diff --git a/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py b/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py
index e572cc058baecdd7d26b4f976b18e8c5b14be642..7cb4f445c2cfad184f37976bbf786e426dad8c32 100644
--- a/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py
+++ b/figures/.ipynb_checkpoints/MAM2EBRAINS-checkpoint.py
@@ -2,8 +2,8 @@ import json
 import numpy as np
 import os
 
-# import sys
-# sys.path.append('./figures/Schmidt2018_dyn')
+import sys
+sys.path.append('/figures/Schmidt2018_dyn')
 
 from helpers import original_data_path, population_labels
 from multiarea_model import MultiAreaModel
diff --git a/figures/MAM2EBRAINS.py b/figures/MAM2EBRAINS.py
index e572cc058baecdd7d26b4f976b18e8c5b14be642..7cb4f445c2cfad184f37976bbf786e426dad8c32 100644
--- a/figures/MAM2EBRAINS.py
+++ b/figures/MAM2EBRAINS.py
@@ -2,8 +2,8 @@ import json
 import numpy as np
 import os
 
-# import sys
-# sys.path.append('./figures/Schmidt2018_dyn')
+import sys
+sys.path.append('/figures/Schmidt2018_dyn')
 
 from helpers import original_data_path, population_labels
 from multiarea_model import MultiAreaModel
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 84f79c3ef4f3e9a28f2cb4e506e17723539a4954..6eb573725f088bf4e72a13a48e238b8cf6434a15 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -378,7 +378,7 @@
      "output_type": "stream",
      "text": [
       "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 7883\n"
+      "RAND_DATA_LABEL 9176\n"
      ]
     },
     {
@@ -554,11 +554,11 @@
      "text": [
       "Prepared simulation in 0.00 seconds.\n",
       "Rank 0: created area V1 with 0 local nodes\n",
-      "Memory after V1 : 1911.93 MB\n",
+      "Memory after V1 : 1911.95 MB\n",
       "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1938.63 MB\n",
+      "Memory after V2 : 1938.56 MB\n",
       "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1967.82 MB\n",
+      "Memory after VP : 1967.79 MB\n",
       "Rank 0: created area V3 with 0 local nodes\n",
       "Memory after V3 : 1996.08 MB\n",
       "Rank 0: created area V3A with 0 local nodes\n",
@@ -566,60 +566,60 @@
       "Rank 0: created area MT with 0 local nodes\n",
       "Memory after MT : 2041.53 MB\n",
       "Rank 0: created area V4t with 0 local nodes\n",
-      "Memory after V4t : 2066.48 MB\n",
+      "Memory after V4t : 2066.51 MB\n",
       "Rank 0: created area V4 with 0 local nodes\n",
-      "Memory after V4 : 2093.50 MB\n",
+      "Memory after V4 : 2093.58 MB\n",
       "Rank 0: created area VOT with 0 local nodes\n",
-      "Memory after VOT : 2118.81 MB\n",
+      "Memory after VOT : 2118.78 MB\n",
       "Rank 0: created area MSTd with 0 local nodes\n",
-      "Memory after MSTd : 2140.28 MB\n",
+      "Memory after MSTd : 2140.24 MB\n",
       "Rank 0: created area PIP with 0 local nodes\n",
-      "Memory after PIP : 2161.59 MB\n",
+      "Memory after PIP : 2161.72 MB\n",
       "Rank 0: created area PO with 0 local nodes\n",
       "Memory after PO : 2183.10 MB\n",
       "Rank 0: created area DP with 0 local nodes\n",
-      "Memory after DP : 2203.33 MB\n",
+      "Memory after DP : 2203.37 MB\n",
       "Rank 0: created area MIP with 0 local nodes\n",
-      "Memory after MIP : 2224.90 MB\n",
+      "Memory after MIP : 2224.99 MB\n",
       "Rank 0: created area MDP with 0 local nodes\n",
-      "Memory after MDP : 2246.41 MB\n",
+      "Memory after MDP : 2246.30 MB\n",
       "Rank 0: created area VIP with 0 local nodes\n",
-      "Memory after VIP : 2268.35 MB\n",
+      "Memory after VIP : 2268.24 MB\n",
       "Rank 0: created area LIP with 0 local nodes\n",
       "Memory after LIP : 2292.30 MB\n",
       "Rank 0: created area PITv with 0 local nodes\n",
-      "Memory after PITv : 2317.64 MB\n",
+      "Memory after PITv : 2317.61 MB\n",
       "Rank 0: created area PITd with 0 local nodes\n",
       "Memory after PITd : 2342.85 MB\n",
       "Rank 0: created area MSTl with 0 local nodes\n",
-      "Memory after MSTl : 2364.19 MB\n",
+      "Memory after MSTl : 2364.20 MB\n",
       "Rank 0: created area CITv with 0 local nodes\n",
-      "Memory after CITv : 2383.38 MB\n",
+      "Memory after CITv : 2383.50 MB\n",
       "Rank 0: created area CITd with 0 local nodes\n",
-      "Memory after CITd : 2402.71 MB\n",
+      "Memory after CITd : 2402.83 MB\n",
       "Rank 0: created area FEF with 0 local nodes\n",
       "Memory after FEF : 2424.18 MB\n",
       "Rank 0: created area TF with 0 local nodes\n",
       "Memory after TF : 2439.82 MB\n",
       "Rank 0: created area AITv with 0 local nodes\n",
-      "Memory after AITv : 2462.53 MB\n",
+      "Memory after AITv : 2462.54 MB\n",
       "Rank 0: created area FST with 0 local nodes\n",
-      "Memory after FST : 2479.30 MB\n",
+      "Memory after FST : 2479.27 MB\n",
       "Rank 0: created area 7a with 0 local nodes\n",
-      "Memory after 7a : 2500.48 MB\n",
+      "Memory after 7a : 2500.44 MB\n",
       "Rank 0: created area STPp with 0 local nodes\n",
-      "Memory after STPp : 2519.20 MB\n",
+      "Memory after STPp : 2519.16 MB\n",
       "Rank 0: created area STPa with 0 local nodes\n",
-      "Memory after STPa : 2538.30 MB\n",
+      "Memory after STPa : 2538.26 MB\n",
       "Rank 0: created area 46 with 0 local nodes\n",
-      "Memory after 46 : 2553.66 MB\n",
+      "Memory after 46 : 2553.71 MB\n",
       "Rank 0: created area AITd with 0 local nodes\n",
-      "Memory after AITd : 2576.39 MB\n",
+      "Memory after AITd : 2576.31 MB\n",
       "Rank 0: created area TH with 0 local nodes\n",
-      "Memory after TH : 2589.01 MB\n",
-      "Created areas and internal connections in 2.28 seconds.\n",
-      "Created cortico-cortical connections in 22.84 seconds.\n",
-      "Simulated network in 62.36 seconds.\n"
+      "Memory after TH : 2588.98 MB\n",
+      "Created areas and internal connections in 2.24 seconds.\n",
+      "Created cortico-cortical connections in 22.49 seconds.\n",
+      "Simulated network in 82.92 seconds.\n"
      ]
     }
    ],
@@ -764,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "f5b58845-4d1a-430f-83f4-402fdf918aef",
    "metadata": {
     "tags": []
@@ -777,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "6607a73d-1c74-4848-9603-081ad0e7cae8",
    "metadata": {
     "tags": []
@@ -821,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "1870cf34-ee62-4614-bc25-c36bc9a7377c",
    "metadata": {
     "tags": []
@@ -868,7 +868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "50b7df89",
    "metadata": {
     "tags": []
@@ -915,7 +915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "d43b493c",
    "metadata": {
     "tags": []
@@ -957,7 +957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "401ece2d-47c8-4775-80ae-92a8e432520c",
    "metadata": {
     "tags": []
@@ -1012,7 +1012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "fa3ea20e-e456-4608-a711-e2c320bcaf91",
    "metadata": {},
    "outputs": [
@@ -1068,21 +1068,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "id": "bea30fc8",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'helpers'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [22]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m rate \u001b[38;5;241m=\u001b[39m spikecount \u001b[38;5;241m/\u001b[39m M\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39mparams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdt\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1e3\u001b[39m \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39msum(M\u001b[38;5;241m.\u001b[39mN_vec)\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# ax = plt.subplot()\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# ax.plot(tsteps, rate)\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# ax.set_ylim(0, 50)\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# ax.legend()\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mMAM2EBRAINS\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_instan_mean_firing_rate\n\u001b[1;32m     17\u001b[0m plot_instan_mean_firing_rate(tsteps, rate, sim_params)\n",
+      "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS.py:8\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# import sys\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# sys.path.append('./figures/Schmidt2018_dyn')\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhelpers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m original_data_path, population_labels\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmultiarea_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MultiAreaModel\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mplotcolors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m myred, myblue\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'helpers'"
+     ]
     }
    ],
    "source": [
@@ -1123,83 +1123,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "ae19bcc3",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 6230\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3440: RuntimeWarning:Mean of empty slice.\n",
-      "/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/core/_methods.py:189: RuntimeWarning:invalid value encountered in double_scalars\n",
-      "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",
-      "{'K_scaling': 1.0,\n",
-      " 'N_scaling': 1.0,\n",
-      " 'connection_params': {'K_stable': '../SchueckerSchmidt2017/K_prime_original.npy',\n",
-      "                       'av_indegree_V1': 3950.0,\n",
-      "                       'cc_weights_I_factor': 2.0,\n",
-      "                       'cc_weights_factor': 1.9,\n",
-      "                       'fac_nu_ext_5E': 1.125,\n",
-      "                       'fac_nu_ext_6E': 1.41666667,\n",
-      "                       'fac_nu_ext_TH': 1.2,\n",
-      "                       'g': -11.0},\n",
-      " 'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: '../SchueckerSchmidt2017/K_prime_original.npy'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [27]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mMAM2EBRAINS\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_resting_state\n\u001b[0;32m----> 2\u001b[0m \u001b[43mplot_resting_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_spikes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS.py:156\u001b[0m, in \u001b[0;36mplot_resting_state\u001b[0;34m(A, label_spikes, label)\u001b[0m\n\u001b[1;32m    154\u001b[0m     data_path \u001b[38;5;241m=\u001b[39m original_data_path\n\u001b[1;32m    155\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 156\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnetwork_simulations\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m init_models\n\u001b[1;32m    157\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data_path\n\u001b[1;32m    158\u001b[0m     models \u001b[38;5;241m=\u001b[39m init_models(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFig5\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/./figures/Schmidt2018_dyn/network_simulations.py:180\u001b[0m, in \u001b[0;36minit_models\u001b[0;34m(figure)\u001b[0m\n\u001b[1;32m    178\u001b[0m models \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    179\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m par \u001b[38;5;129;01min\u001b[39;00m NEW_SIM_PARAMS[figure]:\n\u001b[0;32m--> 180\u001b[0m     M \u001b[38;5;241m=\u001b[39m \u001b[43minit_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    181\u001b[0m     models\u001b[38;5;241m.\u001b[39mappend(M)\n\u001b[1;32m    182\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m models\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/./figures/Schmidt2018_dyn/network_simulations.py:169\u001b[0m, in \u001b[0;36minit_model\u001b[0;34m(par)\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minit_model\u001b[39m(par):\n\u001b[0;32m--> 169\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mMultiAreaModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpar\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    170\u001b[0m \u001b[43m                          \u001b[49m\u001b[43msimulation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[43m                          \u001b[49m\u001b[43msim_spec\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpar\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/multiarea_model/multiarea_model.py:143\u001b[0m, in \u001b[0;36mMultiAreaModel.__init__\u001b[0;34m(self, network_spec, theory, simulation, analysis, *args, **keywords)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot supported. Please store the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    140\u001b[0m                     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatrix in a binary numpy file and define \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    141\u001b[0m                     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe path to the file as the parameter value.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    142\u001b[0m \u001b[38;5;66;03m# Assume that the parameter defines a filename containing the matrix\u001b[39;00m\n\u001b[0;32m--> 143\u001b[0m K_stable \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mconnection_params\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mK_stable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    144\u001b[0m ext \u001b[38;5;241m=\u001b[39m {area: {pop: ind[area][pop][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexternal\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m pop \u001b[38;5;129;01min\u001b[39;00m\n\u001b[1;32m    145\u001b[0m               \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstructure[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mV1\u001b[39m\u001b[38;5;124m'\u001b[39m]} \u001b[38;5;28;01mfor\u001b[39;00m area \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marea_list}\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mK \u001b[38;5;241m=\u001b[39m matrix_to_dict(\n\u001b[1;32m    147\u001b[0m     K_stable, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39marea_list, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstructure, external\u001b[38;5;241m=\u001b[39mext)\n",
-      "File \u001b[0;32m/srv/main-spack-instance-2302/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-numpy-1.21.6-6fewtq7oarp3vtwlxrrcofz5sxwt55s7/lib/python3.8/site-packages/numpy/lib/npyio.py:417\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    415\u001b[0m     own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 417\u001b[0m     fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos_fspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    418\u001b[0m     own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    420\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../SchueckerSchmidt2017/K_prime_original.npy'"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 510.235x450.49 with 7 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from MAM2EBRAINS import plot_resting_state\n",
     "plot_resting_state(A, label_spikes, label)"
@@ -1218,47 +1147,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "1da18fee",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 510.235x450.49 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 510.235x450.49 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 510.235x450.49 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# from MAM2EBRAINS import plot_raster_plot\n",
     "# plot_raster_plot(A)"
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index 53c7f0127b8791771595fb8691b6886247c0a255..6e44202f6fcd4cc078a03c1ba5419fbe91aecd79 100644
--- a/multiarea_model/data_multiarea/viscortex_processed_data.json
+++ b/multiarea_model/data_multiarea/viscortex_processed_data.json
@@ -1 +1 @@
-{"cocomac_completed": {"V1": {"7a": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "IT": {"source_pattern": ["0", "0", 2, "0", "0", "3"], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "LIP": {"source_pattern": ["0", "0", "0", "0", "0", "2"], "target_pattern": null}, "MT": {"source_pattern": ["0", "0", 2, "0", 2, 2], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "PIP": {"source_pattern": null, "target_pattern": null}, "PO": {"source_pattern": null, "target_pattern": null}, "STP": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "STPp": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "TF": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "TH": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": null}, "V1": {"source_pattern": null, "target_pattern": [2.0, 2.0, 2.0, "0", "3", "3"]}, "V2": {"source_pattern": ["0", 2, 2, "0", "3", 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