diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index cd903083d215a313b5478fdbf612eec91b38087c..b32aab190074e69cb7ce74a96083eea53c9de8fb 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -8,12 +8,44 @@
"# Down-scaled multi-area model"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "b952d0ea",
+ "metadata": {},
+ "source": [
+ "### Notebook structure\n",
+ "* [Create config file](#section_1)\n",
+ "* [Import dependencies](#section_2)\n",
+ "* [Jupyter notebook display format setting](#section_3)\n",
+ "* [Specify paramters of model](#section_4)\n",
+ " * [1. Scaling factor (scale_down_to)](#section_4.1)\n",
+ " * [2. Model parameters](#section_4.2)\n",
+ " * [3. Simulation parameters](#section_4.3)\n",
+ " * [4. Theory parameters](#section_4.4)\n",
+ "* [Instantiate a multi-area model and analyse](#section_5)\n",
+ " * [1. Insantiate a multi-area model](#section_5.1)\n",
+ " * [2. Predict firing rates from theory](#section_5.2)\n",
+ " * [3. Extract connectivity](#section_5.3)\n",
+ "* [Run the simulation](#section_6)\n",
+ "* [Simulation results analysis](#section_7)\n",
+ "* [Load and process data of simulation results](#section_8)\n",
+ "* [Simulation results visualization](#section_9) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c0c47ec",
+ "metadata": {},
+ "source": [
+ "<br>"
+ ]
+ },
{
"cell_type": "markdown",
"id": "d782e527",
"metadata": {},
"source": [
- "### Create config file"
+ "### Create config file <a class=\"anchor\" id=\"section_1\"></a>"
]
},
{
@@ -38,7 +70,7 @@
"id": "4a853b97",
"metadata": {},
"source": [
- "### Import dependencies"
+ "### Import dependencies <a class=\"anchor\" id=\"section_2\"></a>"
]
},
{
@@ -73,7 +105,7 @@
"id": "2f429063",
"metadata": {},
"source": [
- "### Jupyter notebook display format setting"
+ "### Jupyter notebook display format setting <a class=\"anchor\" id=\"section_3\"></a>"
]
},
{
@@ -105,7 +137,7 @@
"tags": []
},
"source": [
- "## Specify paramters of model"
+ "### Specify paramters of model <a class=\"anchor\" id=\"section_4\"></a>"
]
},
{
@@ -115,7 +147,7 @@
"tags": []
},
"source": [
- "### 1. Scaling factor (scale_down_to)\n",
+ "#### 1. Scaling factor (scale_down_to) <a class=\"anchor\" id=\"section_4.1\"></a>\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.**"
]
@@ -140,41 +172,13 @@
"scale_down_to = 0.005 # Change it to 1. for running the fullscale network"
]
},
- {
- "cell_type": "markdown",
- "id": "d53f1eab",
- "metadata": {},
- "source": [
- "### 2. Model, simulation and theory parameters"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e779c727",
- "metadata": {},
- "source": [
- "The parameters fall into 3 categories: Model parameters, Simulation parameters, Theory parameters.<br>\n",
- "\n",
- "**Model parameters**<br>\n",
- "Model paramters are the most important among all the paramters, it directly affect the model itself and thus have a great impact on the simulation results. Model paramters define the connection, input, neuron, and network charateristics of the model.\n",
- "* Connection paramters\n",
- "* Input parameters\n",
- "* Neuron parameters\n",
- "* Network parameters<br>\n",
- "\n",
- "**Simualation parameters**<br>\n",
- "Simulation parameters define the paramters that influence the process of simulation, inlcuding the simulation time, the number of processes and theads used to run the simulation.<br>\n",
- "\n",
- "**Theory parameters**<br>\n",
- "Theory parameters defines ..."
- ]
- },
{
"cell_type": "markdown",
"id": "14bac1ce-4ba0-47ec-84b2-9d3c72bc96ec",
"metadata": {},
"source": [
- "#### 2.1 Model parameters"
+ "#### 2. Model parameters <a class=\"anchor\" id=\"section_4.2\"></a>\n",
+ "Model paramters are most important among all the paramters, it directly affect the model itself and thus have a great impact on the simulation results. Model paramters define the connection, input, neuron, and network charateristics of the model, and therefore fall into four categories: **Connection paramters**, **Input paramters**, **Neuron paramters**, and **Network paramters**."
]
},
{
@@ -318,7 +322,8 @@
"id": "a0730f70-ed9b-4664-b677-3dda965a01ef",
"metadata": {},
"source": [
- "### 2.2 Simulation paramters (sim_params)"
+ "#### 3. Simulation paramters (sim_params) <a class=\"anchor\" id=\"section_4.3\"></a>\n",
+ "Simulation parameters define the paramters that influence the process of simulation, inlcuding the simulation time, the number of processes and theads used to run the simulation.<br>"
]
},
{
@@ -354,7 +359,8 @@
"id": "79596d77-c105-45d0-9a57-2d15e31f1189",
"metadata": {},
"source": [
- "### 2.3. Theory paramters (theory_params)"
+ "#### 4. Theory paramters (theory_params) <a class=\"anchor\" id=\"section_4.4\"></a>\n",
+ "Theory parameters defines ..."
]
},
{
@@ -390,7 +396,7 @@
"id": "de4a6703",
"metadata": {},
"source": [
- "## Instantiate a multi-area model and analyse"
+ "### Instantiate a multi-area model and analyse <a class=\"anchor\" id=\"section_5\"></a>"
]
},
{
@@ -398,7 +404,7 @@
"id": "1fd58841",
"metadata": {},
"source": [
- "### 1. Insantiate a multi-area model "
+ "#### 1. Insantiate a multi-area model <a class=\"anchor\" id=\"section_5.1\"></a>"
]
},
{
@@ -419,7 +425,7 @@
"id": "91649c30",
"metadata": {},
"source": [
- "### 2. Predict firing rates from theory"
+ "#### 2. Predict firing rates from theory <a class=\"anchor\" id=\"section_5.2\"></a>"
]
},
{
@@ -439,7 +445,7 @@
"id": "2062ddf3",
"metadata": {},
"source": [
- "### 3. Extract connectivity"
+ "#### 3. Extract connectivity <a class=\"anchor\" id=\"section_5.3\"></a>"
]
},
{
@@ -455,7 +461,7 @@
"id": "b7396606",
"metadata": {},
"source": [
- "#### 3.1 Node indegrees"
+ "##### 3.1 Node indegrees"
]
},
{
@@ -475,7 +481,7 @@
"id": "253a2aba",
"metadata": {},
"source": [
- "#### 3.2 Synapses"
+ "##### 3.2 Synapses"
]
},
{
@@ -503,7 +509,7 @@
"id": "0c1cad59-81d0-4e24-ac33-13c4ca8c6dec",
"metadata": {},
"source": [
- "## Run the simulation"
+ "### Run the simulation <a class=\"anchor\" id=\"section_6\"></a>"
]
},
{
@@ -530,7 +536,7 @@
"id": "28e071f8",
"metadata": {},
"source": [
- "## Simulation results analysis"
+ "### Simulation results analysis <a class=\"anchor\" id=\"section_7\"></a>"
]
},
{
@@ -628,7 +634,7 @@
"id": "9be9287d-4891-4b4b-bd19-cfc2ebed02ac",
"metadata": {},
"source": [
- "## Load and process data of simulation results"
+ "### Load and process data of simulation results"
]
},
{
@@ -636,7 +642,7 @@
"id": "8726a93d",
"metadata": {},
"source": [
- "### 1. Load spike data"
+ "#### 1. Load spike data"
]
},
{
@@ -654,7 +660,7 @@
"id": "8793e033",
"metadata": {},
"source": [
- "### 2. Compute instantaneous rate per neuron across all populations"
+ "#### 2. Compute instantaneous rate per neuron across all populations"
]
},
{
@@ -681,7 +687,7 @@
"id": "57ff902c-d6ce-4f96-9e4f-8e3e7166ab66",
"metadata": {},
"source": [
- "## Simulation results visualization"
+ "### Simulation results visualization"
]
},
{
@@ -689,7 +695,7 @@
"id": "38ddd973",
"metadata": {},
"source": [
- "### 1. Instantaneous and mean rate"
+ "#### 1. Instantaneous and mean rate"
]
},
{