reproduces the \"Metastable\" state activity, \n",
+ "# value range/options: [1., 2.5], \n",
+ "# a weight factor of 1.0 produces Ground state activity.\n",
"# 1.9 was assigned to produce results in Schmidt et al. (2018).\n",
"cc_weights_factor = 1.9\n",
"\n",
"# Cortical areas included in the simulation\n",
- "# Value range/options: any sublist of complete_area_list\n",
+ "# value range/options: any sublist of complete_area_list\n",
"# where complete_area_list is\n",
"complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd',\n",
" 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd',\n",
@@ -227,8 +234,8 @@
"# areas_simulated = ['V1', 'V2']\n",
"\n",
"# Firing rates used to replace the non-simulated areas\n",
- "# Value range/options: None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat'\n",
- "# If areas_simulated is complete_area_list, then replace_non_simulated_areas will be set as None \n",
+ "# value range/options: None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat'\n",
+ "# if areas_simulated is complete_area_list, then replace_non_simulated_areas will be set as None \n",
"# regardless of the value assigned below\n",
"replace_non_simulated_areas = 'het_poisson_stat'\n",
"\n",
@@ -296,10 +303,10 @@
"# Determine cc_weights_I_factor from cc_weights_factor\n",
"if cc_weights_factor == 1.0: # For ground state with cc_weights_factor as 1., \n",
" cc_weights_I_factor = 1.0 # cc_weights_I_factor is set to 1.\n",
- "elif cc_weights_factor > 1.0 and cc_weights_factor <= 2.5: # For metastable state with cc_weights_factor in (1., 2.5],\n",
+ "elif cc_weights_factor > 1.0: # For cc_weights_factor larger than 1.0,\n",
" cc_weights_I_factor = 2.0 # cc_weights_I_factor is set to 2.\n",
"else: # cc_weights_factor outside of (1., 2.5], raise error\n",
- " raise Exception(\"A value in range [1.0, 2.5] should be assigned to the parameter cc_weights_factor!\")\n",
+ " raise Exception(\"A value that is equal to or larger than 1.0 should be assigned to the parameter cc_weights_factor!\")\n",
"\n",
"# Connection parameters\n",
"conn_params = {\n",
@@ -371,6 +378,14 @@
"### 2.3. Predict firing rates from theory <a class=\"anchor\" id=\"section_2_3\"></a>"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "07cc84bf-dbcf-4ac3-b76e-ebe0886b4012",
+ "metadata": {},
+ "source": [
+ "Note: the prediction may differ from the simulation results, especially in the presence of synchrony."
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -397,7 +412,7 @@
"id": "2062ddf3",
"metadata": {},
"source": [
- "### 2.4. Extract and visualize interareal connectivity <a class=\"anchor\" id=\"section_2_4\"></a>"
+ "### 2.4. Extract and visualize inter-areal connectivity <a class=\"anchor\" id=\"section_2_4\"></a>"
]
},
{
@@ -406,15 +421,23 @@
"metadata": {},
"source": [
"The connectivity and neuron numbers are stored in the attributes of the model class. \n",
- "- Neuron numbers are stored in `M.N` as a dictionary (and in `M.N_vec` as an array).\n",
+ "- Neuron numbers of all populations in each area are stored in `M.N` as a dictionary (and in `M.N_vec` as an array).\n",
"\n",
"- Indegrees are stored in `M.K` as a dictionary (and in `M.K_matrix` as an array).<br>\n",
" Dictionary of nodes indegrees organized as:<br>\n",
- " `{<source_area>: {<source_pop>: {<target_area>: {<target_pop>: indegree_values}}}}`\n",
+ " `{<target_area>: {<target_pop>: {<source_area>: {<source_pop>: indegree_values}}}}`\n",
"\n",
"- Number of synapses can be accessed via `M.synapses` (and in `M.syn_matrix` as an array). <br>\n",
" Dictionary of synapses that target neurons receive, it is organized as:<br>\n",
- " `{<source_area>: {<source_pop>: {<target_area>: {<target_pop>: number_of_synapses}}}}`"
+ " `{<target_area>: {<target_pop>: {<source_area>: {<source_pop>: number_of_synapses}}}}`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6b473f0e-ceca-47e1-9563-a613574497c4",
+ "metadata": {},
+ "source": [
+ "The figure below shows the inter-areal connectivity of the model expressed as the relative indegrees of each target area. The relative indegree of a target area from a specific source area is calculated by dividing its indegree by the sum of indegrees that the target area receives from all sources."
]
},
{
@@ -529,7 +552,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# Print the mean firng rate over simulated populations\n",
+ "# Print the mean firing rate over simulated populations\n",
"from M2E_firing_rate import mean_firing_rate\n",
"mean_firing_rate(M, data_path)"
]
@@ -541,7 +564,7 @@
"tags": []
},
"source": [
- "### 3.2. Normalized instantaneous firing rate over simulated areas <a class=\"anchor\" id=\"section_3_2\"></a>"
+ "### 3.2. Instantaneous firing rate over simulated areas <a class=\"anchor\" id=\"section_3_2\"></a>"
]
},
{
@@ -612,8 +635,8 @@
},
"outputs": [],
"source": [
- "# Choose at most 3 areas from the areas_simulated to show their sipking activities\n",
- "# By default, it's set as ['V1', 'V2', 'FEF'] when all areas from complete_area_list are simulated\n",
+ "# Choose at most 3 areas from the areas_simulated to show their spiking activities\n",
+ "# By default, the list is ['V1', 'V2', 'FEF'] when all areas from complete_area_list are simulated\n",
"raster_areas = ['V1', 'V2', 'FEF']\n",
"\n",
"from M2E_visualize_dynamics import visual_dynamics\n",
@@ -633,7 +656,7 @@
"id": "013adaf8-af8b-470e-94f0-b69121d1ca2c",
"metadata": {},
"source": [
- "Comparison of inter-area interactions of the down-scaled MAM visualized as functional connectivity and experimental data. (A) Simulated functional connectivity (FC) for w = 1.9 measured by the zero-time-lag correlation coefficient of synaptic input currents. (B) FC of macaque resting-state fMRI (see Materials and methods)."
+ "Comparison of area-level functional connectivity (FC) between the down-scaled MAM and macaque experimental data. (A) Simulated FC measured by the zero-time-lag correlation coefficient of synaptic input currents. (B) FC of macaque resting-state fMRI (see Materials and methods)."
]
},
{
@@ -653,8 +676,7 @@
"outputs": [],
"source": [
"from M2E_visualize_fc import visualize_fc\n",
- "label = M.simulation.label\n",
- "visualize_fc(M, data_path, label)"
+ "visualize_fc(M, data_path)"
]
},
{