diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index ec7e2c915dda1a9883a22824b1616166a6aa432d..d98ab0e80d0fdfb06ab867bedd2fd5e03fb86b10 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -28,7 +28,7 @@
     "    * [2.3. Extract and visualize interareal connectivity](#section_2_3)\n",
     "    * [2.4. Run a simulation](#section_2_4)\n",
     "* [S3. Data Loading and Processing](#section_3)\n",
-    "* [S4. Simulation Results Visualziation](#section_4) \n",
+    "* [S4. Simulation Results Visualization](#section_4) \n",
     "    * [4.1. Instantaneous and mean firing rate across all populations](#section_4_1)\n",
     "    * [4.2 Resting state plots](#section_4_2)\n",
     "    * [4.3 Time-averaged population rates](#section_4_3)"
@@ -233,7 +233,7 @@
     "# Downscaling factor\n",
     "# Value range/options: (0, 1.]\n",
     "# Value assgined: 0.005\n",
-    "scale_down_to = 0.005 # Change it to 1. for running the fullscale network\n",
+    "scale_down_to = 1 # Change it to 1. for running the fullscale network\n",
     "\n",
     "# Scaling factor for cortico-cortical connections (chi) \n",
     "# Value range/options: [1., 2.5]\n",
@@ -354,7 +354,7 @@
      "output_type": "stream",
      "text": [
       "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 4654\n"
+      "RAND_DATA_LABEL 295\n"
      ]
     },
     {
@@ -375,8 +375,8 @@
       "========================================\n",
       "Customized parameters\n",
       "--------------------\n",
-      "{'K_scaling': 0.005,\n",
-      " 'N_scaling': 0.005,\n",
+      "{'K_scaling': 1,\n",
+      " 'N_scaling': 1,\n",
       " 'connection_params': {'K_stable': 'K_stable.npy',\n",
       "                       'av_indegree_V1': 3950.0,\n",
       "                       'fac_nu_ext_5E': 1.125,\n",
@@ -388,7 +388,7 @@
       " 'input_params': {'rate_ext': 10.0},\n",
       " 'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}\n",
       "========================================\n",
-      "Simulation label: 27d81076e6d6e9e591684be053078477\n",
+      "Simulation label: 155470013b00dadc9c4a4af26ef5090e\n",
       "Copied files.\n",
       "Initialized simulation class.\n"
      ]
@@ -421,7 +421,7 @@
      "output_type": "stream",
      "text": [
       "Iteration: 0\n",
-      "Mean-field theory predicts an average firing rate of 29.588 spikes/s across all populations.\n"
+      "Mean-field theory predicts an average firing rate of 3.729 spikes/s across all populations.\n"
      ]
     }
    ],
@@ -453,24 +453,59 @@
    "id": "6316ac24",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# Neuron numbers\n",
+    "\n",
+    "# Dictionary of neuron numbers\n",
+    "# M.N\n",
+    "\n",
+    "# Array of neuron numbers\n",
+    "# M.N_vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8408d463-557b-481b-afc1-5fbbbd67306d",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Indegrees\n",
+    "\n",
     "# Dictionary of nodes indegrees organized as:\n",
     "# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: indegree_values}}}}\n",
-    "# M.K"
+    "# M.K\n",
+    "\n",
+    "# Array of nodes indegrees\n",
+    "# M.K_matrix.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "445a722a",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Synapses\n",
+    "\n",
     "# Dictionary of synapses that target neurons receive, it is organized as:\n",
     "# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: number_of_synapses}}}}\n",
-    "# M.synapses"
+    "# M.synapses\n",
+    "\n",
+    "# Array of \n",
+    "# M.syn_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05512922-26e5-425f-90a4-0df7c2279ccf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from M2E_visualize_interareal_connectivity import visualize_interareal_connectivity\n",
+    "visualize_interareal_connectivity(M)"
    ]
   },
   {
@@ -501,15 +536,15 @@
      "text": [
       "Prepared simulation in 0.00 seconds.\n",
       "Rank 0: created area V1 with 0 local nodes\n",
-      "Memory after V1 : 1911.71 MB\n",
+      "Memory after V1 : 1911.70 MB\n",
       "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1938.27 MB\n",
+      "Memory after V2 : 1938.30 MB\n",
       "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1967.57 MB\n",
+      "Memory after VP : 1967.50 MB\n",
       "Rank 0: created area V3 with 0 local nodes\n",
       "Memory after V3 : 1995.87 MB\n",
       "Rank 0: created area V3A with 0 local nodes\n",
-      "Memory after V3A : 2015.65 MB\n",
+      "Memory after V3A : 2015.69 MB\n",
       "Rank 0: created area MT with 0 local nodes\n",
       "Memory after MT : 2041.32 MB\n",
       "Rank 0: created area V4t with 0 local nodes\n",
@@ -517,21 +552,21 @@
       "Rank 0: created area V4 with 0 local nodes\n",
       "Memory after V4 : 2093.25 MB\n",
       "Rank 0: created area VOT with 0 local nodes\n",
-      "Memory after VOT : 2118.55 MB\n",
+      "Memory after VOT : 2118.44 MB\n",
       "Rank 0: created area MSTd with 0 local nodes\n",
-      "Memory after MSTd : 2140.02 MB\n",
+      "Memory after MSTd : 2140.03 MB\n",
       "Rank 0: created area PIP with 0 local nodes\n",
-      "Memory after PIP : 2161.34 MB\n",
+      "Memory after PIP : 2161.38 MB\n",
       "Rank 0: created area PO with 0 local nodes\n",
-      "Memory after PO : 2182.81 MB\n",
+      "Memory after PO : 2182.89 MB\n",
       "Rank 0: created area DP with 0 local nodes\n",
-      "Memory after DP : 2203.11 MB\n",
+      "Memory after DP : 2203.16 MB\n",
       "Rank 0: created area MIP with 0 local nodes\n",
-      "Memory after MIP : 2224.61 MB\n",
+      "Memory after MIP : 2224.65 MB\n",
       "Rank 0: created area MDP with 0 local nodes\n",
       "Memory after MDP : 2246.05 MB\n",
       "Rank 0: created area VIP with 0 local nodes\n",
-      "Memory after VIP : 2267.98 MB\n",
+      "Memory after VIP : 2268.10 MB\n",
       "Rank 0: created area LIP with 0 local nodes\n",
       "Memory after LIP : 2292.05 MB\n",
       "Rank 0: created area PITv with 0 local nodes\n",
@@ -539,34 +574,34 @@
       "Rank 0: created area PITd with 0 local nodes\n",
       "Memory after PITd : 2342.60 MB\n",
       "Rank 0: created area MSTl with 0 local nodes\n",
-      "Memory after MSTl : 2363.98 MB\n",
+      "Memory after MSTl : 2364.06 MB\n",
       "Rank 0: created area CITv with 0 local nodes\n",
       "Memory after CITv : 2383.12 MB\n",
       "Rank 0: created area CITd with 0 local nodes\n",
-      "Memory after CITd : 2402.57 MB\n",
+      "Memory after CITd : 2402.46 MB\n",
       "Rank 0: created area FEF with 0 local nodes\n",
-      "Memory after FEF : 2423.96 MB\n",
+      "Memory after FEF : 2423.93 MB\n",
       "Rank 0: created area TF with 0 local nodes\n",
-      "Memory after TF : 2439.61 MB\n",
+      "Memory after TF : 2439.57 MB\n",
       "Rank 0: created area AITv with 0 local nodes\n",
-      "Memory after AITv : 2462.28 MB\n",
+      "Memory after AITv : 2462.29 MB\n",
       "Rank 0: created area FST with 0 local nodes\n",
-      "Memory after FST : 2479.05 MB\n",
+      "Memory after FST : 2479.02 MB\n",
       "Rank 0: created area 7a with 0 local nodes\n",
-      "Memory after 7a : 2500.27 MB\n",
+      "Memory after 7a : 2500.19 MB\n",
       "Rank 0: created area STPp with 0 local nodes\n",
-      "Memory after STPp : 2518.95 MB\n",
+      "Memory after STPp : 2518.91 MB\n",
       "Rank 0: created area STPa with 0 local nodes\n",
       "Memory after STPa : 2538.05 MB\n",
       "Rank 0: created area 46 with 0 local nodes\n",
-      "Memory after 46 : 2553.38 MB\n",
+      "Memory after 46 : 2553.50 MB\n",
       "Rank 0: created area AITd with 0 local nodes\n",
-      "Memory after AITd : 2576.09 MB\n",
+      "Memory after AITd : 2576.05 MB\n",
       "Rank 0: created area TH with 0 local nodes\n",
       "Memory after TH : 2588.76 MB\n",
-      "Created areas and internal connections in 2.27 seconds.\n",
-      "Created cortico-cortical connections in 22.79 seconds.\n",
-      "Simulated network in 76.43 seconds.\n"
+      "Created areas and internal connections in 2.33 seconds.\n",
+      "Created cortico-cortical connections in 22.84 seconds.\n",
+      "Simulated network in 74.35 seconds.\n"
      ]
     }
    ],
@@ -619,6 +654,13 @@
       "pop_rates\n",
       "synchrony\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "python3: can't open file './../Schmidt2018_dyn/compute_bold_signal.py': [Errno 2] No such file or directory\n"
+     ]
     }
    ],
    "source": [
@@ -677,8 +719,8 @@
     }
    ],
    "source": [
-    "from MAM2EBRAINS_VISUALIZATION import plot_instan_mean_firing_rate\n",
-    "plot_instan_mean_firing_rate(tsteps, firing_rate, sim_params)"
+    "from M2E_VISUALIZATION import plot_instan_mean_firing_rate\n",
+    "plot_instan_mean_firing_rate(M)"
    ]
   },
   {
@@ -705,41 +747,11 @@
     "tags": []
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 1478\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{}\n",
-      "========================================\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 510.235x450.49 with 10 Axes>"
+       "<Figure size 720x635.692 with 10 Axes>"
       ]
      },
      "metadata": {
@@ -839,9 +851,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "EBRAINS-23.02",
+   "display_name": "EBRAINS-23.06",
    "language": "python",
-   "name": "ebrains-23.02"
+   "name": "ebrains-23.06"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/MAM2EBRAINS_LOAD_DATA-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_load_and_create_data-checkpoint.py
similarity index 89%
rename from figures/MAM2EBRAINS/.ipynb_checkpoints/MAM2EBRAINS_LOAD_DATA-checkpoint.py
rename to figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_load_and_create_data-checkpoint.py
index c8c46cf1ce8fa716cb425e1d34be6aee9ecae36b..0224fb1c8846ff344a6d491faa0a720e6ec7e355 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/MAM2EBRAINS_LOAD_DATA-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_load_and_create_data-checkpoint.py
@@ -1,15 +1,10 @@
 import numpy as np
 import subprocess
 import matplotlib.pyplot as plt
-from multiarea_model import Analysis
 
-def load_data(M):
-    # load spike data and calculate instantaneous and mean firing rates
-    data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
-    tsteps, spikecount = np.unique(data[:,1], return_counts=True)
-    firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+from multiarea_model import Analysis
 
-    
+def load_and_create_data(M):
     """
     Analysis class.
     An instance of the analysis class for the given network and simulation.
@@ -191,15 +186,15 @@ def load_data(M):
     
     A.save()
     
-    """
-    Compute BOLD signal for a given area from the time series of
-    population-averaged spike rates of a given simulation using the
-    neuRosim package of R (see Schmidt et al. 2018 for more details).
-    """
-    try:
-        subprocess.run(['python3', './../Schmidt2018_dyn/compute_bold_signal.py'])
-        # subprocess.run(['Rscript', '--vanilla', 'compute_bold_signal.R', fn, out_fn])
-    except FileNotFoundError:
-        raise FileNotFoundError("Executing R failed. Did you install R?")
+    # """
+    # Compute BOLD signal for a given area from the time series of
+    # population-averaged spike rates of a given simulation using the
+    # neuRosim package of R (see Schmidt et al. 2018 for more details).
+    # """
+    # try:
+    #     subprocess.run(['python3', './../Schmidt2018_dyn/compute_bold_signal.py'])
+    #     # subprocess.run(['Rscript', '--vanilla', 'compute_bold_signal.R', fn, out_fn])
+    # except FileNotFoundError:
+    #     raise FileNotFoundError("Executing R failed. Did you install R?")
     
-    return A, tsteps, firing_rate
\ No newline at end of file
+    return A
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_instaneus_and_mean_rate-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_instaneus_and_mean_rate-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d223e89cd4911327489812e8eb16008b38eae16
--- /dev/null
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_instaneus_and_mean_rate-checkpoint.py
@@ -0,0 +1,15 @@
+def plot_instan_mean_firing_rate(M):
+    # load spike data and calculate instantaneous and mean firing rates
+    data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
+    tsteps, spikecount = np.unique(data[:,1], return_counts=True)
+    firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+
+    ax = pl.subplot()
+    ax.plot(tsteps, rate)
+    ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
+    ax.set_title('Instantaneous and mean firing rate across all populations')
+    ax.set_xlabel('time (ms)')
+    ax.set_ylabel('firing rate (spikes / s)')
+    ax.set_xlim(0, sim_params['t_sim'])
+    ax.set_ylim(0, 50)
+    ax.legend()
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..713026c1524e5cf480e59fa42709eb551185089d
--- /dev/null
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
@@ -0,0 +1,375 @@
+import json
+import numpy as np
+import matplotlib.pyplot as pl
+import os
+
+from helpers import area_list, datapath
+from matplotlib import gridspec
+from matplotlib.colors import LogNorm
+from matplotlib.ticker import FixedLocator
+from matplotlib import rc_file
+from multiarea_model import MultiAreaModel
+from plotcolors import myblue
+from scipy import stats
+
+# rc_file('plotstyle.rc')
+
+def visualize_interareal_connectivity(M):
+    scale_down_to = 1
+    cc_weights_factor = 1.0
+    areas_simulated = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 
+                       'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 
+                       'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 
+                       'STPa', '46', 'AITd', 'TH']
+    replace_non_simulated_areas = 'het_poisson_stat'
+    
+    conn_params = {
+        'replace_non_simulated_areas': 'het_poisson_stat',
+        'g': -11.,
+        'K_stable': 'K_stable.npy',
+        'fac_nu_ext_TH': 1.2,
+        'fac_nu_ext_5E': 1.125,
+        'fac_nu_ext_6E': 1.41666667,
+        'av_indegree_V1': 3950.
+    }
+
+    input_params = {
+        'rate_ext': 10.
+    } 
+
+    neuron_params = {
+        'V0_mean': -150.,
+        'V0_sd': 50.}
+
+    network_params = {
+        'N_scaling': scale_down_to, # Scaling of population sizes, by default: 1.
+        'K_scaling': scale_down_to, # Scaling of indegrees, by default: 1.
+        'fullscale_rates': 'tests/fullscale_rates.json',
+        'input_params': input_params, # Input parameters
+        'connection_params': conn_params, # Connection parameters
+        'neuron_params': neuron_params # Neuron parameters
+    } 
+
+    sim_params = {
+        'areas_simulated': areas_simulated,
+        't_sim': 2000., # Simulated time (in ms), by default: 10.0
+        'num_processes': 1, # The number of MPI processes, by default: 1
+        'local_num_threads': 1, # The number of threads per MPI process, by default: 1
+        'recording_dict': {'record_vm': False},
+        'rng_seed': 1  # global random seed
+    }
+
+    theory_params = {
+        'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01
+    } 
+
+    M_full_scale = MultiAreaModel(network_params, 
+                                  simulation=True,
+                                  sim_spec=sim_params,
+                                  theory=True,
+                                  theory_spec=theory_params)
+    
+    """
+    Figure layout
+    """
+    nrows = 2
+    ncols = 2
+    width = 6.8556
+    panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
+
+    height = width / panel_wh_ratio * float(nrows) / ncols
+    print(width, height)
+    pl.rcParams['figure.figsize'] = (width, height)
+
+    fig = pl.figure()
+    axes = {}
+
+    # gs1 = gridspec.GridSpec(2, 2)
+    gs1 = gridspec.GridSpec(1, 2)
+    gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.1, hspace=0.3)
+
+    # axes['A'] = pl.subplot(gs1[:1, :1])
+    # axes['B'] = pl.subplot(gs1[:1, 1:2])
+    axes['B'] = pl.subplot(gs1[:1, :1])
+    axes['D'] = pl.subplot(gs1[:1, 1:2])
+
+    # pos = axes['A'].get_position()
+    pos2 = axes['D'].get_position()
+    # axes['C'] = pl.axes([pos.x0 + 0.01, pos2.y0, pos.x1 - pos.x0 - 0.025, 0.23])
+
+    print(pos.x1 - pos.x0 - 0.025)
+
+    # labels = ['A', 'B', 'C', 'D']
+    labels = ['B', 'D']
+    for label in labels:
+        if label in ['C']:
+            label_pos = [-0.045, 1.18]
+        else:
+            label_pos = [-0.2, 1.04]
+        # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
+        #         fontdict={'fontsize': 10, 'weight': 'bold',
+        #                   'horizontalalignment': 'left', 'verticalalignment':
+        #                   'bottom'}, transform=axes[label].transAxes)
+        pl.text(label_pos[0], label_pos[1], label,
+                 fontdict={'fontsize': 10, 'weight': 'bold', 
+                           'horizontalalignment': 'left', 'verticalalignment': 
+                           'bottom'}, transform=axes[label].transAxes)
+
+    # """
+    # Load data
+    # """
+    # M = MultiAreaModel({})
+
+    # with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
+    #     proc = json.load(f)
+    # with open(os.path.join(datapath, 'viscortex_raw_data.json'), 'r') as f:
+    #     raw = json.load(f)
+
+    # FLN_Data_FV91 = proc['FLN_Data_FV91']
+
+    # cocomac_data = raw['cocomac_data']
+    # median_distance_data = raw['median_distance_data']
+
+    # cocomac = np.zeros((32, 32))
+    # conn_matrix = np.zeros((32, 32))
+    # for i, area1 in enumerate(area_list[::-1]):
+    #     for j, area2 in enumerate(area_list):
+    #         if M.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
+    #             cocomac[i][j] = 1.
+    #         if area2 in FLN_Data_FV91[area1]:
+    #             conn_matrix[i][j] = FLN_Data_FV91[area1][area2]
+
+    # """
+    # Panel A: CoCoMac Data
+    # """
+    # ax = axes['A']
+    # ax.yaxis.set_ticks_position("left")
+    # ax.xaxis.set_ticks_position("bottom")
+
+    # ax.set_aspect(1. / ax.get_data_ratio())
+    # ax.yaxis.set_ticks_position("none")
+    # ax.xaxis.set_ticks_position("none")
+
+    # masked_matrix = np.ma.masked_values(cocomac, 0.0)
+    # cmap = pl.cm.binary
+    # cmap.set_bad('w', 1.0)
+
+    # x = np.arange(0, len(area_list) + 1)
+    # y = np.arange(0, len(area_list[::-1]) + 1)
+    # X, Y = np.meshgrid(x, y)
+
+    # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_yticklabels(area_list[::-1], size=6.)
+
+    # ax.set_ylabel('Target area')
+    # ax.set_xlabel('Source area')
+
+    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
+    #                    edgecolors='None', vmin=0., vmax=1.)
+
+    # t = FixedLocator([])
+    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    # cbar.set_alpha(0.)
+    # cbar.remove()
+
+    # """
+    # Panel B: Data from Markov et al. (2014) "A weighted and directed
+    # interareal connectivity matrix for macaque cerebral cortex."
+    # Cerebral Cortex, 24(1), 17–36.
+    # """
+    # ax = axes['B']
+    # ax.set_aspect(1. / ax.get_data_ratio())
+    # ax.yaxis.set_ticks_position("none")
+    # ax.xaxis.set_ticks_position("none")
+
+    # masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
+    # cmap = pl.get_cmap('inferno')
+    # cmap.set_bad('w', 1.0)
+
+    # x = np.arange(0, len(area_list) + 1)
+    # y = np.arange(0, len(area_list[::-1]) + 1)
+    # X, Y = np.meshgrid(x, y)
+
+    # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_yticklabels(area_list[::-1], size=6.)
+
+    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
+    #                    edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+
+    # t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    # cbar.set_alpha(0.)
+
+    """
+    Panel B: Interareal connectivity of full-scaling multi-area model
+    """
+    conn_matrix_full_scale = np.zeros((32, 32))
+    for i, area1 in enumerate(area_list[::-1]):
+        for j, area2 in enumerate(area_list):
+            conn_matrix_full_scale[i][j] = M_full_scale.K_areas[area1][
+                area2] / np.sum(list(M_full_scale.K_areas[area1].values()))
+
+    ax = axes['D']
+    ax.yaxis.set_ticks_position("none")
+    ax.xaxis.set_ticks_position("none")
+
+    ax.set_aspect(1. / ax.get_data_ratio())
+
+    masked_matrix_full_scale = np.ma.masked_values(conn_matrix_full_scale, 0.0)
+    cmap = pl.get_cmap('inferno')
+    cmap.set_bad('w', 1.0)
+
+    x = np.arange(0, len(area_list) + 1)
+    y = np.arange(0, len(area_list[::-1]) + 1)
+    X, Y = np.meshgrid(x, y)
+
+    ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_yticklabels(area_list[::-1], size=6.)
+
+    ax.set_ylabel('Target area')
+    ax.set_xlabel('Source area')
+    im = ax.pcolormesh(masked_matrix_full_scale, cmap=cmap,
+                       edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+
+    t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+    cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    cbar.set_alpha(0.)
+
+    # """
+    # Panel C: Exponential decay of FLN with distance
+    # """
+    # FLN_values_FV91 = np.array([])
+    # distances_FV91 = np.array([])
+
+    # for target_area in FLN_Data_FV91:
+    #     for source_area in FLN_Data_FV91[target_area]:
+    #         if target_area in median_distance_data and source_area in median_distance_data:
+    #             if FLN_Data_FV91[target_area][source_area]:
+    #                 FLN_values_FV91 = np.append(FLN_values_FV91, FLN_Data_FV91[
+    #                                             target_area][source_area])
+    #                 distances_FV91 = np.append(distances_FV91, median_distance_data[
+    #                                            target_area][source_area])
+
+    # # Linear fit of distances vs. log FLN
+    # print("\n \n Linear fit to logarithmic values")
+    # gradient, intercept, r_value, p_value, std_err = stats.linregress(
+    #     distances_FV91, np.log(FLN_values_FV91))
+    # print("Raw parameters: ", gradient, intercept)
+    # print("Transformed parameters: ", -gradient, np.exp(intercept))
+    # print('r_value**2', r_value ** 2)
+    # print('p_value', p_value)
+    # print('std_err', std_err)
+
+    # ax = axes['C']
+    # ax.yaxis.set_ticks_position("left")
+    # ax.xaxis.set_ticks_position("bottom")
+
+    # ax.yaxis.set_ticks_position("left")
+    # ax.xaxis.set_ticks_position("bottom")
+
+    # ax.spines['right'].set_color('none')
+    # ax.spines['top'].set_color('none')
+    # ax.yaxis.set_ticks_position("left")
+    # ax.xaxis.set_ticks_position("bottom")
+
+    # ax.plot(distances_FV91, np.log10(FLN_values_FV91), '.', color=myblue)
+    # x = np.arange(np.min(distances_FV91), np.max(distances_FV91), 1)
+    # ax.plot(x, (intercept + gradient * x) / np.log(10), linewidth=2.0,
+    #         color='Black', label='Linear regression fit')
+
+    # ax.set_xlabel('Distance (mm)', labelpad=7)
+    # ax.set_ylabel(r'$\log(FLN)$')
+    # ax.set_yticks([-6, -4, -2, 0])
+
+    # print("log fit")
+    # print(np.corrcoef(gradient * distances_FV91 + intercept, np.log(FLN_values_FV91))[0][1])
+
+    # """
+    # Panel D: Resulting connectivity matrix
+    # """
+    # conn_matrix = np.zeros((32, 32))
+    # for i, area1 in enumerate(area_list[::-1]):
+    #     for j, area2 in enumerate(area_list):
+    #         conn_matrix[i][j] = M.K_areas[area1][
+    #             area2] / np.sum(list(M.K_areas[area1].values()))
+
+    # ax = axes['D']
+    # ax.yaxis.set_ticks_position("none")
+    # ax.xaxis.set_ticks_position("none")
+
+    # ax.set_aspect(1. / ax.get_data_ratio())
+
+    # masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
+    # cmap = pl.get_cmap('inferno')
+    # cmap.set_bad('w', 1.0)
+
+    # x = np.arange(0, len(area_list) + 1)
+    # y = np.arange(0, len(area_list[::-1]) + 1)
+    # X, Y = np.meshgrid(x, y)
+
+    # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_yticklabels(area_list[::-1], size=6.)
+
+    # ax.set_ylabel('Target area')
+    # ax.set_xlabel('Source area')
+    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
+    #                    edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+
+    # t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    # cbar.set_alpha(0.)
+
+    """
+    Panel D: Interareal connectivity of down-scaling multi-area model
+    """
+    conn_matrix_down_scale = np.zeros((32, 32))
+    for i, area1 in enumerate(area_list[::-1]):
+        for j, area2 in enumerate(area_list):
+            conn_matrix_down_scale[i][j] = M.K_areas[area1][
+                area2] / np.sum(list(M.K_areas[area1].values()))
+
+    ax = axes['D']
+    ax.yaxis.set_ticks_position("none")
+    ax.xaxis.set_ticks_position("none")
+
+    ax.set_aspect(1. / ax.get_data_ratio())
+
+    masked_matrix_down_scale = np.ma.masked_values(conn_matrix_down_scale, 0.0)
+    cmap = pl.get_cmap('inferno')
+    cmap.set_bad('w', 1.0)
+
+    x = np.arange(0, len(area_list) + 1)
+    y = np.arange(0, len(area_list[::-1]) + 1)
+    X, Y = np.meshgrid(x, y)
+
+    ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_yticklabels(area_list[::-1], size=6.)
+
+    ax.set_ylabel('Target area')
+    ax.set_xlabel('Source area')
+    im = ax.pcolormesh(masked_matrix_down_scale, cmap=cmap,
+                       edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+
+    t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+    cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    cbar.set_alpha(0.)
+
+    # """
+    # Save figure
+    # """
+    # pl.savefig('Fig4_connectivity.eps')
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/.ipynb_checkpoints/MAM2EBRAINS_VISUALIZATION-checkpoint.py b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
similarity index 92%
rename from figures/MAM2EBRAINS/.ipynb_checkpoints/MAM2EBRAINS_VISUALIZATION-checkpoint.py
rename to figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
index 6d0f15f24488aed1aa22eb1cf9299efc80775d91..1b89896babc39f435a88db08beebf53adad27d9c 100644
--- a/figures/MAM2EBRAINS/.ipynb_checkpoints/MAM2EBRAINS_VISUALIZATION-checkpoint.py
+++ b/figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
@@ -17,19 +17,6 @@ from matplotlib import gridspec
 icolor = myred
 ecolor = myblue
 
-
-# Instantaneous and mean firing rate across all populations
-def plot_instan_mean_firing_rate(tsteps, rate, sim_params):
-    ax = pl.subplot()
-    ax.plot(tsteps, rate)
-    ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
-    ax.set_title('Instantaneous and mean firing rate across all populations')
-    ax.set_xlabel('time (ms)')
-    ax.set_ylabel('firing rate (spikes / s)')
-    ax.set_xlim(0, sim_params['t_sim'])
-    ax.set_ylim(0, 50)
-    ax.legend()
-
 def set_boxplot_props(d):
     for i in range(len(d['boxes'])):
         if i % 2 == 0:
@@ -83,7 +70,6 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
 
     gs3 = gridspec.GridSpec(1, 1)
     gs3.update(left=0.1, right=0.95, top=0.3, bottom=0.075)
-    # gs3.update(left=0.1, right=0.95, top=0.25, bottom=0.075)
     axes['G'] = pl.subplot(gs3[:1, :1])
 
     areas = ['V1', 'V2', 'FEF']
@@ -96,8 +82,9 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
         #                   'horizontalalignment': 'left', 'verticalalignment':
         #                   'bottom'}, transform=axes[label].transAxes)
         pl.text(label_pos[0], label_pos[1], label + ': ' + area,
-                 fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left', 
-                           'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
+                 fontdict={'fontsize': 10, 'weight': 'bold', 
+                           'horizontalalignment': 'left', 'verticalalignment': 
+                           'bottom'}, transform=axes[label].transAxes)
 
     label = 'G'
     label_pos = [-0.1, 0.92]
@@ -106,8 +93,9 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
     #                   'horizontalalignment': 'left', 'verticalalignment':
     #                   'bottom'}, transform=axes[label].transAxes)
     pl.text(label_pos[0], label_pos[1], label,
-             fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left', 
-                       'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
+             fontdict={'fontsize': 10, 'weight': 'bold', 
+                       'horizontalalignment': 'left', 'verticalalignment': 
+                       'bottom'}, transform=axes[label].transAxes)
 
     labels = ['E', 'D', 'F']
     for label in labels:
@@ -117,8 +105,9 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
         #                   'horizontalalignment': 'left', 'verticalalignment':
         #                   'bottom'}, transform=axes[label].transAxes)
         pl.text(label_pos[0], label_pos[1], label,
-             fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left', 
-                       'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
+             fontdict={'fontsize': 10, 'weight': 'bold', 
+                       'horizontalalignment': 'left', 'verticalalignment': 
+                       'bottom'}, transform=axes[label].transAxes)
         
 
     labels = ['A', 'B', 'C', 'D', 'E', 'F']
@@ -151,10 +140,6 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
 #         models = init_models('Fig5')
 #         label_spikes = models[0].simulation.label
 #         label = models[1].simulation.label
-        
-    # model = M
-    label_spikes = label_spikes
-    label = label_spikes
 
     # """
     # Create MultiAreaModel instance to have access to data structures
diff --git a/figures/MAM2EBRAINS/MAM2EBRAINS_LOAD_DATA.py b/figures/MAM2EBRAINS/M2E_load_and_create_data.py
similarity index 89%
rename from figures/MAM2EBRAINS/MAM2EBRAINS_LOAD_DATA.py
rename to figures/MAM2EBRAINS/M2E_load_and_create_data.py
index c8c46cf1ce8fa716cb425e1d34be6aee9ecae36b..0224fb1c8846ff344a6d491faa0a720e6ec7e355 100644
--- a/figures/MAM2EBRAINS/MAM2EBRAINS_LOAD_DATA.py
+++ b/figures/MAM2EBRAINS/M2E_load_and_create_data.py
@@ -1,15 +1,10 @@
 import numpy as np
 import subprocess
 import matplotlib.pyplot as plt
-from multiarea_model import Analysis
 
-def load_data(M):
-    # load spike data and calculate instantaneous and mean firing rates
-    data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
-    tsteps, spikecount = np.unique(data[:,1], return_counts=True)
-    firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+from multiarea_model import Analysis
 
-    
+def load_and_create_data(M):
     """
     Analysis class.
     An instance of the analysis class for the given network and simulation.
@@ -191,15 +186,15 @@ def load_data(M):
     
     A.save()
     
-    """
-    Compute BOLD signal for a given area from the time series of
-    population-averaged spike rates of a given simulation using the
-    neuRosim package of R (see Schmidt et al. 2018 for more details).
-    """
-    try:
-        subprocess.run(['python3', './../Schmidt2018_dyn/compute_bold_signal.py'])
-        # subprocess.run(['Rscript', '--vanilla', 'compute_bold_signal.R', fn, out_fn])
-    except FileNotFoundError:
-        raise FileNotFoundError("Executing R failed. Did you install R?")
+    # """
+    # Compute BOLD signal for a given area from the time series of
+    # population-averaged spike rates of a given simulation using the
+    # neuRosim package of R (see Schmidt et al. 2018 for more details).
+    # """
+    # try:
+    #     subprocess.run(['python3', './../Schmidt2018_dyn/compute_bold_signal.py'])
+    #     # subprocess.run(['Rscript', '--vanilla', 'compute_bold_signal.R', fn, out_fn])
+    # except FileNotFoundError:
+    #     raise FileNotFoundError("Executing R failed. Did you install R?")
     
-    return A, tsteps, firing_rate
\ No newline at end of file
+    return A
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/M2E_visualize.py b/figures/MAM2EBRAINS/M2E_visualize.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/figures/MAM2EBRAINS/M2E_visualize_instaneus_and_mean_rate.py b/figures/MAM2EBRAINS/M2E_visualize_instaneus_and_mean_rate.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d223e89cd4911327489812e8eb16008b38eae16
--- /dev/null
+++ b/figures/MAM2EBRAINS/M2E_visualize_instaneus_and_mean_rate.py
@@ -0,0 +1,15 @@
+def plot_instan_mean_firing_rate(M):
+    # load spike data and calculate instantaneous and mean firing rates
+    data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
+    tsteps, spikecount = np.unique(data[:,1], return_counts=True)
+    firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
+
+    ax = pl.subplot()
+    ax.plot(tsteps, rate)
+    ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
+    ax.set_title('Instantaneous and mean firing rate across all populations')
+    ax.set_xlabel('time (ms)')
+    ax.set_ylabel('firing rate (spikes / s)')
+    ax.set_xlim(0, sim_params['t_sim'])
+    ax.set_ylim(0, 50)
+    ax.legend()
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..713026c1524e5cf480e59fa42709eb551185089d
--- /dev/null
+++ b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
@@ -0,0 +1,375 @@
+import json
+import numpy as np
+import matplotlib.pyplot as pl
+import os
+
+from helpers import area_list, datapath
+from matplotlib import gridspec
+from matplotlib.colors import LogNorm
+from matplotlib.ticker import FixedLocator
+from matplotlib import rc_file
+from multiarea_model import MultiAreaModel
+from plotcolors import myblue
+from scipy import stats
+
+# rc_file('plotstyle.rc')
+
+def visualize_interareal_connectivity(M):
+    scale_down_to = 1
+    cc_weights_factor = 1.0
+    areas_simulated = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 
+                       'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 
+                       'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 
+                       'STPa', '46', 'AITd', 'TH']
+    replace_non_simulated_areas = 'het_poisson_stat'
+    
+    conn_params = {
+        'replace_non_simulated_areas': 'het_poisson_stat',
+        'g': -11.,
+        'K_stable': 'K_stable.npy',
+        'fac_nu_ext_TH': 1.2,
+        'fac_nu_ext_5E': 1.125,
+        'fac_nu_ext_6E': 1.41666667,
+        'av_indegree_V1': 3950.
+    }
+
+    input_params = {
+        'rate_ext': 10.
+    } 
+
+    neuron_params = {
+        'V0_mean': -150.,
+        'V0_sd': 50.}
+
+    network_params = {
+        'N_scaling': scale_down_to, # Scaling of population sizes, by default: 1.
+        'K_scaling': scale_down_to, # Scaling of indegrees, by default: 1.
+        'fullscale_rates': 'tests/fullscale_rates.json',
+        'input_params': input_params, # Input parameters
+        'connection_params': conn_params, # Connection parameters
+        'neuron_params': neuron_params # Neuron parameters
+    } 
+
+    sim_params = {
+        'areas_simulated': areas_simulated,
+        't_sim': 2000., # Simulated time (in ms), by default: 10.0
+        'num_processes': 1, # The number of MPI processes, by default: 1
+        'local_num_threads': 1, # The number of threads per MPI process, by default: 1
+        'recording_dict': {'record_vm': False},
+        'rng_seed': 1  # global random seed
+    }
+
+    theory_params = {
+        'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01
+    } 
+
+    M_full_scale = MultiAreaModel(network_params, 
+                                  simulation=True,
+                                  sim_spec=sim_params,
+                                  theory=True,
+                                  theory_spec=theory_params)
+    
+    """
+    Figure layout
+    """
+    nrows = 2
+    ncols = 2
+    width = 6.8556
+    panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2.  # golden ratio
+
+    height = width / panel_wh_ratio * float(nrows) / ncols
+    print(width, height)
+    pl.rcParams['figure.figsize'] = (width, height)
+
+    fig = pl.figure()
+    axes = {}
+
+    # gs1 = gridspec.GridSpec(2, 2)
+    gs1 = gridspec.GridSpec(1, 2)
+    gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.1, hspace=0.3)
+
+    # axes['A'] = pl.subplot(gs1[:1, :1])
+    # axes['B'] = pl.subplot(gs1[:1, 1:2])
+    axes['B'] = pl.subplot(gs1[:1, :1])
+    axes['D'] = pl.subplot(gs1[:1, 1:2])
+
+    # pos = axes['A'].get_position()
+    pos2 = axes['D'].get_position()
+    # axes['C'] = pl.axes([pos.x0 + 0.01, pos2.y0, pos.x1 - pos.x0 - 0.025, 0.23])
+
+    print(pos.x1 - pos.x0 - 0.025)
+
+    # labels = ['A', 'B', 'C', 'D']
+    labels = ['B', 'D']
+    for label in labels:
+        if label in ['C']:
+            label_pos = [-0.045, 1.18]
+        else:
+            label_pos = [-0.2, 1.04]
+        # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
+        #         fontdict={'fontsize': 10, 'weight': 'bold',
+        #                   'horizontalalignment': 'left', 'verticalalignment':
+        #                   'bottom'}, transform=axes[label].transAxes)
+        pl.text(label_pos[0], label_pos[1], label,
+                 fontdict={'fontsize': 10, 'weight': 'bold', 
+                           'horizontalalignment': 'left', 'verticalalignment': 
+                           'bottom'}, transform=axes[label].transAxes)
+
+    # """
+    # Load data
+    # """
+    # M = MultiAreaModel({})
+
+    # with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
+    #     proc = json.load(f)
+    # with open(os.path.join(datapath, 'viscortex_raw_data.json'), 'r') as f:
+    #     raw = json.load(f)
+
+    # FLN_Data_FV91 = proc['FLN_Data_FV91']
+
+    # cocomac_data = raw['cocomac_data']
+    # median_distance_data = raw['median_distance_data']
+
+    # cocomac = np.zeros((32, 32))
+    # conn_matrix = np.zeros((32, 32))
+    # for i, area1 in enumerate(area_list[::-1]):
+    #     for j, area2 in enumerate(area_list):
+    #         if M.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
+    #             cocomac[i][j] = 1.
+    #         if area2 in FLN_Data_FV91[area1]:
+    #             conn_matrix[i][j] = FLN_Data_FV91[area1][area2]
+
+    # """
+    # Panel A: CoCoMac Data
+    # """
+    # ax = axes['A']
+    # ax.yaxis.set_ticks_position("left")
+    # ax.xaxis.set_ticks_position("bottom")
+
+    # ax.set_aspect(1. / ax.get_data_ratio())
+    # ax.yaxis.set_ticks_position("none")
+    # ax.xaxis.set_ticks_position("none")
+
+    # masked_matrix = np.ma.masked_values(cocomac, 0.0)
+    # cmap = pl.cm.binary
+    # cmap.set_bad('w', 1.0)
+
+    # x = np.arange(0, len(area_list) + 1)
+    # y = np.arange(0, len(area_list[::-1]) + 1)
+    # X, Y = np.meshgrid(x, y)
+
+    # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_yticklabels(area_list[::-1], size=6.)
+
+    # ax.set_ylabel('Target area')
+    # ax.set_xlabel('Source area')
+
+    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
+    #                    edgecolors='None', vmin=0., vmax=1.)
+
+    # t = FixedLocator([])
+    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    # cbar.set_alpha(0.)
+    # cbar.remove()
+
+    # """
+    # Panel B: Data from Markov et al. (2014) "A weighted and directed
+    # interareal connectivity matrix for macaque cerebral cortex."
+    # Cerebral Cortex, 24(1), 17–36.
+    # """
+    # ax = axes['B']
+    # ax.set_aspect(1. / ax.get_data_ratio())
+    # ax.yaxis.set_ticks_position("none")
+    # ax.xaxis.set_ticks_position("none")
+
+    # masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
+    # cmap = pl.get_cmap('inferno')
+    # cmap.set_bad('w', 1.0)
+
+    # x = np.arange(0, len(area_list) + 1)
+    # y = np.arange(0, len(area_list[::-1]) + 1)
+    # X, Y = np.meshgrid(x, y)
+
+    # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_yticklabels(area_list[::-1], size=6.)
+
+    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
+    #                    edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+
+    # t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    # cbar.set_alpha(0.)
+
+    """
+    Panel B: Interareal connectivity of full-scaling multi-area model
+    """
+    conn_matrix_full_scale = np.zeros((32, 32))
+    for i, area1 in enumerate(area_list[::-1]):
+        for j, area2 in enumerate(area_list):
+            conn_matrix_full_scale[i][j] = M_full_scale.K_areas[area1][
+                area2] / np.sum(list(M_full_scale.K_areas[area1].values()))
+
+    ax = axes['D']
+    ax.yaxis.set_ticks_position("none")
+    ax.xaxis.set_ticks_position("none")
+
+    ax.set_aspect(1. / ax.get_data_ratio())
+
+    masked_matrix_full_scale = np.ma.masked_values(conn_matrix_full_scale, 0.0)
+    cmap = pl.get_cmap('inferno')
+    cmap.set_bad('w', 1.0)
+
+    x = np.arange(0, len(area_list) + 1)
+    y = np.arange(0, len(area_list[::-1]) + 1)
+    X, Y = np.meshgrid(x, y)
+
+    ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_yticklabels(area_list[::-1], size=6.)
+
+    ax.set_ylabel('Target area')
+    ax.set_xlabel('Source area')
+    im = ax.pcolormesh(masked_matrix_full_scale, cmap=cmap,
+                       edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+
+    t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+    cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    cbar.set_alpha(0.)
+
+    # """
+    # Panel C: Exponential decay of FLN with distance
+    # """
+    # FLN_values_FV91 = np.array([])
+    # distances_FV91 = np.array([])
+
+    # for target_area in FLN_Data_FV91:
+    #     for source_area in FLN_Data_FV91[target_area]:
+    #         if target_area in median_distance_data and source_area in median_distance_data:
+    #             if FLN_Data_FV91[target_area][source_area]:
+    #                 FLN_values_FV91 = np.append(FLN_values_FV91, FLN_Data_FV91[
+    #                                             target_area][source_area])
+    #                 distances_FV91 = np.append(distances_FV91, median_distance_data[
+    #                                            target_area][source_area])
+
+    # # Linear fit of distances vs. log FLN
+    # print("\n \n Linear fit to logarithmic values")
+    # gradient, intercept, r_value, p_value, std_err = stats.linregress(
+    #     distances_FV91, np.log(FLN_values_FV91))
+    # print("Raw parameters: ", gradient, intercept)
+    # print("Transformed parameters: ", -gradient, np.exp(intercept))
+    # print('r_value**2', r_value ** 2)
+    # print('p_value', p_value)
+    # print('std_err', std_err)
+
+    # ax = axes['C']
+    # ax.yaxis.set_ticks_position("left")
+    # ax.xaxis.set_ticks_position("bottom")
+
+    # ax.yaxis.set_ticks_position("left")
+    # ax.xaxis.set_ticks_position("bottom")
+
+    # ax.spines['right'].set_color('none')
+    # ax.spines['top'].set_color('none')
+    # ax.yaxis.set_ticks_position("left")
+    # ax.xaxis.set_ticks_position("bottom")
+
+    # ax.plot(distances_FV91, np.log10(FLN_values_FV91), '.', color=myblue)
+    # x = np.arange(np.min(distances_FV91), np.max(distances_FV91), 1)
+    # ax.plot(x, (intercept + gradient * x) / np.log(10), linewidth=2.0,
+    #         color='Black', label='Linear regression fit')
+
+    # ax.set_xlabel('Distance (mm)', labelpad=7)
+    # ax.set_ylabel(r'$\log(FLN)$')
+    # ax.set_yticks([-6, -4, -2, 0])
+
+    # print("log fit")
+    # print(np.corrcoef(gradient * distances_FV91 + intercept, np.log(FLN_values_FV91))[0][1])
+
+    # """
+    # Panel D: Resulting connectivity matrix
+    # """
+    # conn_matrix = np.zeros((32, 32))
+    # for i, area1 in enumerate(area_list[::-1]):
+    #     for j, area2 in enumerate(area_list):
+    #         conn_matrix[i][j] = M.K_areas[area1][
+    #             area2] / np.sum(list(M.K_areas[area1].values()))
+
+    # ax = axes['D']
+    # ax.yaxis.set_ticks_position("none")
+    # ax.xaxis.set_ticks_position("none")
+
+    # ax.set_aspect(1. / ax.get_data_ratio())
+
+    # masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
+    # cmap = pl.get_cmap('inferno')
+    # cmap.set_bad('w', 1.0)
+
+    # x = np.arange(0, len(area_list) + 1)
+    # y = np.arange(0, len(area_list[::-1]) + 1)
+    # X, Y = np.meshgrid(x, y)
+
+    # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    # ax.set_yticklabels(area_list[::-1], size=6.)
+
+    # ax.set_ylabel('Target area')
+    # ax.set_xlabel('Source area')
+    # im = ax.pcolormesh(masked_matrix, cmap=cmap,
+    #                    edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+
+    # t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+    # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    # cbar.set_alpha(0.)
+
+    """
+    Panel D: Interareal connectivity of down-scaling multi-area model
+    """
+    conn_matrix_down_scale = np.zeros((32, 32))
+    for i, area1 in enumerate(area_list[::-1]):
+        for j, area2 in enumerate(area_list):
+            conn_matrix_down_scale[i][j] = M.K_areas[area1][
+                area2] / np.sum(list(M.K_areas[area1].values()))
+
+    ax = axes['D']
+    ax.yaxis.set_ticks_position("none")
+    ax.xaxis.set_ticks_position("none")
+
+    ax.set_aspect(1. / ax.get_data_ratio())
+
+    masked_matrix_down_scale = np.ma.masked_values(conn_matrix_down_scale, 0.0)
+    cmap = pl.get_cmap('inferno')
+    cmap.set_bad('w', 1.0)
+
+    x = np.arange(0, len(area_list) + 1)
+    y = np.arange(0, len(area_list[::-1]) + 1)
+    X, Y = np.meshgrid(x, y)
+
+    ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_xticklabels(area_list, rotation=90, size=6.)
+
+    ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
+    ax.set_yticklabels(area_list[::-1], size=6.)
+
+    ax.set_ylabel('Target area')
+    ax.set_xlabel('Source area')
+    im = ax.pcolormesh(masked_matrix_down_scale, cmap=cmap,
+                       edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
+
+    t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
+    cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
+    cbar.set_alpha(0.)
+
+    # """
+    # Save figure
+    # """
+    # pl.savefig('Fig4_connectivity.eps')
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/MAM2EBRAINS_VISUALIZATION.py b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
similarity index 92%
rename from figures/MAM2EBRAINS/MAM2EBRAINS_VISUALIZATION.py
rename to figures/MAM2EBRAINS/M2E_visualize_resting_state.py
index 6d0f15f24488aed1aa22eb1cf9299efc80775d91..1b89896babc39f435a88db08beebf53adad27d9c 100644
--- a/figures/MAM2EBRAINS/MAM2EBRAINS_VISUALIZATION.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_resting_state.py
@@ -17,19 +17,6 @@ from matplotlib import gridspec
 icolor = myred
 ecolor = myblue
 
-
-# Instantaneous and mean firing rate across all populations
-def plot_instan_mean_firing_rate(tsteps, rate, sim_params):
-    ax = pl.subplot()
-    ax.plot(tsteps, rate)
-    ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
-    ax.set_title('Instantaneous and mean firing rate across all populations')
-    ax.set_xlabel('time (ms)')
-    ax.set_ylabel('firing rate (spikes / s)')
-    ax.set_xlim(0, sim_params['t_sim'])
-    ax.set_ylim(0, 50)
-    ax.legend()
-
 def set_boxplot_props(d):
     for i in range(len(d['boxes'])):
         if i % 2 == 0:
@@ -83,7 +70,6 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
 
     gs3 = gridspec.GridSpec(1, 1)
     gs3.update(left=0.1, right=0.95, top=0.3, bottom=0.075)
-    # gs3.update(left=0.1, right=0.95, top=0.25, bottom=0.075)
     axes['G'] = pl.subplot(gs3[:1, :1])
 
     areas = ['V1', 'V2', 'FEF']
@@ -96,8 +82,9 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
         #                   'horizontalalignment': 'left', 'verticalalignment':
         #                   'bottom'}, transform=axes[label].transAxes)
         pl.text(label_pos[0], label_pos[1], label + ': ' + area,
-                 fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left', 
-                           'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
+                 fontdict={'fontsize': 10, 'weight': 'bold', 
+                           'horizontalalignment': 'left', 'verticalalignment': 
+                           'bottom'}, transform=axes[label].transAxes)
 
     label = 'G'
     label_pos = [-0.1, 0.92]
@@ -106,8 +93,9 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
     #                   'horizontalalignment': 'left', 'verticalalignment':
     #                   'bottom'}, transform=axes[label].transAxes)
     pl.text(label_pos[0], label_pos[1], label,
-             fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left', 
-                       'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
+             fontdict={'fontsize': 10, 'weight': 'bold', 
+                       'horizontalalignment': 'left', 'verticalalignment': 
+                       'bottom'}, transform=axes[label].transAxes)
 
     labels = ['E', 'D', 'F']
     for label in labels:
@@ -117,8 +105,9 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
         #                   'horizontalalignment': 'left', 'verticalalignment':
         #                   'bottom'}, transform=axes[label].transAxes)
         pl.text(label_pos[0], label_pos[1], label,
-             fontdict={'fontsize': 10, 'weight': 'bold', 'horizontalalignment': 'left', 
-                       'verticalalignment': 'bottom'}, transform=axes[label].transAxes)
+             fontdict={'fontsize': 10, 'weight': 'bold', 
+                       'horizontalalignment': 'left', 'verticalalignment': 
+                       'bottom'}, transform=axes[label].transAxes)
         
 
     labels = ['A', 'B', 'C', 'D', 'E', 'F']
@@ -151,10 +140,6 @@ def plot_resting_state(M, A, label_spikes, data_path, sim_params):
 #         models = init_models('Fig5')
 #         label_spikes = models[0].simulation.label
 #         label = models[1].simulation.label
-        
-    # model = M
-    label_spikes = label_spikes
-    label = label_spikes
 
     # """
     # Create MultiAreaModel instance to have access to data structures
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index a4f6d98a417e5803b0c0475d95e711921e55820f..d98ab0e80d0fdfb06ab867bedd2fd5e03fb86b10 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -454,253 +454,58 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Indegrees\n",
-    "# Dictionary of nodes indegrees organized as:\n",
-    "# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: indegree_values}}}}\n",
-    "# M.K\n",
-    "# M.K_matrix.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "1011df6c-6aed-406c-884b-0aff4ba1bb81",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[2.17175900e+04 0.00000000e+00 8.83740443e+03 ... 8.89020759e-01\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [8.56308611e+02 4.23433098e+04 1.76754346e+01 ... 1.56944797e+01\n",
-      "  0.00000000e+00 3.17489369e+00]\n",
-      " [3.17601741e+03 0.00000000e+00 1.53208740e+04 ... 2.36046206e+00\n",
-      "  1.03692057e+02 1.24840032e+01]\n",
-      " ...\n",
-      " [1.42006560e+01 0.00000000e+00 5.62870089e+01 ... 4.80327591e+04\n",
-      "  7.05134206e+01 0.00000000e+00]\n",
-      " [7.59504010e+02 0.00000000e+00 1.71215227e-01 ... 8.18815976e+02\n",
-      "  3.37677709e+04 0.00000000e+00]\n",
-      " [5.25881493e+01 0.00000000e+00 2.14639899e+00 ... 1.52474284e+02\n",
-      "  3.97664965e+01 3.13537399e+04]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_dict = M.K\n",
-    "\n",
-    "areas_simulated = {'V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH'}\n",
-    "\n",
-    "# Create a mapping of area names to indices\n",
-    "area_to_index = {area: idx for idx, area in enumerate(areas_simulated)}\n",
-    "\n",
-    "# Create a zero-initialized 32x32 matrix\n",
-    "reduced_matrix = np.zeros((32, 32))\n",
-    "\n",
-    "# Iterate through the nested dictionary to populate the matrix\n",
-    "for source_area, source_pops in data_dict.items():\n",
-    "    for source_pop, target_areas in source_pops.items():\n",
-    "        for target_area, target_pops in target_areas.items():\n",
-    "            # Skip the external area\n",
-    "            if target_area == \"external\":\n",
-    "                continue\n",
-    "            indegree_sum = sum(target_pops.values())\n",
-    "            reduced_matrix[area_to_index[source_area], area_to_index[target_area]] += indegree_sum\n",
-    "\n",
-    "print(reduced_matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "80a14811-3d32-4bad-a120-20470afca5bd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1.00000000e+00 0.00000000e+00 4.06923808e-01 ... 4.09355162e-05\n",
-      "  0.00000000e+00 0.00000000e+00]\n",
-      " [2.02229966e-02 1.00000000e+00 4.17431577e-04 ... 3.70648392e-04\n",
-      "  0.00000000e+00 7.49798187e-05]\n",
-      " [2.07300015e-01 0.00000000e+00 1.00000000e+00 ... 1.54068369e-04\n",
-      "  6.76802496e-03 8.14836233e-04]\n",
-      " ...\n",
-      " [2.95645228e-04 0.00000000e+00 1.17184626e-03 ... 1.00000000e+00\n",
-      "  1.46802770e-03 0.00000000e+00]\n",
-      " [2.24919795e-02 0.00000000e+00 5.07037399e-06 ... 2.42484462e-02\n",
-      "  1.00000000e+00 0.00000000e+00]\n",
-      " [1.67725284e-03 0.00000000e+00 6.84575109e-05 ... 4.86303340e-03\n",
-      "  1.26831749e-03 1.00000000e+00]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pylab as plt\n",
-    "from matplotlib.colors import LogNorm\n",
-    "\n",
-    "# Get the largest value in each row\n",
-    "row_maxes = np.max(reduced_matrix, axis=1)\n",
-    "\n",
-    "# # To prevent division by zero, replace zero maxes with 1\n",
-    "# row_maxes[row_maxes == 0] = 1\n",
-    "\n",
-    "# Normalize\n",
-    "normalized_matrix = reduced_matrix / row_maxes[:, np.newaxis]\n",
-    "\n",
-    "print(normalized_matrix)\n",
-    "\n",
-    "# uniform_data = reduced_matrix/reduced_matrix.max()\n",
-    "plt.figure(figsize = (10, 10))\n",
-    "ax = sns.heatmap(normalized_matrix, linewidth=1, norm=LogNorm(vmin=1e-6, vmax=1.))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "70d123d8-6625-483c-a2ea-b62d7ac6c0c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # Step 1: Map areas to indices\n",
-    "# data_dict = M.K\n",
-    "\n",
-    "# all_areas = set(data_dict.keys())\n",
-    "# for _, source_data in data_dict.items():\n",
-    "#     for target_area in source_data.keys():\n",
-    "#         all_areas.add(target_area)\n",
+    "# Neuron numbers\n",
     "\n",
-    "# area_to_index = {area: idx for idx, area in enumerate(all_areas)}\n",
+    "# Dictionary of neuron numbers\n",
+    "# M.N\n",
     "\n",
-    "# # Initialize the 32x32 matrix with zeros\n",
-    "# reduced_matrix = np.zeros((32, 32))\n",
-    "\n",
-    "# for source_area, target_data in data_dict.items():\n",
-    "#     for source_pop, target_pops in target_data.items():\n",
-    "#         for target_area, indegree in target_pops.items():\n",
-    "#             try:\n",
-    "#                 reduced_matrix[area_to_index[source_area], area_to_index[target_area]] += indegree\n",
-    "#             except IndexError:\n",
-    "#                 print(f\"Error with source_area: {source_area}, target_area: {target_area}\")\n",
-    "#                 print(\"area_to_index:\", area_to_index)\n",
-    "\n",
-    "# print(reduced_matrix)"
+    "# Array of neuron numbers\n",
+    "# M.N_vec"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b30233b0-e6a5-4644-8cbc-153a517c6052",
+   "id": "8408d463-557b-481b-afc1-5fbbbd67306d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# # Assuming you have a connectivity_matrix of shape (256, 256)\n",
-    "# reduced_matrix = np.zeros((32, 32))\n",
-    "\n",
-    "# for i in range(32):\n",
-    "#     for j in range(32):\n",
-    "#         reduced_matrix[i, j] = connectivity_matrix[i*8 : (i+1)*8, j*8 : (j+1)*8].sum()\n",
+    "# Indegrees\n",
     "\n",
-    "# print(reduced_matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "fa16168a-bfd6-41a0-a7a3-9c115aec306c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x1152 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pylab as plt\n",
+    "# Dictionary of nodes indegrees organized as:\n",
+    "# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: indegree_values}}}}\n",
+    "# M.K\n",
     "\n",
-    "uniform_data = M.K_matrix\n",
-    "plt.figure(figsize = (16, 16))\n",
-    "ax = sns.heatmap(uniform_data, linewidth=1)\n",
-    "plt.show()"
+    "# Array of nodes indegrees\n",
+    "# M.K_matrix.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "445a722a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(254, 255)"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Synapses\n",
+    "\n",
     "# Dictionary of synapses that target neurons receive, it is organized as:\n",
     "# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: number_of_synapses}}}}\n",
     "# M.synapses\n",
+    "\n",
+    "# Array of \n",
     "# M.syn_matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "90ade45a-f8ef-45aa-9152-a0a7b09253d5",
+   "execution_count": null,
+   "id": "05512922-26e5-425f-90a4-0df7c2279ccf",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x1152 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pylab as plt\n",
-    "\n",
-    "uniform_data = M.syn_matrix\n",
-    "plt.figure(figsize = (16, 16))\n",
-    "ax = sns.heatmap(uniform_data, linewidth=0.5)\n",
-    "plt.show()"
+    "from M2E_visualize_interareal_connectivity import visualize_interareal_connectivity\n",
+    "visualize_interareal_connectivity(M)"
    ]
   },
   {
@@ -914,8 +719,8 @@
     }
    ],
    "source": [
-    "from MAM2EBRAINS_VISUALIZATION import plot_instan_mean_firing_rate\n",
-    "plot_instan_mean_firing_rate(tsteps, firing_rate, sim_params)"
+    "from M2E_VISUALIZATION import plot_instan_mean_firing_rate\n",
+    "plot_instan_mean_firing_rate(M)"
    ]
   },
   {
@@ -1046,9 +851,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "EBRAINS-23.02",
+   "display_name": "EBRAINS-23.06",
    "language": "python",
-   "name": "ebrains-23.02"
+   "name": "ebrains-23.06"
   },
   "language_info": {
    "codemirror_mode": {