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3ac823ad · Didi Hou · Administrator · 141076fd · 3ac823ad
Commit 3ac823ad authored 1 year ago by Didi Hou Committed by Administrator 1 year ago
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -8,6 +8,14 @@
    "# Down-scaled multi-area model"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "id": "3e163b35-5330-48ca-8c7e-9e0a884520ca",
+   "metadata": {},
+   "source": [
+    "## Import dependencies"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": null,
@@ -33,10 +41,10 @@
  },
  {
   "cell_type": "markdown",
-   "id": "0c6b6a3c",
+   "id": "98555af1-a5c5-4db6-85dc-6d07e40f38cc",
   "metadata": {},
   "source": [
-    "Create config file."
+    "## Create config file"
   ]
  },
  {
@@ -66,16 +74,21 @@
    "from multiarea_model import MultiAreaModel"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "id": "df83f5ea-1c4b-44d3-9926-01786aa46e14",
+   "metadata": {},
+   "source": [
+    "## Specify paramters of model"
+   ]
+  },
  {
   "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.**"
+    "### 1. Scaling parameter\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.**"
   ]
  },
  {
@@ -93,7 +106,7 @@
   "id": "d53f1eab",
   "metadata": {},
   "source": [
-    "Specify model and simulation parameters."
+    "### 2. Model and simulation parameters"
   ]
  },
  {
@@ -119,8 +132,24 @@
    "                  'fullscale_rates': 'tests/fullscale_rates.json',\n",
    "                  'input_params': input_params,\n",
    "                  'connection_params': conn_params,\n",
-    "                  'neuron_params': neuron_params}\n",
-    "\n",
+    "                  'neuron_params': neuron_params}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0730f70-ed9b-4664-b677-3dda965a01ef",
+   "metadata": {},
+   "source": [
+    "### 3. Simulation paramters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21484ed3-295f-4d06-b757-2969aac429a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
    "sim_params = {'t_sim': 2000.,\n",
    "              'num_processes': 1,\n",
    "              'local_num_threads': 1,\n",
@@ -134,7 +163,7 @@
   "id": "de4a6703",
   "metadata": {},
   "source": [
-    "Instantiate a multi-area model, predict firing rates from theroy, and run the simulation. "
+    "## Instantiate a multi-area model, predict firing rates from theroy "
   ]
  },
  {
@@ -162,6 +191,14 @@
    "      \"rate of {0:.3f} spikes/s across all populations.\".format(np.mean(r[:, -1])))"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "id": "0c1cad59-81d0-4e24-ac33-13c4ca8c6dec",
+   "metadata": {},
+   "source": [
+    "## Run the simulation"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": null,
@@ -172,12 +209,20 @@
    "M.simulation.simulate()"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "id": "9be9287d-4891-4b4b-bd19-cfc2ebed02ac",
+   "metadata": {},
+   "source": [
+    "## Load and process simulation results data"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "8726a93d",
   "metadata": {},
   "source": [
-    "Load spike data."
+    "### 1. Load spike data"
   ]
  },
  {
@@ -195,7 +240,7 @@
   "id": "8793e033",
   "metadata": {},
   "source": [
-    "Compute instantaneous rate per neuron across all populations."
+    "### 2. Compute instantaneous rate per neuron across all populations"
   ]
  },
  {
@@ -209,12 +254,20 @@
    "rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "id": "57ff902c-d6ce-4f96-9e4f-8e3e7166ab66",
+   "metadata": {},
+   "source": [
+    "## Simulation results visualization"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "38ddd973",
   "metadata": {},
   "source": [
-    "Plot instantaneous and mean rate."
+    "### 1. Instantaneous and mean rate"
   ]
  },
  {
@@ -238,7 +291,7 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
@@ -252,7 +305,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.8.10"
  }
 },
 "nbformat": 4,

 %% Cell type:markdown id:b1331599 tags:

 # Down-scaled multi-area model

+%% Cell type:markdown id:3e163b35-5330-48ca-8c7e-9e0a884520ca tags:
+
+## Import dependencies
+
 %% Cell type:code id:96517739 tags:

 ``` python
 %matplotlib inline
 import matplotlib.pyplot as plt
 import numpy as np
 import os
 ```

 %% Cell type:code id:7e07b0d0 tags:

 ``` python
 !pip install nested_dict dicthash
 ```

-%% Cell type:markdown id:0c6b6a3c tags:
+%% Cell type:markdown id:98555af1-a5c5-4db6-85dc-6d07e40f38cc tags:

-Create config file.
+## Create config file

 %% Cell type:code id:72c170e4 tags:

 ``` python
 with open('config.py', 'w') as fp:
    fp.write(
 '''import os
 base_path = os.path.abspath(".")
 data_path = os.path.abspath("simulations")
 jobscript_template = "python {base_path}/run_simulation.py {label}"
 submit_cmd = "bash -c"
 ''')
 ```

 %% Cell type:code id:2784f76b tags:

 ``` python
 from multiarea_model import MultiAreaModel
 ```

-%% Cell type:markdown id:2cedd26b tags:
+%% Cell type:markdown id:df83f5ea-1c4b-44d3-9926-01786aa46e14 tags:
+
+## Specify paramters of model

-Neurons and indegrees are both scaled down to 0.5%.
-Can usually be simulated on a local machine.
+%% Cell type:markdown id:2cedd26b tags:

-**Warning: This will not yield reasonable dynamical results from the
-network and is only meant to demonstrate the simulation workflow.**
+### 1. Scaling parameter
+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:code id:e940bb6b tags:

 ``` python
 scale_down_to = 0.005
 ```

 %% Cell type:markdown id:d53f1eab tags:

-Specify model and simulation parameters.
+### 2. Model and simulation parameters

 %% Cell type:code id:7af3a191 tags:

 ``` python
 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,
                  'K_scaling': scale_down_to,
                  'fullscale_rates': 'tests/fullscale_rates.json',
                  'input_params': input_params,
                  'connection_params': conn_params,
                  'neuron_params': neuron_params}
+```
+
+%% Cell type:markdown id:a0730f70-ed9b-4664-b677-3dda965a01ef tags:
+
+### 3. Simulation paramters

+%% Cell type:code id:21484ed3-295f-4d06-b757-2969aac429a4 tags:
+
+``` python
 sim_params = {'t_sim': 2000.,
              'num_processes': 1,
              'local_num_threads': 1,
              'recording_dict': {'record_vm': False}}

 theory_params = {'dt': 0.1}
 ```

 %% Cell type:markdown id:de4a6703 tags:

-Instantiate a multi-area model, predict firing rates from theroy, and run the simulation.
+## Instantiate a multi-area model, predict firing rates from theroy

 %% Cell type:code id:d409be95 tags:

 ``` python
 M = MultiAreaModel(network_params, simulation=True,
                   sim_spec=sim_params,
                   theory=True,
                   theory_spec=theory_params)
 ```

 %% Cell type:code id:918d907f tags:

 ``` python
 p, r = M.theory.integrate_siegert()
 print("Mean-field theory predicts an average "
      "rate of {0:.3f} spikes/s across all populations.".format(np.mean(r[:, -1])))
 ```

+%% Cell type:markdown id:0c1cad59-81d0-4e24-ac33-13c4ca8c6dec tags:
+
+## Run the simulation
+
 %% Cell type:code id:15778e9c tags:

 ``` python
 M.simulation.simulate()
 ```

+%% Cell type:markdown id:9be9287d-4891-4b4b-bd19-cfc2ebed02ac tags:
+
+## Load and process simulation results data
+
 %% Cell type:markdown id:8726a93d tags:

-Load spike data.
+### 1. Load spike data

 %% Cell type:code id:cb8e3edd tags:

 ``` python
 data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
 ```

 %% Cell type:markdown id:8793e033 tags:

-Compute instantaneous rate per neuron across all populations.
+### 2. Compute instantaneous rate per neuron across all populations

 %% Cell type:code id:9590223b tags:

 ``` python
 tsteps, spikecount = np.unique(data[:,1], return_counts=True)
 rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
 ```

+%% Cell type:markdown id:57ff902c-d6ce-4f96-9e4f-8e3e7166ab66 tags:
+
+## Simulation results visualization
+
 %% Cell type:markdown id:38ddd973 tags:

-Plot instantaneous and mean rate.
+### 1. Instantaneous and mean rate

 %% Cell type:code id:bea30fc8 tags:

 ``` python
 fig, ax = plt.subplots()
 ax.plot(tsteps, rate)
 ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
 ax.set_title('instantaneous rate across all populations')
 ax.set_xlabel('time (ms)')
 ax.set_ylabel('rate (spikes / s)')
 ax.set_xlim(0, sim_params['t_sim'])
 ax.set_ylim(0, 50)
 ax.legend()
 ```