{
"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"
]
},
{
"ename": "SyntaxError",
"evalue": "invalid syntax (M2E_visualize_time_ave_pop_rates.py, line 2)",
"output_type": "error",
"traceback": [
"Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n",
"\u001b[0m File \u001b[1;32m/srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/view/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3378\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\u001b[0m\n",
"\u001b[0;36m Cell \u001b[0;32mIn [2], line 17\u001b[0;36m\n\u001b[0;31m from M2E_visualize_time_ave_pop_rates import plot_time_averaged_population_rates\u001b[0;36m\n",
"\u001b[0;36m File \u001b[0;32m~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py:2\u001b[0;36m\u001b[0m\n\u001b[0;31m An overview over time-averaged population rates encoded in colors with areas along x-axis and populations along y-axis.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\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 multiarea_model import Analysis\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": null,
"id": "7e07b0d0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%capture captured\n",
"!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))\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` <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",
"# Value range/options: (0, 1.]\n",
"# Value assgined: 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 assgined: 1.0\n",
"cc_weights_factor = 1.0\n",
"\n",
"# Cortical areas included in the simulation\n",
"# Value range/options: any sublist of complete_ares_list\n",
"# Value assgined: complete_ares_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 assgined: '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."
]
},
{
"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",
" # 't_sim': 1500., # 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": null,
"id": "ab25f9f8",
"metadata": {},
"outputs": [],
"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": 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 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": null,
"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": null,
"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": null,
"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": null,
"id": "05512922-26e5-425f-90a4-0df7c2279ccf",
"metadata": {},
"outputs": [],
"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\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": null,
"id": "15778e9c",
"metadata": {},
"outputs": [],
"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 Visualziation <a class=\"anchor\" id=\"section_3\"></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1d7aa61-e85a-4e6a-9e01-e018413a572b",
"metadata": {},
"outputs": [],
"source": [
"# Instantiate an analysis class and load spike data\n",
"A = Analysis(network=M, \n",
" simulation=M.simulation, \n",
" data_list=['spikes'],\n",
" load_areas=None)"
]
},
{
"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": null,
"id": "bea30fc8",
"metadata": {},
"outputs": [],
"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": "markdown",
"id": "aeae56a4",
"metadata": {},
"source": [
"Comparable figure in our publications: <br>\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",
" **Fig 5. Resting state of the model with χ =1.9.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae19bcc3",
"metadata": {
"tags": []
},
"outputs": [],
"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",
"raster_areas = ['V4', 'V1', 'FEF']\n",
"plot_resting_state(M, A, data_path, raster_areas)"
]
},
{
"cell_type": "markdown",
"id": "473d0882-8e45-4330-bfa2-2c7e1af0dac4",
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"### 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."
]
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"source": [
"plot_time_averaged_population_rates(A)"
]
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"|Area index|0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|\n",
"|:--------:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n",
"|Area |V1|V2|VP|V3|PIP|V3A|MT|V4t|V4|PO|VOT|DP|MIP|MDP|MSTd|VIP|LIP|PITv|PITd|AITv|MSTl|FST|CITv|CITd|7a|STPp|STPa|FEF|46|TF|TH|AITd| "
]
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"source": [
"Go back to [Notebook structure](#toc)"
]
}
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