diff --git a/README.md b/README.md
index 9c82bec131843f334d01b194a2003da007826087..b8cb421ba43b447ae1fef2a2ff40797fc46c8265 100644
--- a/README.md
+++ b/README.md
@@ -227,6 +227,17 @@ The SLN fit in `multiarea_model/data_multiarea/VisualCortex_Data.py` and `figure
The calculation of BOLD signals from the simulated firing rates for Fig. 8 of [3] requires an installation of R and the R library `neuRosim` (<https://cran.r-project.org/web/packages/neuRosim/index.html>).
+## Testing on EBRAINS
+
+The Jupyter Notebook `multi-area-model.ipynb` illustrates the simulation workflow with a down-scaled version of the multi-area model. This notebook can be explored and executed online in the Jupyter Lab provided by EBRAINS without the need to install any software yourself.
+
+1. Create a fork of this repository. Copy the address of your fork by clicking on `Code`, `HTTPS`, and then the copy icon.
+2. Go to https://lab.de.ebrains.eu, log in, and select a computing center from the given list.
+3. In the Jupyter Lab, click on the `Git Clone` icon and paste the address of your fork.
+4. Enter the folder `multi-area-model` with the cloned repository and open the notebook `multi-area-model.ipynb`. Make sure that the kernel (top right) is set to `EBRAINS_experimental_release`.
+5. Run the notebook!
+6. You can also modify the code, do git commits, and push your changes to your own fork.
+
## Contributors
All authors of the publications [1-3] made contributions to the
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..765111899f6ff1ab91d8a16bcea30fd4976735d1
--- /dev/null
+++ b/multi-area-model.ipynb
@@ -0,0 +1,260 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b1331599",
+ "metadata": {},
+ "source": [
+ "# Down-scaled multi-area model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "96517739",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e07b0d0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install nested_dict dicthash"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c6b6a3c",
+ "metadata": {},
+ "source": [
+ "Create config file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "72c170e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('config.py', 'w') as fp:\n",
+ " fp.write(\n",
+ "'''import os\n",
+ "base_path = os.path.abspath(\".\")\n",
+ "data_path = os.path.abspath(\"simulations\")\n",
+ "jobscript_template = \"python {base_path}/run_simulation.py {label}\"\n",
+ "submit_cmd = \"bash -c\"\n",
+ "''')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2784f76b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from multiarea_model import MultiAreaModel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2cedd26b",
+ "metadata": {},
+ "source": [
+ "Neurons and indegrees are both scaled down to 0.5%.\n",
+ "Can usually be simulated on a local machine.\n",
+ "\n",
+ "**Warning: This will not yield reasonable dynamical results from the\n",
+ "network and is only meant to demonstrate the simulation workflow.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e940bb6b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scale_down_to = 0.005"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d53f1eab",
+ "metadata": {},
+ "source": [
+ "Specify model and simulation parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7af3a191",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "conn_params = {'replace_non_simulated_areas': 'het_poisson_stat',\n",
+ " 'g': -11.,\n",
+ " \n",
+ " 'K_stable': 'K_stable.npy',\n",
+ " 'fac_nu_ext_TH': 1.2,\n",
+ " 'fac_nu_ext_5E': 1.125,\n",
+ " 'fac_nu_ext_6E': 1.41666667,\n",
+ " 'av_indegree_V1': 3950.}\n",
+ "input_params = {'rate_ext': 10.}\n",
+ "neuron_params = {'V0_mean': -150.,\n",
+ " 'V0_sd': 50.}\n",
+ "network_params = {'N_scaling': scale_down_to,\n",
+ " 'K_scaling': scale_down_to,\n",
+ " 'fullscale_rates': 'tests/fullscale_rates.json',\n",
+ " 'input_params': input_params,\n",
+ " 'connection_params': conn_params,\n",
+ " 'neuron_params': neuron_params}\n",
+ "\n",
+ "sim_params = {'t_sim': 2000.,\n",
+ " 'num_processes': 1,\n",
+ " 'local_num_threads': 1,\n",
+ " 'recording_dict': {'record_vm': False}}\n",
+ "\n",
+ "theory_params = {'dt': 0.1}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "de4a6703",
+ "metadata": {},
+ "source": [
+ "Instantiate a multi-area model, predict firing rates from theroy, and run the simulation. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d409be95",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "M = MultiAreaModel(network_params, simulation=True,\n",
+ " sim_spec=sim_params,\n",
+ " theory=True,\n",
+ " theory_spec=theory_params)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "918d907f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "p, r = M.theory.integrate_siegert()\n",
+ "print(\"Mean-field theory predicts an average \"\n",
+ " \"rate of {0:.3f} spikes/s across all populations.\".format(np.mean(r[:, -1])))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15778e9c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "M.simulation.simulate()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8726a93d",
+ "metadata": {},
+ "source": [
+ "Load spike data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cb8e3edd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + \"-spikes-1-0.dat\", skiprows=3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8793e033",
+ "metadata": {},
+ "source": [
+ "Compute instantaneous rate per neuron across all populations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9590223b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tsteps, spikecount = np.unique(data[:,1], return_counts=True)\n",
+ "rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "38ddd973",
+ "metadata": {},
+ "source": [
+ "Plot instantaneous and mean rate."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bea30fc8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(tsteps, rate)\n",
+ "ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')\n",
+ "ax.set_title('instantaneous rate across all populations')\n",
+ "ax.set_xlabel('time (ms)')\n",
+ "ax.set_ylabel('rate (spikes / s)')\n",
+ "ax.set_xlim(0, sim_params['t_sim'])\n",
+ "ax.set_ylim(0, 50)\n",
+ "ax.legend()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/multiarea_model/data_multiarea/VisualCortex_Data.py b/multiarea_model/data_multiarea/VisualCortex_Data.py
index bc84ee5c63c2740dda3e77f87f08f5baa4a4dcab..d5c866ea52bcd1588bec24a6a959ccad0891114e 100644
--- a/multiarea_model/data_multiarea/VisualCortex_Data.py
+++ b/multiarea_model/data_multiarea/VisualCortex_Data.py
@@ -1325,8 +1325,8 @@ def process_raw_data():
out = proc.communicate()[0].decode('utf-8')
R_fit = [float(out.split('\n')[1].split(' ')[1]),
float(out.split('\n')[1].split(' ')[3])]
- except OSError:
- print("No R installation, taking hard-coded SLN fit parameters.")
+ except (OSError, IndexError):
+ print("No R installation or IndexError, taking hard-coded SLN fit parameters.")
R_fit = [-0.1516142, -1.5343200]
"""