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.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
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figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py
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......@@ -193,7 +193,7 @@ def plot_resting_state(M, data_path):
spike_data = A.spike_data
# stationary firing rates
fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
fn = os.path.join(data_path, str(label), 'Analysis', 'pop_rates.json')
with open(fn, 'r') as f:
pop_rates = json.load(f)
......
figures/MAM2EBRAINS/M2E_visualize_resting_state.py
+ 1
− 1
View file @ 73487490
......@@ -193,7 +193,7 @@ def plot_resting_state(M, data_path):
spike_data = A.spike_data
# stationary firing rates
fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
fn = os.path.join(data_path, str(label), 'Analysis', 'pop_rates.json')
with open(fn, 'r') as f:
pop_rates = json.load(f)
......
multi-area-model.ipynb
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View file @ 73487490
%% Cell type:markdown id:b1331599 tags:
# Down-scaled multi-area model
%% Cell type:markdown id:f4a649cc-3b68-49e4-b2b6-6f29f13a6d9c tags:
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:
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
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
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
<br>
%% Cell type:markdown id:b952d0ea tags:
#### Notebook structure <a class="anchor" id="toc"></a>
* [S0. Configuration](#section_0)
* [S1. Parameterization](#section_1)
* [1.1. Parameters to tune](#section_1_1)
* [1.2. Default parameters](#section_1_2)
* [S2. Multi-Area Model Instantiation and Simulation](#section_2)
* [2.1. Insantiate a multi-area model](#section_2_1)
* [2.1. Instantiate a multi-area model](#section_2_1)
* [2.2. Predict firing rates from theory](#section_2_2)
* [2.3. Extract and visualize interareal connectivity](#section_2_3)
* [2.4. Run a simulation](#section_2_4)
* [S3. Simulation Results Visualization](#section_3)
* [3.1. Instantaneous and mean firing rate across all populations](#section_3_1)
* [3.2. Resting state plots](#section_3_2)
* [3.3. Time-averaged population rates](#section_3_3)
%% Cell type:markdown id:d782e527 tags:
## S0. Configuration <a class="anchor" id="section_0"></a>
%% Cell type:code id:9d6cc7d9-3110-4d96-9f9a-9ec7dee6d145 tags:
``` python
# Create config file
with open('config.py', 'w') as fp:
fp.write(
'''import os
base_path = os.path.abspath(".")
data_path = os.path.abspath("simulations")
jobscript_template = "python {base_path}/run_simulation.py {label}"
submit_cmd = "bash -c"
''')
```
%% Cell type:code id:96517739 tags:
``` python
%matplotlib inline
import numpy as np
import os
import nest
import json
import sys
from IPython.display import display, HTML
from multiarea_model import MultiAreaModel
from config import base_path, data_path
sys.path.append('./figures/MAM2EBRAINS')
```
%% Output
-- N E S T --
Copyright (C) 2004 The NEST Initiative
Version: 3.5
Built: Jul 12 2023 06:25:27
This program is provided AS IS and comes with
NO WARRANTY. See the file LICENSE for details.
Problems or suggestions?
Visit https://www.nest-simulator.org
Type 'nest.help()' to find out more about NEST.
%% Cell type:code id:7e07b0d0 tags:
``` python
%%capture captured
!pip install nested_dict dicthash
```
%% Cell type:code id:1d440c07-9b69-4e52-8573-26b13493bc5a tags:
``` python
# Jupyter notebook display format setting
style = """
<style>
table {float:left}
</style>
"""
display(HTML(style))
```
%% Output
%% Cell type:markdown id:27160ba8 tags:
Go back to [Notebook structure](#toc)
%% Cell type:markdown id:df83f5ea-1c4b-44d3-9926-01786aa46e14 tags:
## S1. Parameterization <a class="anchor" id="section_1"></a>
%% Cell type:markdown id:30655817 tags:
### 1.1. Parameters to tune <a class="anchor" id="section_1_1"></a>
%% Cell type:markdown id:4f67c1ba tags:
|Parameter|Default value|Value range/options|Value assigned|Description|
|:-------:|:-----------:|:-----------------:|:------------:|:---------:|
|scale_down_to|1. |(0, 1.0] |0.005 |$^1$ |
|cc_weights_factor|1. |[1.0, 2.5] |1. |$^2$ |
|areas_simulated|complete_area_list|Sublists of complete_area_list|complete_area_list|$^3$|
|replace_non_simulated_areas|None|None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat'|'het_poisson_stat'|$^4$ |
%% Cell type:markdown id:a2161477 tags:
1. `scale_down_to` <br>
`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>
2. `cc_weights_factor` <br>
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>
3. `areas_simulated` <br>
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>
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>
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>
4. `replace_non_simulated_areas` <br>
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 id:60265d52 tags:
``` python
# Downscaling factor
# Value range/options: (0, 1.]
# Value assgined: 0.005
scale_down_to = 0.005 # Change it to 1. for running the fullscale network
# Scaling factor for cortico-cortical connections (chi)
# Value range/options: [1., 2.5]
# Value assgined: 1.0
cc_weights_factor = 1.0
# Cortical areas included in the simulation
# Value range/options: any sublist of complete_ares_list
# Value assgined: complete_ares_list
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']
# Firing rates used to replace the non-simulated areas
# Value range/options: None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat'
# Value assgined: 'het_poisson_stat'
replace_non_simulated_areas = 'het_poisson_stat'
```
%% Cell type:markdown id:de11b07f tags:
### 1.2. Default parameters <a class="anchor" id="section_1_2"></a>
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 id:6e4bed8d tags:
``` python
# Connection parameters
conn_params = {
'replace_non_simulated_areas': 'het_poisson_stat', # Whether to replace non-simulated areas by Poisson sources with the same global rate, by default: None
'g': -11., # It sets the relative inhibitory synaptic strength, by default: -16.
'K_stable': 'K_stable.npy', # Whether to apply the stabilization method of Schuecker, Schmidt et al. (2017), by default: None
'fac_nu_ext_TH': 1.2, # Increase the external input to 2/3E and 5E in area TH
'fac_nu_ext_5E': 1.125, # Increase the external Poisson indegree onto 5E
'fac_nu_ext_6E': 1.41666667, # Increase the external Poisson indegree onto 6E
'av_indegree_V1': 3950. # Adjust the average indegree in V1 based on monkey data
}
# Input parameters
input_params = {
'rate_ext': 10. # Rate of the Poissonian spike generator (in spikes/s)
}
# Neuron parameters
neuron_params = {
'V0_mean': -150., # Mean for the distribution of initial membrane potentials, by default: -100.
'V0_sd': 50.} # Standard deviation for the distribution of initial membrane potentials, by default: 50.
# Network parameters
network_params = {
'N_scaling': scale_down_to, # Scaling of population sizes, by default: 1.
'K_scaling': scale_down_to, # Scaling of indegrees, by default: 1.
'fullscale_rates': 'tests/fullscale_rates.json', # Absolute path to the file holding full-scale rates for scaling synaptic weights, by default: None
'input_params': input_params, # Input parameters
'connection_params': conn_params, # Connection parameters
'neuron_params': neuron_params # Neuron parameters
}
# Simulation parameters
sim_params = {
'areas_simulated': areas_simulated,
't_sim': 2000., # Simulated time (in ms), by default: 10.0
# 't_sim': 1500., # Simulated time (in ms), by default: 10.0
'num_processes': 1, # The number of MPI processes, by default: 1
'local_num_threads': 1, # The number of threads per MPI process, by default: 1
'recording_dict': {'record_vm': False},
'rng_seed': 1 # global random seed
}
# Theory paramters (theory_params)
theory_params = {
'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01
}
```
%% Cell type:markdown id:1472e9c5 tags:
Go back to [Notebook structure](#toc)
%% Cell type:markdown id:de4a6703 tags:
## S2. Multi-Area Model Instantiation and Simulation <a class="anchor" id="section_2"></a>
%% Cell type:markdown id:1fd58841 tags:
### 2.1. Insantiate a multi-area model <a class="anchor" id="section_2_1"></a>
### 2.1. Instantiate a multi-area model <a class="anchor" id="section_2_1"></a>
%% Cell type:code id:ab25f9f8 tags:
``` python
%%capture captured
M = MultiAreaModel(network_params,
simulation=True,
sim_spec=sim_params,
theory=True,
theory_spec=theory_params);
```
%% Output
Error in library("aod") : there is no package called ‘aod’
Execution halted
%% Cell type:markdown id:91649c30 tags:
### 2.2. Predict firing rates from theory <a class="anchor" id="section_2_2"></a>
%% Cell type:code id:6a7ddf0e tags:
``` python
p, r = M.theory.integrate_siegert()
print("Mean-field theory predicts an average "
"firing rate of {0:.3f} spikes/s across all populations.".format(np.mean(r[:, -1])))
```
%% Output
Iteration: 0
Mean-field theory predicts an average firing rate of 29.588 spikes/s across all populations.
%% Cell type:markdown id:2062ddf3 tags:
### 2.3. Extract and visualize interareal connectivity <a class="anchor" id="section_2_3"></a>
%% Cell type:markdown id:8a7c09e0 tags:
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 id:6316ac24 tags:
``` python
# Neuron numbers
# Dictionary of neuron numbers
# M.N
# Array of neuron numbers
# M.N_vec
```
%% Cell type:code id:8408d463-557b-481b-afc1-5fbbbd67306d tags:
``` python
# Indegrees
# Dictionary of nodes indegrees organized as:
# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: indegree_values}}}}
# M.K
# Array of nodes indegrees
# M.K_matrix.shape
```
%% Cell type:code id:445a722a tags:
``` python
# Synapses
# Dictionary of synapses that target neurons receive, it is organized as:
# {<source_area>: {<source_pop>: {<target_area>: {<target_pop>: number_of_synapses}}}}
# M.synapses
# Array of
# M.syn_matrix
```
%% Cell type:code id:05512922-26e5-425f-90a4-0df7c2279ccf tags:
``` python
%%capture captured
# from M2E_visualize_interareal_connectivity import visualize_interareal_connectivity
# visualize_interareal_connectivity(M)
```
%% Cell type:markdown id:bae85d86-157c-47a2-9826-860b410a440e tags:
Full-scale interareal connectivity is from: <br>
Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ <br>
Multi-scale account of the network structure of macaque visual cortex <br>
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>
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 tags:
Go back to [Notebook structure](#toc)
%% Cell type:markdown id:0c1cad59-81d0-4e24-ac33-13c4ca8c6dec tags:
### 2.4. Run a simulation <a class="anchor" id="section_2_4"></a>
%% Cell type:code id:15778e9c tags:
``` python
%%capture captured
# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.
M.simulation.simulate()
```
%% Cell type:markdown id:fd6e3232 tags:
Go back to [Notebook structure](#toc)
%% Cell type:markdown id:bb71c922 tags:
## S3. Simulation Results Visualziation <a class="anchor" id="section_3"></a>
%% Cell type:markdown id:38ddd973 tags:
### 3.1. Instantaneous and mean firing rate across all populations <a class="anchor" id="section_3_1"></a>
%% Cell type:code id:bea30fc8 tags:
``` python
from M2E_visualize_instantaneous_and_mean_firing_rates import plot_instan_mean_firing_rate
plot_instan_mean_firing_rate(M)
```
%% Output
%% Cell type:markdown id:e91c436e-db94-4cd7-a531-29c032efeeae tags:
### 3.2 Resting state plots <a class="anchor" id="section_3_2"></a>
%% Cell type:markdown id:aeae56a4 tags:
**Fig 5. Resting state of the model with χ =1.9.** (A-C) 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. (D) Population-averaged firing rates. (E) Average pairwise correlation coefficients of spiking activity. (F) Irregularity measured by revised local variation LvR averaged across neurons. (G) 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 id:ae19bcc3 tags:
``` python
from M2E_visualize_resting_state import plot_resting_state
plot_resting_state(M, data_path)
```
%% Output
loading spikes
Loading data from file
Computing population rates done
Loading data from file
Computing population LvR done
Loading data from file
Loading data from file
Computing rate time series done
Loading data from file
Computing synchrony done
pop_LvR
pop_rates
synchrony
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In [15], line 2
1 from M2E_visualize_resting_state import plot_resting_state
----> 2 plot_resting_state(M, data_path)
File ~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_resting_state.py:197, in plot_resting_state(M, data_path)
195 # stationary firing rates
196 fn = os.path.join(data_path, label, 'Analysis', 'pop_rates.json')
--> 197 with open(fn, 'r') as f:
198 pop_rates = json.load(f)
200 # time series of firing rates
FileNotFoundError: [Errno 2] No such file or directory: '/opt/app-root/src/MAM2EBRAINS/simulations/C/Analysis/pop_rates.json'
%% Cell type:markdown id:473d0882-8e45-4330-bfa2-2c7e1af0dac4 tags:
### 3.3 Time-averaged population rates <a class="anchor" id="section_4_3"></a>
An overview over time-averaged population rates encoded in colors with areas along x-axis and populations along y-axis.
%% Cell type:code id:721d1f03-df25-468d-8075-a807025a9c58 tags:
``` python
# %%capture captured
A.show_rates()
```
%% Cell type:code id:5b40db5d-51b2-4d16-b36b-9f1995452b05 tags:
``` python
|Index|0|1|2|3|4|5|6|7|
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
```
%% Cell type:markdown id:b03d44e8-2216-44ff-ada4-83e9c3e6d30a tags:
|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|
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
|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|
%% Cell type:markdown id:ef74ca3e-98dc-49c9-a4a0-2c640e29b1d9 tags:
Go back to [Notebook structure](#toc)
......
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