diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 92ec9be9aa6e847ace8c2b222bc306c8caefe207..af6ffc99a2b4ea2faf7aa705980d304e2e19a368 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -10,96 +10,92 @@
},
{
"cell_type": "markdown",
- "id": "3e163b35-5330-48ca-8c7e-9e0a884520ca",
+ "id": "d782e527",
"metadata": {},
"source": [
- "## Import dependencies"
+ "### Create config file"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "96517739",
+ "id": "9b985084",
"metadata": {},
"outputs": [],
"source": [
- "%matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import os"
+ "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": "markdown",
+ "id": "4a853b97",
+ "metadata": {},
+ "source": [
+ "### Import dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "7e07b0d0",
+ "id": "96517739",
"metadata": {},
"outputs": [],
"source": [
- "!pip install nested_dict dicthash"
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import os\n",
+ "import nest\n",
+ "\n",
+ "from multiarea_model import MultiAreaModel\n",
+ "from config import base_path"
]
},
{
"cell_type": "code",
- "execution_count": 7,
- "id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
+ "execution_count": null,
+ "id": "7e07b0d0",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "table {float:left}\n",
- "</style>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "%%html\n",
- "<style>\n",
- "table {float:left}\n",
- "</style>"
+ "!pip install nested_dict dicthash"
]
},
{
"cell_type": "markdown",
- "id": "98555af1-a5c5-4db6-85dc-6d07e40f38cc",
+ "id": "2f429063",
"metadata": {},
"source": [
- "## Create config file"
+ "### Jupyter notebook display format setting"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "72c170e4",
+ "id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
"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",
- "''')"
+ "# specify the format the table in output\n",
+ "%%html\n",
+ "<style>\n",
+ "table {float:left}\n",
+ "</style>"
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "id": "2784f76b",
+ "cell_type": "markdown",
+ "id": "89d4866d",
"metadata": {},
- "outputs": [],
"source": [
- "from multiarea_model import MultiAreaModel"
+ "<br>"
]
},
{
@@ -119,9 +115,19 @@
"tags": []
},
"source": [
- "### 1. Scaling parameter\n",
- "**Scaling parameter** (scale_down_to) is the parameter defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained in relative shorter period of time.<br> <br> \n",
- "Neurons and indegrees are both scaled down to 0.5%, where the model can usually be simulated on a local machine.<br> **Warning: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow.**"
+ "### 1. Scaling factor (scale_down_to)\n",
+ "**Scaling factor** (scale_down_to) is the parameter which defines the the ratio of the full scale multi-area model being down-scaled to a model with fewer neurons and indegrees so as to be simulated on machines with lower computational ability and the simulation results can be obtained within relative shorter period of time.<br> <br> \n",
+ "Neurons and indegrees are both scaled down to 0.5%, where the model can usually be simulated on a local machine.<br> **Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f66dbf02",
+ "metadata": {},
+ "source": [
+ "|Parameter |Parameter description |Variable |Value |Value description |\n",
+ "|:-------------:|:----------------------|:---------------------------:|:------------------:|:------------------|\n",
+ "|Scaling factor | | scale_down_to |0.005 | |"
]
},
{
@@ -131,7 +137,7 @@
"metadata": {},
"outputs": [],
"source": [
- "scale_down_to = 0.005"
+ "scale_down_to = 0.005 # Change it to 1. for running the fullscale network"
]
},
{
@@ -175,7 +181,6 @@
"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",
@@ -254,14 +259,14 @@
"id": "3e437aca-1fd0-4296-a0f6-a346b1319903",
"metadata": {},
"source": [
- "| Parameter | Parameter description | Variable | Value | Value description |\n",
- "|:---------:|:----------------------|:---------------------:|:-----------------------------:|:------------------|\n",
- "| | | 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 | |"
+ "| Parameter | Parameter description | Variable | Value | Value description |\n",
+ "|:---------------------------------------:|:----------------------|:---------------------:|:-----------------------------:|:------------------|\n",
+ "| Scaling factor of the number of neurons | | N_scaling | scale_down_to | |\n",
+ "| Scaling factor of the number of synapses| | K_scaling | scale_down_to | |\n",
+ "| Fullscale rates | | fullscale_rates | 'tests/fullscale_rates.json' | |\n",
+ "| Input parameters | | input_params | input_params | |\n",
+ "| Connections parameters | | connection_params | conn_params | |\n",
+ "| Neuron parameters | | neuron_params | neuron_params | |"
]
},
{
@@ -284,7 +289,7 @@
"id": "a0730f70-ed9b-4664-b677-3dda965a01ef",
"metadata": {},
"source": [
- "### 3. Simulation paramters (sim_params)"
+ "### 2.5 Simulation paramters (sim_params)"
]
},
{
@@ -292,13 +297,13 @@
"id": "c989ee79-fa3d-405c-a1bf-da8ec884d878",
"metadata": {},
"source": [
- "| Parameter | Parameter description | Variable | Value | Value description |\n",
- "|:---------:|:----------------------|:--------------------:|:------------------:|:------------------|\n",
- "| | |t_sim |2000. | |\n",
- "| | |num_processes |1 | |\n",
- "| | |local_num_threads |1 | |\n",
- "| | |recording_dict |input_params | |\n",
- "| | |record_vm |False | |"
+ "| Parameter | Parameter description | Variable | Value | Value description |\n",
+ "|:---------------------:|:----------------------|:--------------------:|:------------------:|:------------------|\n",
+ "|Simulation time | |t_sim |2000. | |\n",
+ "|Number of processes | |num_processes |1 | |\n",
+ "|Number of local threads| |local_num_threads |1 | |\n",
+ "| | |recording_dict |input_params | |\n",
+ "| | |record_vm |False | |"
]
},
{
@@ -311,7 +316,8 @@
"sim_params = {'t_sim': 2000.,\n",
" 'num_processes': 1,\n",
" 'local_num_threads': 1,\n",
- " 'recording_dict': {'record_vm': False}}"
+ " 'recording_dict': {'record_vm': False},\n",
+ " 'rng_seed': 1} # global random seed\n"
]
},
{
@@ -319,7 +325,7 @@
"id": "79596d77-c105-45d0-9a57-2d15e31f1189",
"metadata": {},
"source": [
- "### 4. Theory paramters (theory_params)"
+ "### 2.6. Theory paramters (theory_params)"
]
},
{
@@ -342,18 +348,34 @@
"theory_params = {'dt': 0.1}"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "1472e9c5",
+ "metadata": {},
+ "source": [
+ "<br>"
+ ]
+ },
{
"cell_type": "markdown",
"id": "de4a6703",
"metadata": {},
"source": [
- "## Instantiate a multi-area model, predict firing rates from theroy "
+ "## Instantiate a multi-area model and analyse"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1fd58841",
+ "metadata": {},
+ "source": [
+ "### 1. Insantiate a multi-area model "
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "d409be95",
+ "id": "ab25f9f8",
"metadata": {},
"outputs": [],
"source": [
@@ -363,10 +385,18 @@
" theory_spec=theory_params)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "91649c30",
+ "metadata": {},
+ "source": [
+ "### 2. Predict firing rates from theory"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "918d907f",
+ "id": "6a7ddf0e",
"metadata": {},
"outputs": [],
"source": [
@@ -375,6 +405,70 @@
" \"rate of {0:.3f} spikes/s across all populations.\".format(np.mean(r[:, -1])))"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "2062ddf3",
+ "metadata": {},
+ "source": [
+ "### 3. Extract connectivity"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8a7c09e0",
+ "metadata": {},
+ "source": [
+ "The connectivity and neuron numbers are stored in the attributes of the model class. Neuron numbers are stored in `M.N` as a dictionary (and in `M.N_vec` as an array), indegrees in `M.K` as a dictionary (and in `M.K_matrix` as an array). Number of synapses can also be access via `M.synapses` (and in `M.syn_matrix` as an array)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b7396606",
+ "metadata": {},
+ "source": [
+ "#### 3.1 Node indegrees"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6316ac24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Dictionary of nodes indegrees organized as:\n",
+ "# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: indegree_values}}}}\n",
+ "M.K"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "253a2aba",
+ "metadata": {},
+ "source": [
+ "#### 3.2 Synapses"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "445a722a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6c01820e",
+ "metadata": {},
+ "source": [
+ "<br>"
+ ]
+ },
{
"cell_type": "markdown",
"id": "0c1cad59-81d0-4e24-ac33-13c4ca8c6dec",
@@ -390,15 +484,122 @@
"metadata": {},
"outputs": [],
"source": [
+ "# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.\n",
"M.simulation.simulate()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "fd6e3232",
+ "metadata": {},
+ "source": [
+ "<br>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "28e071f8",
+ "metadata": {},
+ "source": [
+ "## Simulation results analysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "89c7b7cf",
+ "metadata": {},
+ "source": [
+ "The following instructions will work when the `simulate` parameter is set to `True` during the creation of the MultiAreaModel object, and the `M.simulation.simulate()` method is executed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dc3b1820",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Uncomment the lines in this code cell below to test if the number of synapses created by NEST matches the expected values\n",
+ "\n",
+ "# \"\"\"\n",
+ "# Test if the correct number of synapses has been created.\n",
+ "# \"\"\"\n",
+ "# print(\"Testing synapse numbers\")\n",
+ "# for target_area_name in M.area_list:\n",
+ "# target_area = M.simulation.areas[M.simulation.areas.index(target_area_name)]\n",
+ "# for source_area_name in M.area_list:\n",
+ "# source_area = M.simulation.areas[M.simulation.areas.index(source_area_name)]\n",
+ "# for target_pop in M.structure[target_area.name]:\n",
+ "# target_nodes = target_area.gids[target_pop]\n",
+ "# for source_pop in M.structure[source_area.name]:\n",
+ "# source_nodes = source_area.gids[source_pop]\n",
+ "# created_syn = nest.GetConnections(source=source_nodes,\n",
+ "# target=target_nodes)\n",
+ "# syn = M.synapses[target_area.name][target_pop][source_area.name][source_pop]\n",
+ "# assert(len(created_syn) == int(syn))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "57401110",
+ "metadata": {},
+ "source": [
+ "To obtain the connections information, you can extract the lists of connected sources and targets. Moreover, you can access additional synaptic details, such as synaptic weights and delays."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e7eb052e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "conns = nest.GetConnections()\n",
+ "conns_sparse_matrix = conns.get(['source', 'target', 'weight'])\n",
+ "\n",
+ "srcs = conns_sparse_matrix['source']\n",
+ "tgts = conns_sparse_matrix['target']\n",
+ "weights = conns_sparse_matrix['weight']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ef4b2e4b",
+ "metadata": {},
+ "source": [
+ "You can determine the area and subpopulation to which the neuron ID ranges belong by referring to the file `network_gids.txt`, which is automatically generated during network creation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "902f2800",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Open the file using a with statement\n",
+ "with open(os.path.join(M.simulation.data_dir,\"recordings/network_gids.txt\"), \"r\") as file:\n",
+ " # Read the contents of the file\n",
+ " gids = file.read()\n",
+ "\n",
+ "# Print the contents\n",
+ "print(gids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "875263c1",
+ "metadata": {},
+ "source": [
+ "<br>"
+ ]
+ },
{
"cell_type": "markdown",
"id": "9be9287d-4891-4b4b-bd19-cfc2ebed02ac",
"metadata": {},
"source": [
- "## Load and process simulation results data"
+ "## Load and process data of simulation results"
]
},
{
@@ -438,6 +639,14 @@
"rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "ca603daf",
+ "metadata": {},
+ "source": [
+ "<br>"
+ ]
+ },
{
"cell_type": "markdown",
"id": "57ff902c-d6ce-4f96-9e4f-8e3e7166ab66",