diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 8edb0c0b628a855ff3a4687c0a1572cd09495fd2..631cbcf2ccf93bc5e0e54bbd5627142d01e6455c 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -1,5 +1,26 @@
{
"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,
@@ -10,16 +31,38 @@
"!pip install nested_dict dicthash"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "0c6b6a3c",
+ "metadata": {},
+ "source": [
+ "Create config file."
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "61e83d47",
+ "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": [
- "import numpy as np\n",
- "import os\n",
- "\n",
"from multiarea_model import MultiAreaModel"
]
},
@@ -28,10 +71,11 @@
"id": "2cedd26b",
"metadata": {},
"source": [
- "# Down-scaled model\n",
- "\n",
"Neurons and indegrees are both scaled down to 1%.\n",
- "Can usually be simulated on a local machine."
+ "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.**"
]
},
{
@@ -46,11 +90,10 @@
},
{
"cell_type": "markdown",
- "id": "241db524",
+ "id": "d53f1eab",
"metadata": {},
"source": [
- "**Warning: This will not yield reasonable dynamical results from the\n",
- "network and is only meant to demonstrate the simulation workflow.**"
+ "Specify model and simulation parameters."
]
},
{
@@ -60,7 +103,6 @@
"metadata": {},
"outputs": [],
"source": [
- "d = {}\n",
"conn_params = {'replace_non_simulated_areas': 'het_poisson_stat',\n",
" 'g': -11.,\n",
" \n",
@@ -88,110 +130,109 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "id": "d409be95",
+ "cell_type": "markdown",
+ "id": "de4a6703",
"metadata": {},
- "outputs": [],
"source": [
- "M = MultiAreaModel(network_params, simulation=True,\n",
- " sim_spec=sim_params,\n",
- " theory=True,\n",
- " theory_spec=theory_params)\n",
- "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])))"
+ "Instantiate a multi-area model, predict firing rates from theroy, and run the simulation. "
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "15778e9c",
+ "id": "d409be95",
"metadata": {},
"outputs": [],
"source": [
- "M.simulation.simulate()"
+ "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": "cb8e3edd",
+ "id": "918d907f",
"metadata": {},
"outputs": [],
"source": [
- "data = np.loadtxt(M.simulation.label + \"-spikes-1-0.dat\", skiprows=3)"
+ "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": "84dc3bde",
+ "id": "15778e9c",
"metadata": {},
"outputs": [],
"source": [
- "data"
+ "M.simulation.simulate()"
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "id": "9590223b",
+ "cell_type": "markdown",
+ "id": "8726a93d",
"metadata": {},
- "outputs": [],
"source": [
- "tsteps, spikecount = np.unique(data[:,1], return_counts=True)"
+ "Load spike data."
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "0c3fc302",
+ "id": "cb8e3edd",
"metadata": {},
"outputs": [],
"source": [
- "import matplotlib.pyplot as plt"
+ "data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + \"-spikes-1-0.dat\", skiprows=3)"
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "id": "9f21dd40",
+ "cell_type": "markdown",
+ "id": "8793e033",
"metadata": {},
- "outputs": [],
"source": [
- "fig, ax = plt.subplots()\n",
- "ax.plot(tsteps, spikecount)"
+ "Compute instantaneous rate per neuron across all populations."
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "41028d2c",
+ "id": "9590223b",
"metadata": {},
"outputs": [],
"source": [
- "plt.show()"
+ "tsteps, spikecount = np.unique(data[:,1], return_counts=True)\n",
+ "rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)"
]
},
{
- "cell_type": "code",
- "execution_count": null,
- "id": "c6091457",
+ "cell_type": "markdown",
+ "id": "38ddd973",
"metadata": {},
- "outputs": [],
"source": [
- "len(spikecount), np.average(spikecount)"
+ "Plot instantaneous and mean rate."
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "ad2bd7b2",
+ "id": "bea30fc8",
"metadata": {},
"outputs": [],
"source": [
- "np.average(spikecount[2000:])"
+ "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()"
]
}
],