diff --git a/.ipynb_checkpoints/.ipynb_checkpoints/multi-area-model-checkpoint-checkpoint.ipynb b/.ipynb_checkpoints/.ipynb_checkpoints/multi-area-model-checkpoint-checkpoint.ipynb
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@@ -1,1288 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "b1331599",
- "metadata": {},
- "source": [
- "# Down-scaled multi-area model"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b952d0ea",
- "metadata": {
- "tags": []
- },
- "source": [
- "#### Notebook structure <a class=\"anchor\" id=\"toc\"></a>\n",
- "* [S0. Configuration](#section_0)\n",
- "* [S1. Paramters specification](#section_1)\n",
- " * [1.1. Parameters to tune](#section_1_1)\n",
- " * [1.2. Default parameters](#section_1_2)\n",
- "* [S2. Multi-area model instantiation and simulation](#section_2)\n",
- " * [2.1. Insantiate a multi-area model](#section_2_1)\n",
- " * [2.2. Predict firing rates from theory](#section_2_2)\n",
- " * [2.3. Extract interarea connectivity](#section_2_3)\n",
- " * [2.4. Run the simulation](#section_2_4)\n",
- "* [S3. Simulation results analysis and data processing](#section_3)\n",
- "* [S4. Simulation results visualization](#section_4) \n",
- " * [4.1. Instantaneous firing rate and mean firing rate](#section_4_1)\n",
- " * [4.2. Raster plot of spiking activity for single area](#section_4_2)\n",
- " * [4.3. Population-averaged firing rate](#section_4_3)\n",
- " * [4.4. Average pairwise correlation coefficients of spiking activity](#section_4_4)\n",
- " * [4.5. Irregularity of spiking activity](#section_4_5)\n",
- " * [4.6. Time series of population- and area-averaged firing rates](#section_4_6)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bd3d4b0e",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d782e527",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "## S0. Configuration <a class=\"anchor\" id=\"section_0\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "96517739",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Import dependencies\n",
- "%matplotlib inline\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import os\n",
- "import nest\n",
- "from IPython.display import display, HTML\n",
- "\n",
- "# Import the MultiAreaModel class\n",
- "from multiarea_model import MultiAreaModel\n",
- "from multiarea_model import Analysis\n",
- "from config import base_path"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2dd47c64",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create config file\n",
- "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": "7e07b0d0",
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install nested_dict dicthash"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Jupyter notebook display format setting\n",
- "style = \"\"\"\n",
- "<style>\n",
- "table {float:left}\n",
- "</style>\n",
- "\"\"\"\n",
- "display(HTML(style))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27160ba8",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "565be233",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "df83f5ea-1c4b-44d3-9926-01786aa46e14",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "## S1. Paramters specification <a class=\"anchor\" id=\"section_1\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "30655817",
- "metadata": {},
- "source": [
- "### 1.1. Parameters to tune <a class=\"anchor\" id=\"section_1_1\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4f67c1ba",
- "metadata": {},
- "source": [
- "|Parameter |Default value |Value range/options |Value assigned |Description |\n",
- "|:----------------------------:|:-----------------------:|:--------------------------------------------------------------------:|:------------------:|:-----------:|\n",
- "|scale_down_to |1. |(0, 1.] |0.005 |$^1$ |\n",
- "|cc_weights_factor |1. |(0, 1.] |1. |$^2$ |\n",
- "|areas_simulated |complete_area_list |All sublists of complete_area_list |complete_area_list |$^3$ |\n",
- "|replace_non_simulated_areas |None |None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat' |'het_poisson_stat' |$^4$ |"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a2161477",
- "metadata": {},
- "source": [
- "1. `scale_down_to` <br>\n",
- "`scale_down_to` is the down-scaling factor 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> Its deafualt value if `1.` meaning full scale simulation. <br> In the pre-set downscale version, it's set as `0.005`, where the numer of neurons and indegrees are both scaled down to 0.5% of its full scale amount, 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 <br> \n",
- "2. `cc_weights_factor` <br>\n",
- "This scaling factor controls the cortico-cortical synaptic strength. <br> By default it's set as `1.0`, where the inter-area synaptic strength is the same as the intra-areal. <br> **Important**: This factor changes the network activity from ground state to metastable state. <br>\n",
- "3. `areas_simulated` <br>\n",
- "This parameter specifies the cortical areas included in the simulation process. Its default value is `complete_area_list` meaning all the areas in the complete_area_list will be actually simulated. <br>\n",
- "complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH'] <br>\n",
- "The value assigned to simulation_areas can be any sublist of the compete_area_list specifying areas a user want to include in his/her simulation. <br>\n",
- "4. `replace_non_simulated_areas` <br>\n",
- "The paramter `replace_non_simulated_areas` defines how non-simulated areas will be replaced. <br> It's set as `None` by default when the parameter areas_simulated is set as full_area_list where all areas will be simulated so that no areas need to be replaced. <br> Other options are: `'hom_poisson_stat'`, `'het_poisson_stat'`, and `'het_current_nonstat'`. `'hom_poisson_stat'` is a manually set parameter which can be tuned. When it's set as 'het_poisson_stat' or 'het_current_nonstat', the data to replace the cortico-cortical input is loaded from 'replace_cc_input_source' which is the firing rates of our full scale simulation results. The differenc between 'het_poisson_stat' and 'het_current_nonstat' is that 'het_poisson_stat' is the mean of the time-series firing rate so that it's static, yet 'het_current_nonstat' is time-varying specific current, which is varying by time. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "60265d52",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Downscaling factor\n",
- "scale_down_to = 0.005 # Change it to 1. for running the fullscale network\n",
- "\n",
- "# Scaling factor for cortico-cortical connections (chi) \n",
- "cc_weights_factor = 1.\n",
- "\n",
- "# Cortical areas included in the simulation\n",
- "areas_simulated = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']\n",
- "\n",
- "# Firing rates used to replace the non-simulated areas\n",
- "replace_non_simulated_areas = 'het_poisson_stat'"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "de11b07f",
- "metadata": {},
- "source": [
- "### 1.2. Default parameters <a class=\"anchor\" id=\"section_1_2\"></a>\n",
- "We try our best not to confuse users with too many parameters. However, if you want to change more parameters and explore the model, you can do so by passing a dictionary to the `default_params` argument of the `MultiAreaModel` class."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6e4bed8d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Connection parameters\n",
- "conn_params = {\n",
- " 'replace_non_simulated_areas': 'het_poisson_stat', # Whether to replace non-simulated areas by Poisson sources with the same global rate, by default: None\n",
- " 'g': -11., # It sets the relative inhibitory synaptic strength, by default: -16.\n",
- " 'K_stable': 'K_stable.npy', # Whether to apply the stabilization method of Schuecker, Schmidt et al. (2017), by default: None\n",
- " 'fac_nu_ext_TH': 1.2, # Increase the external input to 2/3E and 5E in area TH\n",
- " 'fac_nu_ext_5E': 1.125, # Increase the external Poisson indegree onto 5E\n",
- " 'fac_nu_ext_6E': 1.41666667, # Increase the external Poisson indegree onto 6E\n",
- " 'av_indegree_V1': 3950. # Adjust the average indegree in V1 based on monkey data\n",
- "}\n",
- "\n",
- "# Input parameters\n",
- "input_params = {\n",
- " 'rate_ext': 10. # Rate of the Poissonian spike generator (in spikes/s)\n",
- "} \n",
- "\n",
- "# Neuron parameters\n",
- "neuron_params = {\n",
- " 'V0_mean': -150., # Mean for the distribution of initial membrane potentials, by default: -100.\n",
- " 'V0_sd': 50.} # Standard deviation for the distribution of initial membrane potentials, by default: 50.\n",
- "\n",
- "# Network parameters\n",
- "network_params = {\n",
- " 'N_scaling': scale_down_to, # Scaling of population sizes, by default: 1.\n",
- " 'K_scaling': scale_down_to, # Scaling of indegrees, by default: 1.\n",
- " 'fullscale_rates': 'tests/fullscale_rates.json', # Absolute path to the file holding full-scale rates for scaling synaptic weights, by default: None\n",
- " 'input_params': input_params, # Input parameters\n",
- " 'connection_params': conn_params, # Connection parameters\n",
- " 'neuron_params': neuron_params # Neuron parameters\n",
- "} \n",
- "\n",
- "# Simulation parameters\n",
- "sim_params = {\n",
- " 'areas_simulated': areas_simulated,\n",
- " 't_sim': 2000., # Simulated time (in ms), by default: 10.0\n",
- " 'num_processes': 1, # The number of MPI processes, by default: 1\n",
- " 'local_num_threads': 1, # The number of threads per MPI process, by default: 1\n",
- " 'recording_dict': {'record_vm': False},\n",
- " 'rng_seed': 1 # global random seed\n",
- "}\n",
- "\n",
- "# Theory paramters (theory_params)\n",
- "theory_params = {\n",
- " 'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01\n",
- "} "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1472e9c5",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c532a861-824f-4713-a311-590aef8b6134",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "de4a6703",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "## S2. Multi-area model instantiation and simulation <a class=\"anchor\" id=\"section_2\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1fd58841",
- "metadata": {},
- "source": [
- "### 2.1. Insantiate a multi-area model <a class=\"anchor\" id=\"section_2_1\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ab25f9f8",
- "metadata": {},
- "outputs": [],
- "source": [
- "M = MultiAreaModel(network_params, \n",
- " simulation=True,\n",
- " sim_spec=sim_params,\n",
- " theory=True,\n",
- " theory_spec=theory_params)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "91649c30",
- "metadata": {},
- "source": [
- "### 2.2. Predict firing rates from theory <a class=\"anchor\" id=\"section_2_2\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6a7ddf0e",
- "metadata": {},
- "outputs": [],
- "source": [
- "p, r = M.theory.integrate_siegert()\n",
- "print(\"Mean-field theory predicts an average \"\n",
- " \"firing rate of {0:.3f} spikes/s across all populations.\".format(np.mean(r[:, -1])))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2062ddf3",
- "metadata": {},
- "source": [
- "### 2.3. Extract interarea connectivity <a class=\"anchor\" id=\"section_2_3\"></a>"
- ]
- },
- {
- "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). <br>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b7396606",
- "metadata": {},
- "source": [
- "#### 2.3.1 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": [
- "#### 2.3.2 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": "e67f37e9-ec8d-4bb1-bd21-45e966f47ab6",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "04894f5e-35ec-4b22-8891-bd7ba86098e9",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0c1cad59-81d0-4e24-ac33-13c4ca8c6dec",
- "metadata": {},
- "source": [
- "### 2.4. Run the simulation <a class=\"anchor\" id=\"section_2_4\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "15778e9c",
- "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": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4003c5a5-4a6f-49c5-be17-09f1bc68c411",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "28e071f8",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "## S3. Simulation results analysis <a class=\"anchor\" id=\"section_3\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "89c7b7cf",
- "metadata": {},
- "source": [
- "### 3.1 Test if the correct number of synapses has been created"
- ]
- },
- {
- "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",
- "# 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": [
- "### 3.2 Extract connections information\n",
- "**Warning**: Memory explosion <br>\n",
- "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": "b1320ab1",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "529b1ade",
- "metadata": {},
- "source": [
- "<br>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "57ff902c-d6ce-4f96-9e4f-8e3e7166ab66",
- "metadata": {},
- "source": [
- "## S4. Data processing and simulation results visualization <a class=\"anchor\" id=\"section_4\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6806564b-d5d4-47d4-afa9-5da0df83e025",
- "metadata": {},
- "outputs": [],
- "source": [
- "from config import data_path\n",
- "label_spikes = M.simulation.label\n",
- "label = M.simulation.label"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7997d893-252d-4295-a22a-1e510f8424ae",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "\"\"\"\n",
- "Analysis class.\n",
- "An instance of the analysis class for the given network and simulation.\n",
- "Can be created as a member class of a multiarea_model instance or standalone.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "network : MultiAreaModel\n",
- " An instance of the multiarea_model class that specifies\n",
- " the network to be analyzed.\n",
- "simulation : Simulation\n",
- " An instance of the simulation class that specifies\n",
- " the simulation to be analyzed.\n",
- "data_list : list of strings {'spikes', vm'}, optional\n",
- " Specifies which type of data is to load. Defaults to ['spikes'].\n",
- "load_areas : list of strings with area names, optional\n",
- " Specifies the areas for which data is to be loaded.\n",
- " Default value is None and leads to loading of data for all\n",
- " simulated areas.\n",
- "\"\"\"\n",
- "A = Analysis(network=M, \n",
- " simulation=M.simulation, \n",
- " data_list=['spikes'],\n",
- " load_areas=None)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "da58921f-713b-424c-8fa1-80d9755558f3",
- "metadata": {},
- "outputs": [],
- "source": [
- "\"\"\"\n",
- "Loads simulation data of the requested type either from hdf5 files.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "\n",
- "data_list : list\n",
- " list of observables to be loaded. Can contain 'spikes' and 'vm'\n",
- "\"\"\"\n",
- "A.load_data(data_list=['spikes'])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "38ddd973",
- "metadata": {
- "tags": []
- },
- "source": [
- "### 4.1. Instantaneous firing rate and mean firing rate <a class=\"anchor\" id=\"section_4_1\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "56e368b6-72a2-43fb-b02e-f70ad6770e40",
- "metadata": {},
- "outputs": [],
- "source": [
- "data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + \"-spikes-1-0.dat\", skiprows=3)\n",
- "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": "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 firing rate across all populations')\n",
- "ax.set_xlabel('time (ms)')\n",
- "ax.set_ylabel('Firing rate (spikes / s)')\n",
- "ax.set_xlim(0, sim_params['t_sim'])\n",
- "ax.set_ylim(0, 50)\n",
- "ax.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ae19bcc3",
- "metadata": {
- "tags": []
- },
- "source": [
- "### 4.2 Raster plot of spiking activity for single area <a class=\"anchor\" id=\"section_4_2\"></a>\n",
- "Raster plot of spiking activity of 3% of the neurons in area V1 (A), V2 (B), and FEF (C). Blue: excitatory neurons, red: inhibitory neurons. (D-F) Spiking statistics across all 32 areas for the respective populations shown as area-averaged box plots. Crosses: medians, boxes: interquartile range (IQR), whiskers extend to the most extremeobservat ions within 1.5×IQR beyond the IQR."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c47e82ec-32f3-4de1-a32e-8f6b500787e0",
- "metadata": {},
- "outputs": [],
- "source": [
- "areas = ['V1', 'V2', 'FEF']\n",
- "labels = ['A', 'B', 'C']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4066f042-995f-4987-bac2-7d7c61addbd0",
- "metadata": {},
- "outputs": [],
- "source": [
- "# spike data \n",
- "spike_data = {}\n",
- "for area in areas:\n",
- " spike_data[area] = {}\n",
- " for pop in M.structure[area]:\n",
- " spike_data[area][pop] = np.load(os.path.join(M.simulation.data_dir,\n",
- " 'recordings',\n",
- " '{}-spikes-{}-{}.npy'.format(label_spikes, area, pop)))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1da18fee",
- "metadata": {},
- "outputs": [],
- "source": [
- "# \"\"\"\n",
- "# Create raster display of a single area with populations stacked onto each other. Excitatory neurons in blue, inhibitory neurons in red.\n",
- "\n",
- "# Parameters\n",
- "# ----------\n",
- "# area : string {area}\n",
- "# Area to be plotted.\n",
- "# frac_neurons : float, [0,1]\n",
- "# Fraction of cells to be considered.\n",
- "# t_min : float, optional\n",
- "# Minimal time in ms of spikes to be shown. Defaults to 0 ms.\n",
- "# t_max : float, optional\n",
- "# Minimal time in ms of spikes to be shown. Defaults to simulation time.\n",
- "# output : {'pdf', 'png', 'eps'}, optional\n",
- "# If given, the function stores the plot to a file of the given format.\n",
- "\n",
- "# \"\"\"\n",
- "# t_min = 0.\n",
- "# t_max = 500.\n",
- "\n",
- "# # Draw V1\n",
- "# area = 'V1'\n",
- "# frac_neurons = 1.\n",
- "# A.single_dot_display(area, frac_neurons, t_min, t_max)\n",
- "\n",
- "# # Draw V2\n",
- "# area = 'V2'\n",
- "# frac_neurons = 1.\n",
- "# A.single_dot_display(area, frac_neurons, t_min, t_max)\n",
- "\n",
- "# # Draw FEF\n",
- "# area = 'FEF'\n",
- "# frac_neurons = 1.\n",
- "# A.single_dot_display(area, frac_neurons, t_min, t_max)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "73ca1021-0f0a-45bb-a3d5-51381d1357c3",
- "metadata": {},
- "outputs": [],
- "source": [
- "for area, label in zip(areas, labels):\n",
- " label_pos = [-0.2, 1.01]\n",
- " pl.text(label_pos[0], label_pos[1], r'\\bfseries{}' + label + ': ' + area,\n",
- " fontdict={'fontsize': 10, 'weight': 'bold',\n",
- " 'horizontalalignment': 'left', 'verticalalignment':\n",
- " 'bottom'}, transform=axes[label].transAxes)\n",
- "print(\"Raster plots\")\n",
- "\n",
- "t_min = 3000.\n",
- "t_max = 3500.\n",
- "\n",
- "icolor = myred\n",
- "ecolor = myblue\n",
- "\n",
- "frac_neurons = 0.03\n",
- "\n",
- "for i, area in enumerate(areas):\n",
- " ax = axes[labels[i]]\n",
- "\n",
- " if area in spike_data:\n",
- " n_pops = len(spike_data[area])\n",
- " # Determine number of neurons that will be plotted for this area (for\n",
- " # vertical offset)\n",
- " offset = 0\n",
- " n_to_plot = {}\n",
- " for pop in M.structure[area]:\n",
- " n_to_plot[pop] = int(M.N[area][pop] * frac_neurons)\n",
- " offset = offset + n_to_plot[pop]\n",
- " y_max = offset + 1\n",
- " prev_pop = ''\n",
- " yticks = []\n",
- " yticklocs = []\n",
- " for jj, pop in enumerate(M.structure[area]):\n",
- " if pop[0:-1] != prev_pop:\n",
- " prev_pop = pop[0:-1]\n",
- " yticks.append('L' + population_labels[jj][0:-1])\n",
- " yticklocs.append(offset - 0.5 * n_to_plot[pop])\n",
- " ind = np.where(np.logical_and(\n",
- " spike_data[area][pop][:, 1] <= t_max, spike_data[area][pop][:, 1] >= t_min))\n",
- " pop_data = spike_data[area][pop][ind]\n",
- " pop_neurons = np.unique(pop_data[:, 0])\n",
- " neurons_to_ = np.arange(np.min(spike_data[area][pop][:, 0]), np.min(\n",
- " spike_data[area][pop][:, 0]) + n_to_plot[pop], 1)\n",
- "\n",
- " if pop.find('E') > (-1):\n",
- " pcolor = ecolor\n",
- " else:\n",
- " pcolor = icolor\n",
- "\n",
- " for kk in range(n_to_plot[pop]):\n",
- " spike_times = pop_data[pop_data[:, 0] == neurons_to_[kk], 1]\n",
- "\n",
- " _ = ax.plot(spike_times, np.zeros(len(spike_times)) +\n",
- " offset - kk, '.', color=pcolor, markersize=1)\n",
- " offset = offset - n_to_plot[pop]\n",
- " y_min = offset\n",
- " ax.set_xlim([t_min, t_max])\n",
- " ax.set_ylim([y_min, y_max])\n",
- " ax.set_yticklabels(yticks)\n",
- " ax.set_yticks(yticklocs)\n",
- " ax.set_xlabel('Time (s)', labelpad=-0.1)\n",
- " ax.set_xticks([t_min, t_min + 250., t_max])\n",
- " ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "019d805e",
- "metadata": {
- "tags": []
- },
- "source": [
- "### 4.3 Population-averaged firing rate <a class=\"anchor\" id=\"section_4_3\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c05412f6-c842-415f-888a-b7604b795912",
- "metadata": {},
- "outputs": [],
- "source": [
- "\"\"\"\n",
- "Calculate time-averaged population rates and store them in member pop_rates.\n",
- "If the rates had previously been stored with the same\n",
- "parameters, they are loaded from file.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "compute_stat : bool, optional\n",
- " If set to true, the mean and variance of the population rate\n",
- " is calculated. Defaults to False.\n",
- " Caution: Setting to True slows down the computation.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "\"\"\"\n",
- "A.create_pop_rates(t_min=0)\n",
- "print(\"Computing population rates done!\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "721d1f03-df25-468d-8075-a807025a9c58",
- "metadata": {},
- "outputs": [],
- "source": [
- "# stationary firing rates\n",
- "fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')\n",
- "with open(fn, 'r') as f:\n",
- " pop_rates = json.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9ba5ca35-7f90-47c8-a057-7cb02ee7be02",
- "metadata": {},
- "outputs": [],
- "source": [
- "def set_boxplot_props(d):\n",
- " for i in range(len(d['boxes'])):\n",
- " if i % 2 == 0:\n",
- " d['boxes'][i].set_facecolor(icolor)\n",
- " d['boxes'][i].set_color(icolor)\n",
- " else:\n",
- " d['boxes'][i].set_facecolor(ecolor)\n",
- " d['boxes'][i].set_color(ecolor)\n",
- " pl.setp(d['whiskers'], color='k')\n",
- " pl.setp(d['fliers'], color='k', markerfacecolor='k', marker='+')\n",
- " pl.setp(d['medians'], color='none')\n",
- " pl.setp(d['caps'], color='k')\n",
- " pl.setp(d['means'], marker='x', color='k',\n",
- " markerfacecolor='k', markeredgecolor='k', markersize=3.)\n",
- " \n",
- "print(\"plotting Population rates\")\n",
- "\n",
- "rates = np.zeros((len(M.area_list), 8))\n",
- "for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " rate = pop_rates[area][pop][0]\n",
- " if rate == 0.0:\n",
- " rate = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " rates[i][j + 2] = rate\n",
- " else:\n",
- " rates[i][j] = rate\n",
- "\n",
- "\n",
- "rates = np.transpose(rates)\n",
- "masked_rates = np.ma.masked_where(rates < 1e-4, rates)\n",
- "\n",
- "ax = axes['D']\n",
- "d = ax.boxplot(np.transpose(rates), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- "set_boxplot_props(d)\n",
- "\n",
- "ax.plot(np.mean(rates, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "ax.set_yticklabels(population_labels[::-1], size=8)\n",
- "ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- "ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- "x_max = 220.\n",
- "ax.set_xlim((-1., x_max))\n",
- "ax.set_xlabel(r'Rate (spikes/s)', labelpad=-0.1)\n",
- "ax.set_xticks([0., 50., 100.])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "06a595de",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 4.4 Average pairwise correlation coefficients of spiking activity <a class=\"anchor\" id=\"section_4_4\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "84d1689c",
- "metadata": {},
- "outputs": [],
- "source": [
- "compute_corrcoeff.py"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a8e77836-4c37-4b78-b7c4-5e11bc67b4fa",
- "metadata": {},
- "outputs": [],
- "source": [
- "# correlation coefficients\n",
- "fn = os.path.join(data_path, label, 'Analysis', 'corrcoeff.json')\n",
- "with open(fn, 'r') as f:\n",
- " corrcoeff = json.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "218367da-82ef-47b6-bf15-083ef3d43013",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(\"plotting Synchrony\")\n",
- "\n",
- "syn = np.zeros((len(M.area_list), 8))\n",
- "for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = corrcoeff[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " syn[i][j + 2] = value\n",
- " else:\n",
- " syn[i][j] = value\n",
- "\n",
- "\n",
- "syn = np.transpose(syn)\n",
- "masked_syn = np.ma.masked_where(syn < 1e-4, syn)\n",
- "\n",
- "ax = axes['E']\n",
- "d = ax.boxplot(np.transpose(syn), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- "set_boxplot_props(d)\n",
- "\n",
- "ax.plot(np.mean(syn, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "\n",
- "ax.set_yticklabels(population_labels[::-1], size=8)\n",
- "ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- "ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "ax.set_xticks(np.arange(0.0, 0.601, 0.2))\n",
- "ax.set_xlabel('Correlation coefficient', labelpad=-0.1)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a3847e67",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 4.5 Irregularity of spiking activity <a class=\"anchor\" id=\"section_4_5\"></a>\n",
- "Irregularity is measured by revised local variation LvR averaged across neurons"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3c41c7d1-c39a-4c56-bda7-daa515cbaef7",
- "metadata": {},
- "outputs": [],
- "source": [
- "\"\"\"\n",
- "Calculate poulation-averaged LvR (see Shinomoto et al. 2009) and\n",
- "store as member pop_LvR. Uses helper function LvR.\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "\"\"\"\n",
- "A.create_pop_cv_isi()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "65377033-f3c0-4f90-be13-70594cfda292",
- "metadata": {},
- "outputs": [],
- "source": [
- "# local variance revised (LvR)\n",
- "fn = os.path.join(data_path, label, 'Analysis', 'pop_LvR.json')\n",
- "with open(fn, 'r') as f:\n",
- " pop_LvR = json.load(f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d7480a9b",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(\"plotting Irregularity\")\n",
- "\n",
- "LvR = np.zeros((len(M.area_list), 8))\n",
- "for i, area in enumerate(M.area_list):\n",
- " for j, pop in enumerate(M.structure[area][::-1]):\n",
- " value = pop_LvR[area][pop]\n",
- " if value == 0.0:\n",
- " value = 1e-5\n",
- " if area == 'TH' and j > 3: # To account for missing layer 4 in TH\n",
- " LvR[i][j + 2] = value\n",
- " else:\n",
- " LvR[i][j] = value\n",
- "\n",
- "LvR = np.transpose(LvR)\n",
- "masked_LvR = np.ma.masked_where(LvR < 1e-4, LvR)\n",
- "\n",
- "ax = axes['F']\n",
- "d = ax.boxplot(np.transpose(LvR), vert=False,\n",
- " patch_artist=True, whis=1.5, showmeans=True)\n",
- "set_boxplot_props(d)\n",
- "\n",
- "ax.plot(np.mean(LvR, axis=1), np.arange(\n",
- " 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)\n",
- "ax.set_yticklabels(population_labels[::-1], size=8)\n",
- "ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))\n",
- "ax.set_ylim((0., len(M.structure['V1']) + .5))\n",
- "\n",
- "\n",
- "x_max = 2.9\n",
- "ax.set_xlim((0., x_max))\n",
- "ax.set_xlabel('Irregularity', labelpad=-0.1)\n",
- "ax.set_xticks([0., 1., 2.])\n",
- "\n",
- "axes['G'].spines['right'].set_color('none')\n",
- "axes['G'].spines['left'].set_color('none')\n",
- "axes['G'].spines['top'].set_color('none')\n",
- "axes['G'].spines['bottom'].set_color('none')\n",
- "axes['G'].yaxis.set_ticks_position(\"none\")\n",
- "axes['G'].xaxis.set_ticks_position(\"none\")\n",
- "axes['G'].set_xticks([])\n",
- "axes['G'].set_yticks([])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "90ae8f4c",
- "metadata": {
- "jp-MarkdownHeadingCollapsed": true,
- "tags": []
- },
- "source": [
- "### 4.6 Time series of population- and area-averaged firing rates <a class=\"anchor\" id=\"section_4_6\"></a>\n",
- "Area-averaged firing rates, shown as raw binned spike histograms with 1ms bin width (gray) and convolved histograms, with aGaussian kernel (black) of optimal width"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "94b0b0c4-d70b-4c49-8b5d-e1ca75f0ccf4",
- "metadata": {},
- "outputs": [],
- "source": [
- "\"\"\"\n",
- "Calculate time series of population- and area-averaged firing rates.\n",
- "Uses ah.pop_rate_time_series.\n",
- "If the rates have previously been stored with the\n",
- "same parameters, they are loaded from file.\n",
- "\n",
- "\n",
- "Parameters\n",
- "----------\n",
- "t_min : float, optional\n",
- " Minimal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to 500 ms.\n",
- "t_max : float, optional\n",
- " Maximal time in ms of the simulation to take into account\n",
- " for the calculation. Defaults to the simulation time.\n",
- "areas : list, optional\n",
- " Which areas to include in the calculcation.\n",
- " Defaults to all loaded areas.\n",
- "pops : list or {'complete'}, optional\n",
- " Which populations to include in the calculation.\n",
- " If set to 'complete', all populations the respective areas\n",
- " are included. Defaults to 'complete'.\n",
- "kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional\n",
- " Specifies the kernel to be convolved with the spike histogram.\n",
- " Defaults to 'binned', which corresponds to no convolution.\n",
- "resolution: float, optional\n",
- " Width of the convolution kernel. Specifically it correponds to:\n",
- " - 'binned' : bin width of the histogram\n",
- " - 'gauss_time_window' : sigma\n",
- " - 'alpha_time_window' : time constant of the alpha function\n",
- " - 'rect_time_window' : width of the moving rectangular function\n",
- "\"\"\"\n",
- "A.create_rate_time_series(t_max=1000.)\n",
- "# M.analysis.save()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "28796b50-2500-4944-97ae-fbb506a557fb",
- "metadata": {},
- "outputs": [],
- "source": [
- "# time series of firing rates\n",
- "rate_time_series = {}\n",
- "for area in areas:\n",
- " fn = os.path.join(data_path, label,\n",
- " 'Analysis',\n",
- " 'rate_time_series_full',\n",
- " 'rate_time_series_full_{}.npy'.format(area))\n",
- " rate_time_series[area] = np.load(fn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "65e4be2d-5e8b-4daa-a37c-07f1be629f80",
- "metadata": {},
- "outputs": [],
- "source": [
- "# time series of firing rates convolved with a kernel\n",
- "rate_time_series_auto_kernel = {}\n",
- "for area in areas:\n",
- " fn = os.path.join(data_path, label,\n",
- " 'Analysis',\n",
- " 'rate_time_series_auto_kernel',\n",
- " 'rate_time_series_auto_kernel_{}.npy'.format(area))\n",
- " rate_time_series_auto_kernel[area] = np.load(fn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4460d823-543a-482b-8ef1-a049e5837af4",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(\"Plotting rate time series\")\n",
- "pos = axes['G'].get_position()\n",
- "ax = []\n",
- "h = pos.y1 - pos.y0\n",
- "w = pos.x1 - pos.x0\n",
- "ax.append(pl.axes([pos.x0, pos.y0, w, 0.28 * h]))\n",
- "ax.append(pl.axes([pos.x0, pos.y0 + 0.33 * h, w, 0.28 * h]))\n",
- "ax.append(pl.axes([pos.x0, pos.y0 + 0.67 * h, w, 0.28 * h]))\n",
- "\n",
- "colors = ['0.5', '0.3', '0.0']\n",
- "\n",
- "t_min = 500.\n",
- "t_max = 10500.\n",
- "time = np.arange(500., t_max)\n",
- "for i, area in enumerate(areas[::-1]):\n",
- " ax[i].spines['right'].set_color('none')\n",
- " ax[i].spines['top'].set_color('none')\n",
- " ax[i].yaxis.set_ticks_position(\"left\")\n",
- " ax[i].xaxis.set_ticks_position(\"none\")\n",
- "\n",
- " binned_spikes = rate_time_series[area][np.where(\n",
- " np.logical_and(time >= t_min, time < t_max))]\n",
- " ax[i].plot(time, binned_spikes, color=colors[0], label=area)\n",
- " rate = rate_time_series_auto_kernel[area]\n",
- " ax[i].plot(time, rate, color=colors[2], label=area)\n",
- " ax[i].set_xlim((500., t_max))\n",
- "\n",
- " ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)\n",
- "\n",
- " if i > 0:\n",
- " ax[i].spines['bottom'].set_color('none')\n",
- " ax[i].set_xticks([])\n",
- " ax[i].set_yticks([0., 30.])\n",
- " else:\n",
- " ax[i].set_xticks([1000., 5000., 10000.])\n",
- " ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])\n",
- " ax[i].set_yticks([0., 5.])\n",
- " if i == 1:\n",
- " ax[i].set_ylabel(r'Rate (spikes/s)')\n",
- "\n",
- "ax[0].set_xlabel('Time (s)', labelpad=-0.05)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ef74ca3e-98dc-49c9-a4a0-2c640e29b1d9",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "EBRAINS-23.02",
- "language": "python",
- "name": "ebrains-23.02"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.11"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/README-checkpoint.md b/.ipynb_checkpoints/README-checkpoint.md
deleted file mode 100644
index 6990456c365492f93ad17b34d6ea434f3bbc6e61..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/README-checkpoint.md
+++ /dev/null
@@ -1,298 +0,0 @@
-# Multi-scale spiking network model of macaque visual cortex
-[](https://www.python.org) <a href="http://www.nest-simulator.org"> <img src="https://github.com/nest/nest-simulator/blob/master/doc/logos/nest-simulated.png" alt="NEST simulated" width="50"/></a> [](https://creativecommons.org/licenses/by-nc-sa/4.0/)
-
-
-
-This code implements the spiking network model of macaque visual cortex developed
-at the Institute of Neuroscience and Medicine (INM-6), Research Center Jülich.
-The model has been documented in the following publications:
-
-1. Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ
- Multi-scale account of the network structure of macaque visual cortex
- Brain Structure and Function (2018), 223: 1409 [https://doi.org/10.1007/s00429-017-1554-4](https://doi.org/10.1007/s00429-017-1554-4)
-
-2. Schuecker J, Schmidt M, van Albada SJ, Diesmann M & Helias M (2017)
- Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome.
- PLOS Computational Biology, 13(2): e1005179. [https://doi.org/10.1371/journal.pcbi.1005179](https://doi.org/10.1371/journal.pcbi.1005179)
-
-3. Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018)
- A multi-scale layer-resolved spiking network model of
- resting-state dynamics in macaque cortex. PLOS Computational Biology, 14(9): e1006359. [https://doi.org/10.1371/journal.pcbi.1006359](https://doi.org/10.1371/journal.pcbi.1006359)
-
-The code in this repository is self-contained and allows one to
-reproduce the results of all three papers.
-
-A video providing a brief introduction of the model and the code in this repository can be found [here](https://www.youtube.com/watch?v=NGAqe78vmHY&t=22s).
-
-## Try it on EBRAINS
-
-Want to start using or simply run the model? Click the button below.<br>
-**Please note**: make sure you check and follow our [User instructions](https://github.com/didi-hou/multi-area-model/tree/didihou#user-instructions), especially if you plan to make and save the changes, or if you simply need step-by-step instructions.<br>
-<a href="https://lab.ebrains.eu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FINM-6%2Fmulti-area-model&branch=master&urlpath=lab%2Ftree%2Fmulti-area-model%2Fmulti-area-model.ipynb&branch=master"> <img src="https://nest-simulator.org/TryItOnEBRAINS.png" alt="Try it on EBRAINS" width="260"/></a>
-
---------------------------------------------------------------------------------
-
-### User instructions
-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.<br>
-* Prerequisites: an [EBRAINS](https://www.ebrains.eu/) account. If you don’t have it yet, register at [register page](https://iam.ebrains.eu/auth/realms/hbp/protocol/openid-connect/registrations?response_type=code&client_id=xwiki&redirect_uri=https://wiki.ebrains.eu). Please note: registering an EBRAINS account requires an institutional email.<br>
-* If you plan to only run the model, instead of making and saving changes you made, go to [Try it on EBRAINS](https://github.com/didi-hou/MAM2EBRAINS#try-it-on-ebrains-1); Shold you want to adjust the parameters, thereafter save the changes you made, go to [Fork the repostory and save your changes](https://github.com/didi-hou/MAM2EBRAINS#fork-the-repostory-and-save-your-changes).
-
-#### Try it on EBRAINS
-1. Click [Try it on EBRAINS](https://lab.ebrains.eu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FINM-6%2Fmulti-area-model&branch=master&urlpath=lab%2Ftree%2Fmulti-area-model%2Fmulti-area-model.ipynb&branch=master). If any error happens during the following process, please close the browser tab and restart the [User instruction](https://lab.ebrains.eu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FINM-6%2Fmulti-area-model&branch=master&urlpath=lab%2Ftree%2Fmulti-area-model%2Fmulti-area-model.ipynb&branch=master) process again.
-2. On the `Lab Execution Site` page, select a computing center from the given list.
-3. If you’re using EBRAINS for the first time, click `Sign in with GenericOAuth2` to sign in on EBRAINS. To do this, you need an EBRAINS account.
-4. Once signed in, on the `Server Options` page, choose `Official EBRAINS Docker image 23.06 for Collaboratory.Lab (recommended)`, and click `start`.
-5. Once succeeded, you’re now at a Jupyter Notebook named `multi-area-model.ipynb`. Click the field that displays `Python 3 (ipykernel)` in the upper right corner and switch the `kernel` to `EBRAINS-23.02`.
-6. Congratulations! Now you can run the model. Enjoy!<br> To run the model, click the `Run` on the title bar and choose `Run All Cells`. It takes several minutes until you get all results.<br>
-**Please note**: every time you click the `Try it on EBRAINS` button, the repository is loaded into your home directory on EBRAINS Lab and it overrides your old repository with the same name. Therefore, make sure you follow the [Fork the repostory and save your changes](https://github.com/didi-hou/MAM2EBRAINS#fork-the-repostory-and-save-your-changes) if you makes changes and want to save them.
-
-#### Fork the repostory and save your changes
-With limited resources, EBRAINS Lab regularly deletes and cleans data loaded on the server. This means the repository on the EBRAINS Lab will be deleted automatically after a period of time. To save changes you made, make sure you fork the repository to your own GitHub, then clone it to the EBRAINS Lab, and do git commits and push changes.
-1. Go to our [Multi-area model](https://github.com/INM-6/multi-area-model) under INM-6, create a fork by clicking the `Fork`. In the `Owner` field, choose your username and click `Create fork`. Copy the address of your fork by clicking on `Code`, `HTTPS`, and then the copy icon.
-2. Go to [EBRAINS Lab](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` icon on the left toolbar, click `Clone a Repository` and paste the address of your fork.
-4. Now your forked repository of multi-area model is loaded on the server. Enter the folder `multi-area-model` and open the notebook `multi-area-model.ipynb`.
-5. Click the field that displays `Python 3 (ipykernel)` in the upper right corner and switch the `kernel` to `EBRAINS-23.02`.
-6. Run the notebook! To run the model, click the `Run` on the title bar and choose `Run All Cells`. It takes several minutes until you get all results.
-7. You can modify the exposed parameters before running the model. If you want to save the changes you made, press `Control+S` on the keyboard, click the `Git` icon on the most left toolbar, do git commits and push.<br>
-To commit, on `Changed` bar, click the `+` icon, and filled a comment in the `Summary (Control+Enter to commit)` at lower left corner and click `COOMMIT`.<br>
-To push, click the `Push commited changes` icon at upper left which is looks like cloud, you may be asked to enter your username and password (user name is your GitHUb username, password should be [Personal access tokens](https://github.com/settings/tokens) you generated on your GitHUb account, make sure you select the `repo` option when you generate the token), enter them and click `Ok`.
-8. If you would like to contribute to our model or bring your ideas to us, you’re most welcomed to contact us. It’s currently not possible to directly make changes to the original repository, since it is connected to our publications.
-
-## Python framework for the multi-area model
-
-The entire framework is summarized in the figure below:
-
-
-We separate the structure of the network (defined by population sizes,
-synapse numbers/indegrees etc.) from its dynamics (neuron model,
-neuron parameters, strength of external input, etc.). The complete set
-of default parameters for all components of the framework is defined
-in `multiarea_model/default_params.py`.
-
-A description of the requirements for the code can be found at the end of this README.
-
---------------------------------------------------------------------------------
-
-### Preparations
-
-To start using the framework, the user has to define a few environment variables
-in a new file called `config.py`. The file `config_template.py` lists the required
-environment variables that need to specified by the user.
-
-Furthermore, please add the path to the repository to your PYTHONPATH:
-
-`export PYTHONPATH=/path/to/repository/:$PYTHONPATH`.
-
-
---------------------------------------------------------------------------------
-
-`MultiAreaModel`
-
-The central class that initializes the network and contains all
-information about population sizes and network connectivity. This
-enables reproducing all figures in [1]. Network parameters only
-refer to the structure of the network and ignore any information on
-its dynamical simulation or description via analytical theory.
-
-`Simulation`
-
-This class can be initialized by `MultiAreaModel` or as standalone and
-takes simulation parameters as input. These parameters include, e.g.,
-neuron and synapses parameters, the simulated biological time and also
-technical parameters such as the number of parallel MPI processes and
-threads. The simulation uses the network simulator NEST
-(https://www.nest-simulator.org). For the simulations in [2, 3], we
-used NEST version 2.8.0. The code in this repository runs with a
-later release of NEST, version 2.14.0, as well as NEST 3.0.
-
-`Theory`
-
-This class can be initialized by `MultiAreaModel` or as standalone and
-takes simulation parameters as input. It provides two main features:
-- predict the stable fixed points of the system using mean-field theory and characterize them (for instance by computing the gain matrix).
-- via the script `stabilize.py`, one can execute the stabilization method described in [2] on a network instance. Please see `figures/SchueckerSchmidt2017/stabilization.py` for an example of running the stabilization.
-
-`Analysis`
-
-This class allows the user to load simulation data and perform some
-basic analysis and plotting.
-
-
-## Analysis and figure scripts for [1-3]
-
-The `figures` folder contains subfolders with all scripts necessary to produce
-the figures from [1-3]. If Snakemake (Köster J & Rahmann S, Bioinformatics (2012) 28(19): 2520-2522)
-is installed, the figures can be produced by executing
-`snakemake` in the respective folder, e.g.:
-
- cd figures/Schmidt2018/
- snakemake
-
-Note that it can sometimes be necessary to execute `snakemake --touch` to avoid unnecessary rule executions. See https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#flag-files for more details.
-
-## Running a simulation
-
-The files `run_example_downscaled.py` and `run_example_fullscale.py` provide examples. A simple simulation can be run in the following way:
-
-1. Define custom parameters.
- See `multi_area_model/default_params.py` for a full list of parameters. All parameters can be customized.
-
-2. Instantiate the model class together with a simulation class instance.
-
- M = MultiAreaModel(custom_params, simulation=True, sim_spec=custom_simulation_params)
-
-3. Start the simulation.
-
- M.simulation.simulate()
-
-
-Typically, a simulation of the model will be run in parallel on a compute cluster.
-The files `start_jobs.py` and `run_simulation.py` provide the necessary framework
-for doing this in an automated fashion.
-The procedure is similar to a simple simulation:
-1. Define custom parameters
-
-2. Instantiate the model class together with a simulation class instance.
-
- M = MultiAreaModel(custom_params, simulation=True, sim_spec=custom_simulation_params)
-3. Start the simulation.
- Call `start_job` to create a job file using the `jobscript_template` from the configuration file
- and submit it to the queue with the user-defined `submit_cmd`.
-
-Be aware that, depending on the chosen parameters and initial conditions, the network can enter a high-activity state, which slows down the simulation drastically and can cost a significant amount of computing resources.
-
-## Extracting connectivity & neuron numbers
-
-First, the model class has to be instantiated:
-
-1. Define custom parameters.
- See `multi_area_model/default_params.py` for a full list of parameters. All parameters can be customized.
-
-2. Instantiate the model class.
-
- from multiarea_model import MultiAreaModel
- M = MultiAreaModel(custom_params)
-
-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). To extract e.g.
-the neuron numbers into a yaml file execute
-
- import yaml
- with open('neuron_numbers.yaml', 'w') as f:
- yaml.dump(M.N, f, default_flow_style=False)
-
-Alternatively, you can have a look at the data with `print(M.N)`.
-
-## Simulation modes
-
-The multi-area model can be run in different modes.
-
-1. Full model
-
- Simulating the entire networks with all 32 areas with default
- connectivity as defined in `default_params.py`.
-
-2. Down-scaled model
-
- Since simulating the entire network with approx. 4.13 million neurons and 24.2 billion
- synapses requires a large amount of resources, the user has the option to scale down
- the network in terms of neuron numbers and synaptic indegrees (number of synapses
- per receiving neuron).
- This can be achieved by setting the parameters `N_scaling` and `K_scaling` in `network_params`
- to values smaller than 1. In general, this will affect the dynamics of the network.
- To approximately preserve the population-averaged spike rates, one can specify a set of target rates
- that is used to scale synaptic weights and apply an additional external DC input.
-
-3. Subset of the network
-
- You can choose to simulate a subset of the 32 areas specified by the `areas_simulated`
- parameter in the `sim_params`. If a subset of areas is simulated, one has different options for how to replace the rest of the network set by the `replace_non_simulated_areas` parameter:
- - `hom_poisson_stat`: all non-simulated areas are replaced by Poissonian spike trains with the
- same rate as the stationary background input (`rate_ext` in `input_params`).
- - `het_poisson_stat`: all non-simulated areas are replaced by Poissonian spike trains with
- population-specific stationary rate stored in an external file.
- - `current_nonstat`: all non-simulated areas are replaced by stepwise constant currents with
- population-specific, time-varying time series defined in an external file.
-
-4. Cortico-cortical connections replaced
-
- In addition, it is possible to replace the cortico-cortical
- connections between simulated areas with the options
- `het_poisson_stat` or `current_nonstat`. This mode can be used with
- the full network of 32 areas or for a subset of them (therefore
- combining this mode with the previous mode 'Subset of the
- network').
-
-## Test suite
-
-The `tests/` folder holds a test suite that tests different aspects of
-network model initalization and mean-field calculations. It can be
-conveniently run by executing `pytest` in the `tests/` folder:
-
- cd tests/
- pytest
-
-
-## Requirements
-Python 3, python\_dicthash ([https://github.com/INM-6/python-dicthash](https://github.com/INM-6/python-dicthash)),
-correlation\_toolbox ([https://github.com/INM-6/correlation-toolbox](https://github.com/INM-6/correlation-toolbox)),
-pandas, numpy, nested_dict, matplotlib (2.1.2), scipy, pytest, NEST 2.14.0 or NEST 3.0
-
-Optional: seaborn, Sumatra
-
-To install the required packages with pip, execute:
-
-`pip install -r requirements.txt`
-
-Note that NEST needs to be installed separately, see <http://www.nest-simulator.org/installation/>.
-
-In addition, reproducing the figures of [1] requires networkx, python-igraph, pycairo and pyx. To install these additional packages, execute:
-
-`pip install -r figures/Schmidt2018/additional_requirements.txt`
-
-In addition, Figure 7 of [1] requires installing the `infomap` package to perform the map equation clustering. See <http://www.mapequation.org/code.html> for all necessary information.
-
-Similarly, reproducing the figures of [3] requires statsmodels, networkx, pyx, python-louvain, which can be installed by executing:
-
-`pip install -r figures/Schmidt2018_dyn/additional_requirements.txt`
-
-The SLN fit in `multiarea_model/data_multiarea/VisualCortex_Data.py` and `figures/Schmidt2018/Fig5_cc_laminar_pattern.py` requires an installation of R and the R library `aod` (<http://cran.r-project.org/package=aod>). Without R installation, both scripts will directly use the resulting values of the fit (see Fig. 5 of [1]).
-
-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>).
-
-## Contributors
-
-All authors of the publications [1-3] made contributions to the
-scientific content. The code base was written by Maximilian Schmidt,
-Jannis Schuecker, and Sacha van Albada with small contributions from
-Moritz Helias. Testing and review was supported by Alexander van
-Meegen.
-
-## Citation
-
-If you use this code, we ask you to cite the appropriate papers in your publication. For the multi-area model itself, please cite [1] and [3]. If you use the mean-field theory or the stabilization method, please cite [2] in addition. We provide bibtex entries in the file called `CITATION`.
-
-If you have questions regarding the code or scientific content, please create an issue on github.
-
-<img src="https://github.com/nest/nest-simulator/blob/master/doc/logos/nest-simulated.png" alt="NEST simulated" width="200"/> <img src="https://raw.githubusercontent.com/INM-6/multi-area-model/master/HBP_logo.png" alt="HBP logo" width="200"/> <img src="https://raw.githubusercontent.com/INM-6/multi-area-model/master/FZJ_logo.png" alt="FZJ logo" width="200"/>
-
-## Acknowledgements
-
-We thank Sarah Beul for discussions on cortical architecture; Kenneth Knoblauch for sharing his R code
-for the SLN fit (`multiarea_model/data_multiarea/bbalt.R`); and Susanne Kunkel for help with creating Fig. 3a of [1] (`figures/Schmidt2018/Fig3_syntypes.eps`).
-
-This work was supported by the Helmholtz Portfolio Supercomputing and
-Modeling for the Human Brain (SMHB), the European Union 7th Framework
-Program (Grant 269921, BrainScaleS and 604102, Human Brain Project,
-Ramp up phase) and European Unions Horizon 2020 research and
-innovation program (Grants 720270 and 737691, Human Brain Project, SGA1 and SGA2), the
-Jülich Aachen Research Alliance (JARA), the Helmholtz young
-investigator group VH-NG-1028,and the German Research Council (DFG
-Grants SFB936/A1,Z1 and TRR169/A2) and computing time granted by the
-JARA-HPC Ver- gabegremium and provided on the JARA-HPC Partition part
-of the supercomputer JUQUEEN (Jülich Supercomputing Centre 2015) at
-Forschungszentrum Jülich (VSR Computation Time Grant JINB33), and Priority
-Program 2041 (SPP 2041) "Computational Connectomics" of the German Research
-Foundation (DFG).
diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
deleted file mode 100644
index c83d453d721c55b5e0bc4534ed7a3897da774ad7..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ /dev/null
@@ -1,851 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "b1331599",
- "metadata": {
- "tags": []
- },
- "source": [
- "# Down-scaled multi-area model"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "edec8345-aec1-419e-b9e3-7f612aff8262",
- "metadata": {},
- "source": [
- "<img src=\"model_construction.png\" alt=\"Model overview\" width=\"1000\"/>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f4a649cc-3b68-49e4-b2b6-6f29f13a6d9c",
- "metadata": {},
- "source": [
- "The code in this notebook implements the down-scaled version of spiking network model of macaque visual cortex developed at the Institute of Neuroscience and Medicine (INM-6), Research Center Jülich. The full-scale model has been documented in the following publications:\n",
- "\n",
- "1. Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ\n",
- " Multi-scale account of the network structure of macaque visual cortex\n",
- " Brain Structure and Function (2018), 223: 1409 [https://doi.org/10.1007/s00429-017-1554-4](https://doi.org/10.1007/s00429-017-1554-4)\n",
- "\n",
- "2. Schuecker J, Schmidt M, van Albada SJ, Diesmann M & Helias M (2017)\n",
- " Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome.\n",
- " PLOS Computational Biology, 13(2): e1005179. [https://doi.org/10.1371/journal.pcbi.1005179](https://doi.org/10.1371/journal.pcbi.1005179)\n",
- "\n",
- "3. Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018)\n",
- " A multi-scale layer-resolved spiking network model of\n",
- " resting-state dynamics in macaque cortex. PLOS Computational Biology, 14(9): e1006359. [https://doi.org/10.1371/journal.pcbi.1006359](https://doi.org/10.1371/journal.pcbi.1006359)\n",
- "<br>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b952d0ea",
- "metadata": {
- "tags": []
- },
- "source": [
- "#### Notebook structure <a class=\"anchor\" id=\"toc\"></a>\n",
- "* [S0. Configuration](#section_0)\n",
- "* [S1. Parameterization](#section_1)\n",
- " * [1.1. Parameters to tune](#section_1_1)\n",
- " * [1.2. Default parameters](#section_1_2)\n",
- "* [S2. Multi-Area Model Instantiation and Simulation](#section_2)\n",
- " * [2.1. Instantiate a multi-area model](#section_2_1)\n",
- " * [2.2. Predict firing rates from theory](#section_2_2)\n",
- " * [2.3. Extract and visualize interareal connectivity](#section_2_3)\n",
- " * [2.4. Run a simulation](#section_2_4)\n",
- "* [S3. Simulation Results Visualization](#section_3) \n",
- " * [3.1. Instantaneous and mean firing rate across all populations](#section_3_1)\n",
- " * [3.2. Resting state plots](#section_3_2)\n",
- " * [3.3. Time-averaged population rates](#section_3_3)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d782e527",
- "metadata": {
- "tags": []
- },
- "source": [
- "## S0. Configuration <a class=\"anchor\" id=\"section_0\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "9d6cc7d9-3110-4d96-9f9a-9ec7dee6d145",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create config file\n",
- "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": 2,
- "id": "96517739",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- " -- N E S T --\n",
- " Copyright (C) 2004 The NEST Initiative\n",
- "\n",
- " Version: 3.5\n",
- " Built: Jul 12 2023 06:25:27\n",
- "\n",
- " This program is provided AS IS and comes with\n",
- " NO WARRANTY. See the file LICENSE for details.\n",
- "\n",
- " Problems or suggestions?\n",
- " Visit https://www.nest-simulator.org\n",
- "\n",
- " Type 'nest.help()' to find out more about NEST.\n",
- "\n"
- ]
- }
- ],
- "source": [
- "%matplotlib inline\n",
- "import numpy as np\n",
- "import os\n",
- "import nest\n",
- "import json\n",
- "import sys\n",
- "from IPython.display import display, HTML\n",
- "import warnings\n",
- "\n",
- "sys.path.append('./figures/MAM2EBRAINS')\n",
- "\n",
- "from multiarea_model import MultiAreaModel\n",
- "from M2E_visualize_interareal_connectivity import visualize_interareal_connectivity\n",
- "from M2E_visualize_instantaneous_and_mean_firing_rates import plot_instan_mean_firing_rate\n",
- "from M2E_visualize_resting_state import plot_resting_state\n",
- "from M2E_visualize_time_ave_pop_rates import plot_time_averaged_population_rates\n",
- "from config import base_path, data_path"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "7e07b0d0",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "%%capture captured\n",
- "!pip install nested_dict dicthash"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "1d440c07-9b69-4e52-8573-26b13493bc5a",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "<style>\n",
- "table {float:left}\n",
- "</style>\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Jupyter notebook display format setting\n",
- "style = \"\"\"\n",
- "<style>\n",
- "table {float:left}\n",
- "</style>\n",
- "\"\"\"\n",
- "display(HTML(style))\n",
- "\n",
- "warnings.filterwarnings('ignore')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "27160ba8",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "df83f5ea-1c4b-44d3-9926-01786aa46e14",
- "metadata": {
- "tags": []
- },
- "source": [
- "## S1. Parameterization <a class=\"anchor\" id=\"section_1\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "30655817",
- "metadata": {},
- "source": [
- "### 1.1. Parameters to tune <a class=\"anchor\" id=\"section_1_1\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4f67c1ba",
- "metadata": {},
- "source": [
- "|Parameter|Default value|Value range/options|Value assigned|Description|\n",
- "|:-------:|:-----------:|:-----------------:|:------------:|:---------:|\n",
- "|scale_down_to|1. |(0, 1.0] |0.005 |$^1$ |\n",
- "|cc_weights_factor|1. |[1.0, 2.5] |1. |$^2$ |\n",
- "|areas_simulated|complete_area_list|Sublists of complete_area_list|complete_area_list|$^3$|\n",
- "|replace_non_simulated_areas|None|None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat'|'het_poisson_stat'|$^4$ |"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a2161477",
- "metadata": {},
- "source": [
- "1. `scale_down_to` is the down-scaling factor that defines the ratio by which the full-scale multi-area model is reduced to a model with fewer neurons and indegrees. This reduction is essential to enable simulation on machines with limited computational power, ensuring that simulation results can be obtained in a relatively shorter timeframe. <br> If the value is `scale_down_to = 1.`, the full-scale network will be simulated. <br> In the pre-set downscale version, it is set to `scale_down_to = 0.005`, This setting reduces both the number of neurons and indegrees to 0.5 % of their full-scale counterparts, facilitating simulation on a typical local machine. <br> **Warning**: This will not yield reasonable dynamical results from the network and is only meant to demonstrate the simulation workflow <br> \n",
- "\n",
- "2. `cc_weights_factor` is the scaling factor that controls the cortico-cortical synaptic strength. <br> By default it's set as `1.0`, where the inter-area synaptic strength is the same as the intra-areal. <br> **Important**: This factor changes the network activity from ground state to metastable state. <br>\n",
- "\n",
- "3. `areas_simulated` specifies the cortical areas to be included in the simulation process. Its default value is `complete_area_list` meaning all the areas in the complete_area_list will be simulated. The value assigned to `areas_simulated` can be any sublist of the list below:\n",
- "```python\n",
- "complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']\n",
- "```\n",
- "<br>\n",
- "\n",
- "4. `replace_non_simulated_areas` defines how non-simulated areas will be replaced. <br> When all areas are included, it is set as `None` by default. <br> Other options are: `'hom_poisson_stat'`, `'het_poisson_stat'`, and `'het_current_nonstat'`.<br> `'hom_poisson_stat'` replaces the non-simulated areas by Poisson sources with the same global rate `rate_ext`. The `'het_poisson_stat'` and `'het_current_nonstat'` options use the loaded specific rates from `'replace_cc_input_source'`, which contains the area-specific firing rates of our full scale simulation results. The difference between them is that `'het_poisson_stat'` replaces the non-simulated areas by Poisson spike trains and `'het_current_nonstat'` replaces it by a time-varying current input."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "60265d52",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Downscaling factor\n",
- "# Value range/options: (0, 1.]\n",
- "# Value assigned: 0.005\n",
- "scale_down_to = 0.005 # Change it to 1. for running the fullscale network\n",
- "\n",
- "# Scaling factor for cortico-cortical connections (chi) \n",
- "# Value range/options: [1., 2.5]\n",
- "# Value assigned: 1.0\n",
- "cc_weights_factor = 1.0\n",
- "\n",
- "# Cortical areas included in the simulation\n",
- "# Value range/options: any sublist of complete_area_list\n",
- "# where complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']\n",
- "# Value assigned: complete_area_list\n",
- "# Note: at this pre-released multi-area model v2.0.0, the areas_similated has to be complete_area_list\n",
- "areas_simulated = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']\n",
- "\n",
- "# Firing rates used to replace the non-simulated areas\n",
- "# Value range/options: None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat'\n",
- "# Value assigned: 'het_poisson_stat'\n",
- "replace_non_simulated_areas = 'het_poisson_stat'"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "de11b07f",
- "metadata": {},
- "source": [
- "### 1.2. Default parameters <a class=\"anchor\" id=\"section_1_2\"></a>\n",
- "We try our best not to confuse users with too many parameters. However, if you want to change more parameters and explore the model, you can do so by passing a dictionary to the `default_params` argument of the `MultiAreaModel` class. (*NOTE: it should may be moved to the default parameters file in the future.*)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "6e4bed8d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Decide the parameter replace_cc_input_source\n",
- "complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']\n",
- "\n",
- "if areas_simulated != complete_area_list and replace_non_simulated_areas == None:\n",
- " raise Exception(\"When not all areas are simulated, a not None value should be assigned to replace_non_simulated_areas!\")\n",
- "elif replace_non_simulated_areas == 'hom_poisson_stat':\n",
- " replace_cc_input_source = None\n",
- "else:\n",
- " replace_cc_input_source = 'tests/fullscale_rates.json'\n",
- "\n",
- "# Connection parameters\n",
- "conn_params = {\n",
- " 'replace_non_simulated_areas': replace_non_simulated_areas, # Whether to replace non-simulated areas by Poisson sources with the same global rate, by default: None\n",
- " 'g': -11., # It sets the relative inhibitory synaptic strength, by default: -16.\n",
- " 'K_stable': 'K_stable.npy', # Whether to apply the stabilization method of Schuecker, Schmidt et al. (2017), by default: None\n",
- " 'fac_nu_ext_TH': 1.2, # Increase the external input to 2/3E and 5E in area TH\n",
- " 'fac_nu_ext_5E': 1.125, # Increase the external Poisson indegree onto 5E\n",
- " 'fac_nu_ext_6E': 1.41666667, # Increase the external Poisson indegree onto 6E\n",
- " 'av_indegree_V1': 3950., # Adjust the average indegree in V1 based on monkey data\n",
- " 'replace_cc_input_source': replace_cc_input_source\n",
- "}\n",
- "\n",
- "# Input parameters\n",
- "input_params = {\n",
- " 'rate_ext': 10. # Rate of the Poissonian spike generator (in spikes/s)\n",
- "} \n",
- "\n",
- "# Neuron parameters\n",
- "neuron_params = {\n",
- " 'V0_mean': -150., # Mean for the distribution of initial membrane potentials, by default: -100.\n",
- " 'V0_sd': 50. # Standard deviation for the distribution of initial membrane potentials, by default: 50.\n",
- "}\n",
- "\n",
- "# Network parameters\n",
- "network_params = {\n",
- " 'N_scaling': scale_down_to, # Scaling of population sizes, by default: 1.\n",
- " 'K_scaling': scale_down_to, # Scaling of indegrees, by default: 1.\n",
- " 'fullscale_rates': 'tests/fullscale_rates.json', # Absolute path to the file holding full-scale rates for scaling synaptic weights, by default: None\n",
- " 'input_params': input_params, # Input parameters\n",
- " 'connection_params': conn_params, # Connection parameters\n",
- " 'neuron_params': neuron_params # Neuron parameters\n",
- "} \n",
- "\n",
- "# Simulation parameters\n",
- "sim_params = {\n",
- " 'areas_simulated': areas_simulated,\n",
- " 't_sim': 2000., # Simulated time (in ms), by default: 10.0\n",
- " 'num_processes': 1, # The number of MPI processes, by default: 1\n",
- " 'local_num_threads': 1, # The number of threads per MPI process, by default: 1\n",
- " 'recording_dict': {'record_vm': False},\n",
- " 'rng_seed': 1 # global random seed\n",
- "}\n",
- "\n",
- "# Theory paramters (theory_params)\n",
- "theory_params = {\n",
- " 'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01\n",
- "} "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1472e9c5",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "de4a6703",
- "metadata": {
- "tags": []
- },
- "source": [
- "## S2. Multi-Area Model Instantiation and Simulation <a class=\"anchor\" id=\"section_2\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1fd58841",
- "metadata": {
- "tags": []
- },
- "source": [
- "### 2.1. Instantiate a multi-area model <a class=\"anchor\" id=\"section_2_1\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "ab25f9f8",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Error in library(\"aod\") : there is no package called ‘aod’\n",
- "Execution halted\n"
- ]
- }
- ],
- "source": [
- "%%capture captured\n",
- "M = MultiAreaModel(network_params, \n",
- " simulation=True,\n",
- " sim_spec=sim_params,\n",
- " theory=True,\n",
- " theory_spec=theory_params)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "91649c30",
- "metadata": {},
- "source": [
- "### 2.2. Predict firing rates from theory <a class=\"anchor\" id=\"section_2_2\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "6a7ddf0e",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Iteration: 0\n",
- "Mean-field theory predicts an average firing rate of 29.588 spikes/s across all populations.\n"
- ]
- }
- ],
- "source": [
- "p, r = M.theory.integrate_siegert()\n",
- "print(\"Mean-field theory predicts an average \"\n",
- " \"firing rate of {0:.3f} spikes/s across all populations.\".format(np.mean(r[:, -1])))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2062ddf3",
- "metadata": {},
- "source": [
- "### 2.3. Extract and visualize interareal connectivity <a class=\"anchor\" id=\"section_2_3\"></a>"
- ]
- },
- {
- "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). <br>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "6316ac24",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Neuron numbers\n",
- "\n",
- "# Dictionary of neuron numbers\n",
- "# print(M.N)\n",
- "\n",
- "# Array of neuron numbers\n",
- "# (M.N_vec)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "8408d463-557b-481b-afc1-5fbbbd67306d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Indegrees\n",
- "\n",
- "# Dictionary of nodes indegrees organized as:\n",
- "# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: indegree_values}}}}\n",
- "# M.K\n",
- "\n",
- "# Array of nodes indegrees\n",
- "# M.K_matrix.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "445a722a",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Synapses\n",
- "\n",
- "# 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\n",
- "\n",
- "# Array of \n",
- "# M.syn_matrix"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "05512922-26e5-425f-90a4-0df7c2279ccf",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Initializing network from dictionary.\n",
- "RAND_DATA_LABEL 7342\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Error in library(\"aod\") : there is no package called ‘aod’\n",
- "Execution halted\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
- "\n",
- "\n",
- "========================================\n",
- "Customized parameters\n",
- "--------------------\n",
- "{}\n",
- "========================================\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1080x476.769 with 4 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "visualize_interareal_connectivity(M)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bae85d86-157c-47a2-9826-860b410a440e",
- "metadata": {},
- "source": [
- "Comparable figure in our publications: <br>\n",
- "1. Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ <br>\n",
- " Multi-scale account of the network structure of macaque visual cortex\n",
- " Brain Structure and Function (2018), 223: 1409 [https://doi.org/10.1007/s00429-017-1554-4](https://doi.org/10.1007/s00429-017-1554-4) <br>\n",
- " **Fig. 4 D Area-level connectivity of the model, based on data in a–c, expressed as relative indegrees for each target area**"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e67f37e9-ec8d-4bb1-bd21-45e966f47ab6",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0c1cad59-81d0-4e24-ac33-13c4ca8c6dec",
- "metadata": {},
- "source": [
- "### 2.4. Run a simulation <a class=\"anchor\" id=\"section_2_4\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "15778e9c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Prepared simulation in 0.00 seconds.\n",
- "Rank 0: created area V1 with 0 local nodes\n",
- "Memory after V1 : 1515.89 MB\n",
- "Rank 0: created area V2 with 0 local nodes\n",
- "Memory after V2 : 1542.60 MB\n",
- "Rank 0: created area VP with 0 local nodes\n",
- "Memory after VP : 1571.73 MB\n",
- "Rank 0: created area V3 with 0 local nodes\n",
- "Memory after V3 : 1600.09 MB\n",
- "Rank 0: created area V3A with 0 local nodes\n",
- "Memory after V3A : 1619.91 MB\n",
- "Rank 0: created area MT with 0 local nodes\n",
- "Memory after MT : 1645.56 MB\n",
- "Rank 0: created area V4t with 0 local nodes\n",
- "Memory after V4t : 1670.48 MB\n",
- "Rank 0: created area V4 with 0 local nodes\n",
- "Memory after V4 : 1697.43 MB\n",
- "Rank 0: created area VOT with 0 local nodes\n",
- "Memory after VOT : 1722.00 MB\n",
- "Rank 0: created area MSTd with 0 local nodes\n",
- "Memory after MSTd : 1742.09 MB\n",
- "Rank 0: created area PIP with 0 local nodes\n",
- "Memory after PIP : 1763.43 MB\n",
- "Rank 0: created area PO with 0 local nodes\n",
- "Memory after PO : 1784.93 MB\n",
- "Rank 0: created area DP with 0 local nodes\n",
- "Memory after DP : 1805.07 MB\n",
- "Rank 0: created area MIP with 0 local nodes\n",
- "Memory after MIP : 1826.64 MB\n",
- "Rank 0: created area MDP with 0 local nodes\n",
- "Memory after MDP : 1848.13 MB\n",
- "Rank 0: created area VIP with 0 local nodes\n",
- "Memory after VIP : 1870.06 MB\n",
- "Rank 0: created area LIP with 0 local nodes\n",
- "Memory after LIP : 1894.00 MB\n",
- "Rank 0: created area PITv with 0 local nodes\n",
- "Memory after PITv : 1919.31 MB\n",
- "Rank 0: created area PITd with 0 local nodes\n",
- "Memory after PITd : 1944.48 MB\n",
- "Rank 0: created area MSTl with 0 local nodes\n",
- "Memory after MSTl : 1965.81 MB\n",
- "Rank 0: created area CITv with 0 local nodes\n",
- "Memory after CITv : 1984.64 MB\n",
- "Rank 0: created area CITd with 0 local nodes\n",
- "Memory after CITd : 2003.96 MB\n",
- "Rank 0: created area FEF with 0 local nodes\n",
- "Memory after FEF : 2025.34 MB\n",
- "Rank 0: created area TF with 0 local nodes\n",
- "Memory after TF : 2040.98 MB\n",
- "Rank 0: created area AITv with 0 local nodes\n",
- "Memory after AITv : 2063.68 MB\n",
- "Rank 0: created area FST with 0 local nodes\n",
- "Memory after FST : 2080.41 MB\n",
- "Rank 0: created area 7a with 0 local nodes\n",
- "Memory after 7a : 2101.49 MB\n",
- "Rank 0: created area STPp with 0 local nodes\n",
- "Memory after STPp : 2120.20 MB\n",
- "Rank 0: created area STPa with 0 local nodes\n",
- "Memory after STPa : 2139.42 MB\n",
- "Rank 0: created area 46 with 0 local nodes\n",
- "Memory after 46 : 2154.79 MB\n",
- "Rank 0: created area AITd with 0 local nodes\n",
- "Memory after AITd : 2177.45 MB\n",
- "Rank 0: created area TH with 0 local nodes\n",
- "Memory after TH : 2190.03 MB\n",
- "Created areas and internal connections in 2.07 seconds.\n",
- "Created cortico-cortical connections in 20.73 seconds.\n",
- "Simulated network in 76.76 seconds.\n"
- ]
- }
- ],
- "source": [
- "# %%capture captured\n",
- "# 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": [
- "Go back to [Notebook structure](#toc)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bb71c922",
- "metadata": {
- "tags": []
- },
- "source": [
- "## S3. Simulation Results Visualization <a class=\"anchor\" id=\"section_3\"></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "38ddd973",
- "metadata": {
- "tags": []
- },
- "source": [
- "### 3.1. Instantaneous and mean firing rate across all populations <a class=\"anchor\" id=\"section_3_1\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "bea30fc8",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_instan_mean_firing_rate(M)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e91c436e-db94-4cd7-a531-29c032efeeae",
- "metadata": {},
- "source": [
- "### 3.2 Resting state plots <a class=\"anchor\" id=\"section_3_2\"></a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "ae19bcc3",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n",
- "Loading data from file\n",
- "Loading data from file\n",
- "Loading data from file\n",
- "Loading data from file\n"
- ]
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 864x762.831 with 10 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Choose 3 areas from the complete_area_list to show their sipking activity\n",
- "# By default, it's set as ['V1', 'V2', 'FEF']\n",
- "# Note: at this pre-released multi-area model v2.0.0, the areas_similated has to be \n",
- "raster_areas = ['V1', 'V2', 'FEF']\n",
- "plot_resting_state(M, data_path, raster_areas)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "cfc2c065-491c-4a3c-bfe2-1233308aaf77",
- "metadata": {},
- "source": [
- "Comparable figure in our publications: <br>\n",
- "1. Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018)\n",
- " A multi-scale layer-resolved spiking network model of\n",
- " resting-state dynamics in macaque cortex. PLOS Computational Biology, 14(9): e1006359. [https://doi.org/10.1371/journal.pcbi.1006359](https://doi.org/10.1371/journal.pcbi.1006359) <br>\n",
- " **Fig 3. Ground state of the model.**"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "473d0882-8e45-4330-bfa2-2c7e1af0dac4",
- "metadata": {
- "tags": []
- },
- "source": [
- "### 3.3 Time-averaged population rates <a class=\"anchor\" id=\"section_4_3\"></a>\n",
- "An overview over time-averaged population rates encoded in colors with areas along x-axis and populations along y-axis."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "721d1f03-df25-468d-8075-a807025a9c58",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Loading data from file\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 864x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_time_averaged_population_rates(M)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ef74ca3e-98dc-49c9-a4a0-2c640e29b1d9",
- "metadata": {},
- "source": [
- "Go back to [Notebook structure](#toc)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "EBRAINS-23.06",
- "language": "python",
- "name": "ebrains-23.06"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.11"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/run_example_fullscale-checkpoint.py b/.ipynb_checkpoints/run_example_fullscale-checkpoint.py
deleted file mode 100644
index 30e0c8bcbf9877a52a65a8ca699c889c5011b0bc..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/run_example_fullscale-checkpoint.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import numpy as np
-import os
-
-from multiarea_model import MultiAreaModel
-from start_jobs import start_job
-from config import submit_cmd, jobscript_template
-from config import base_path
-
-"""
-Example script showing how to simulate the multi-area model
-on a cluster.
-
-We choose the same configuration as in
-Fig. 3 of Schmidt et al. (2018).
-
-"""
-
-"""
-Full model. Needs to be simulated with sufficient
-resources, for instance on a compute cluster.
-"""
-d = {}
-conn_params = {'g': -11.,
- 'K_stable': os.path.join(base_path, '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': 1.,
- 'K_scaling': 1.,
- 'connection_params': conn_params,
- 'input_params': input_params,
- 'neuron_params': neuron_params}
-
-sim_params = {'t_sim': 2000.,
- 'num_processes': 720,
- 'local_num_threads': 1,
- 'recording_dict': {'record_vm': False}}
-
-theory_params = {'dt': 0.1}
-
-M = MultiAreaModel(network_params, simulation=True,
- sim_spec=sim_params,
- theory=True,
- theory_spec=theory_params)
-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])))
-start_job(M.simulation.label, submit_cmd, jobscript_template)
diff --git a/.ipynb_checkpoints/run_simulation-checkpoint.py b/.ipynb_checkpoints/run_simulation-checkpoint.py
deleted file mode 100644
index 4f2e4917859d644a059629b6b24a8080de54ea43..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/run_simulation-checkpoint.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""
-This script is used to run a simulation from the given command-line
-arguments:
-1. Label of the simulation
-2. Label of the network to be simulated
-
-It initializes the network class and then runs the simulate method of
-the simulation class instance.
-
-This script should be used in the `jobscript_template` defined in the
-config.py file. See config_template.py.
-"""
-
-import json
-import nest
-import os
-import sys
-
-from config import data_path
-from multiarea_model import MultiAreaModel
-
-label = sys.argv[1]
-network_label = sys.argv[2]
-fn = os.path.join(data_path,
- label,
- '_'.join(('custom_params',
- label,
- str(nest.Rank()))))
-with open(fn, 'r') as f:
- custom_params = json.load(f)
-
-os.remove(fn)
-
-M = MultiAreaModel(network_label,
- simulation=True,
- sim_spec=custom_params['sim_params'])
-M.simulation.simulate()