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1 merge request!35Pre-release MAM v1.1.0
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figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_resting_state-checkpoint.py → figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_VISUALIZATION-checkpoint.py
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......@@ -17,6 +17,29 @@ from matplotlib import gridspec
icolor = myred
ecolor = myblue
# label_spikes = M.simulation.label
label = M.simulation.label
from MAM2EBRAINS_LOAD_DATA import load_and_create_data
A = load_and_create_data(M)
def plot_instan_mean_firing_rate(M):
# load spike data and calculate instantaneous and mean firing rates
data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
tsteps, spikecount = np.unique(data[:,1], return_counts=True)
firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
# visualize calculate instantaneous and mean firing rates
ax = pl.subplot()
ax.plot(tsteps, rate)
ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
ax.set_title('Instantaneous and mean firing rate across all populations')
ax.set_xlabel('time (ms)')
ax.set_ylabel('firing rate (spikes / s)')
ax.set_xlim(0, sim_params['t_sim'])
ax.set_ylim(0, 50)
ax.legend()
def set_boxplot_props(d):
for i in range(len(d['boxes'])):
if i % 2 == 0:
......
figures/MAM2EBRAINS/M2E_visualize_resting_state.py → figures/MAM2EBRAINS/M2E_VISUALIZATION.py
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View file @ fb2387d6
......@@ -17,6 +17,29 @@ from matplotlib import gridspec
icolor = myred
ecolor = myblue
# label_spikes = M.simulation.label
label = M.simulation.label
from MAM2EBRAINS_LOAD_DATA import load_and_create_data
A = load_and_create_data(M)
def plot_instan_mean_firing_rate(M):
# load spike data and calculate instantaneous and mean firing rates
data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
tsteps, spikecount = np.unique(data[:,1], return_counts=True)
firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
# visualize calculate instantaneous and mean firing rates
ax = pl.subplot()
ax.plot(tsteps, rate)
ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
ax.set_title('Instantaneous and mean firing rate across all populations')
ax.set_xlabel('time (ms)')
ax.set_ylabel('firing rate (spikes / s)')
ax.set_xlim(0, sim_params['t_sim'])
ax.set_ylim(0, 50)
ax.legend()
def set_boxplot_props(d):
for i in range(len(d['boxes'])):
if i % 2 == 0:
......
figures/MAM2EBRAINS/M2E_visualize_instaneus_and_mean_rate.py
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− 15
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def plot_instan_mean_firing_rate(M):
# load spike data and calculate instantaneous and mean firing rates
data = np.loadtxt(M.simulation.data_dir + '/recordings/' + M.simulation.label + "-spikes-1-0.dat", skiprows=3)
tsteps, spikecount = np.unique(data[:,1], return_counts=True)
firing_rate = spikecount / M.simulation.params['dt'] * 1e3 / np.sum(M.N_vec)
ax = pl.subplot()
ax.plot(tsteps, rate)
ax.plot(tsteps, np.average(rate)*np.ones(len(tsteps)), label='mean')
ax.set_title('Instantaneous and mean firing rate across all populations')
ax.set_xlabel('time (ms)')
ax.set_ylabel('firing rate (spikes / s)')
ax.set_xlim(0, sim_params['t_sim'])
ax.set_ylim(0, 50)
ax.legend()
\ No newline at end of file
multi-area-model.ipynb
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− 135
View file @ fb2387d6
%% Cell type:markdown id:b1331599 tags:
# Down-scaled multi-area model
%% 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)
* [2.2. 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. Data Loading and Processing](#section_3)
* [S4. Simulation Results Visualization](#section_4)
* [4.1. Instantaneous and mean firing rate across all populations](#section_4_1)
* [4.2 Resting state plots](#section_4_2)
* [4.3 Time-averaged population rates](#section_4_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 io import StringIO
from multiarea_model import MultiAreaModel
from config import base_path, data_path
sys.path.append('./figures/MAM2EBRAINS')
# Ignore and don't display warning messages
import warnings
warnings.filterwarnings("ignore")
# Redirect stdout to a dummy stream
original_stdout = sys.stdout
sys.stdout = StringIO()
# # Ignore and don't display warning messages
# import warnings
# warnings.filterwarnings("ignore")
# # Redirect stdout to a dummy stream
# original_stdout = sys.stdout
# sys.stdout = StringIO()
```
%% Cell type:code id:7e07b0d0 tags:
``` python
!pip install nested_dict dicthash;
%%capture captured
!pip install nested_dict dicthash
```
%% Output
Requirement already satisfied: nested_dict in /srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/._view/ugqhkplzbs5vx62qrzwub7ficqgepl3r/lib/python3.8/site-packages (1.61)
Requirement already satisfied: dicthash in /srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/._view/ugqhkplzbs5vx62qrzwub7ficqgepl3r/lib/python3.8/site-packages (0.0.2)
Requirement already satisfied: future in /srv/main-spack-instance-2305/spack/var/spack/environments/ebrains-23-06/.spack-env/._view/ugqhkplzbs5vx62qrzwub7ficqgepl3r/lib/python3.8/site-packages (from dicthash) (0.18.2)
%% Cell type:code id:1d440c07-9b69-4e52-8573-26b13493bc5a tags:
``` python
# Jupyter notebook display format setting
from IPython.display import display, HTML
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>
%% 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
Initializing network from dictionary.
RAND_DATA_LABEL 3631
Error in library("aod") : there is no package called ‘aod’
Execution halted
No R installation or IndexError, taking hard-coded SLN fit parameters.
========================================
Customized parameters
--------------------
{'K_scaling': 0.005,
'N_scaling': 0.005,
'connection_params': {'K_stable': 'K_stable.npy',
'av_indegree_V1': 3950.0,
'fac_nu_ext_5E': 1.125,
'fac_nu_ext_6E': 1.41666667,
'fac_nu_ext_TH': 1.2,
'g': -11.0,
'replace_non_simulated_areas': 'het_poisson_stat'},
'fullscale_rates': 'tests/fullscale_rates.json',
'input_params': {'rate_ext': 10.0},
'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}
========================================
Simulation label: 27d81076e6d6e9e591684be053078477
Copied files.
Initialized simulation class.
%% 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);
```
%% Output
Initializing network from dictionary.
RAND_DATA_LABEL 9405
Error in library("aod") : there is no package called ‘aod’
Execution halted
No R installation or IndexError, taking hard-coded SLN fit parameters.
========================================
Customized parameters
--------------------
{'K_scaling': 1,
'N_scaling': 1,
'connection_params': {'K_stable': 'K_stable.npy',
'av_indegree_V1': 3950.0,
'fac_nu_ext_5E': 1.125,
'fac_nu_ext_6E': 1.41666667,
'fac_nu_ext_TH': 1.2,
'g': -11.0,
'replace_non_simulated_areas': 'het_poisson_stat'},
'fullscale_rates': 'tests/fullscale_rates.json',
'input_params': {'rate_ext': 10.0},
'neuron_params': {'V0_mean': -150.0, 'V0_sd': 50.0}}
========================================
Simulation label: 155470013b00dadc9c4a4af26ef5090e
Copied files.
Initialized simulation class.
6.8556 6.052848304676828
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [24], line 2
Cell In [48], line 2
1 from M2E_visualize_interareal_connectivity import visualize_interareal_connectivity
----> 2 visualize_interareal_connectivity(M)
File ~/MAM2EBRAINS/./figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py:236, in visualize_interareal_connectivity(M)
233 X, Y = np.meshgrid(x, y)
235 ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
--> 236 ax.set_xticklabels(area_list, rotation=90, size=6.)
238 ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
239 ax.set_yticklabels(area_list[::-1], size=6.)
File /srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-matplotlib-3.6.2-lhkot3cmeebfk5dp74dnubweq56upksc/lib/python3.8/site-packages/matplotlib/axes/_base.py:73, in _axis_method_wrapper.__set_name__.<locals>.wrapper(self, *args, **kwargs)
72 def wrapper(self, *args, **kwargs):
---> 73 return get_method(self)(*args, **kwargs)
File /srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-matplotlib-3.6.2-lhkot3cmeebfk5dp74dnubweq56upksc/lib/python3.8/site-packages/matplotlib/axis.py:1968, in Axis._set_ticklabels(self, labels, fontdict, minor, **kwargs)
1966 if fontdict is not None:
1967 kwargs.update(fontdict)
-> 1968 return self.set_ticklabels(labels, minor=minor, **kwargs)
File /srv/main-spack-instance-2305/spack/opt/spack/linux-ubuntu20.04-x86_64/gcc-10.3.0/py-matplotlib-3.6.2-lhkot3cmeebfk5dp74dnubweq56upksc/lib/python3.8/site-packages/matplotlib/axis.py:1890, in Axis.set_ticklabels(self, ticklabels, minor, **kwargs)
1886 if isinstance(locator, mticker.FixedLocator):
1887 # Passing [] as a list of ticklabels is often used as a way to
1888 # remove all tick labels, so only error for > 0 ticklabels
1889 if len(locator.locs) != len(ticklabels) and len(ticklabels) != 0:
-> 1890 raise ValueError(
1891 "The number of FixedLocator locations"
1892 f" ({len(locator.locs)}), usually from a call to"
1893 " set_ticks, does not match"
1894 f" the number of ticklabels ({len(ticklabels)}).")
1895 tickd = {loc: lab for loc, lab in zip(locator.locs, ticklabels)}
1896 func = functools.partial(self._format_with_dict, tickd)
ValueError: The number of FixedLocator locations (33), usually from a call to set_ticks, does not match the number of ticklabels (32).
%% Cell type:markdown id:bae85d86-157c-47a2-9826-860b410a440e tags:
Based on: <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
# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.
M.simulation.simulate()
```
%% Output
Prepared simulation in 0.00 seconds.
Rank 0: created area V1 with 0 local nodes
Memory after V1 : 1911.70 MB
Rank 0: created area V2 with 0 local nodes
Memory after V2 : 1938.30 MB
Rank 0: created area VP with 0 local nodes
Memory after VP : 1967.50 MB
Rank 0: created area V3 with 0 local nodes
Memory after V3 : 1995.87 MB
Rank 0: created area V3A with 0 local nodes
Memory after V3A : 2015.69 MB
Rank 0: created area MT with 0 local nodes
Memory after MT : 2041.32 MB
Rank 0: created area V4t with 0 local nodes
Memory after V4t : 2066.22 MB
Rank 0: created area V4 with 0 local nodes
Memory after V4 : 2093.25 MB
Rank 0: created area VOT with 0 local nodes
Memory after VOT : 2118.44 MB
Rank 0: created area MSTd with 0 local nodes
Memory after MSTd : 2140.03 MB
Rank 0: created area PIP with 0 local nodes
Memory after PIP : 2161.38 MB
Rank 0: created area PO with 0 local nodes
Memory after PO : 2182.89 MB
Rank 0: created area DP with 0 local nodes
Memory after DP : 2203.16 MB
Rank 0: created area MIP with 0 local nodes
Memory after MIP : 2224.65 MB
Rank 0: created area MDP with 0 local nodes
Memory after MDP : 2246.05 MB
Rank 0: created area VIP with 0 local nodes
Memory after VIP : 2268.10 MB
Rank 0: created area LIP with 0 local nodes
Memory after LIP : 2292.05 MB
Rank 0: created area PITv with 0 local nodes
Memory after PITv : 2317.39 MB
Rank 0: created area PITd with 0 local nodes
Memory after PITd : 2342.60 MB
Rank 0: created area MSTl with 0 local nodes
Memory after MSTl : 2364.06 MB
Rank 0: created area CITv with 0 local nodes
Memory after CITv : 2383.12 MB
Rank 0: created area CITd with 0 local nodes
Memory after CITd : 2402.46 MB
Rank 0: created area FEF with 0 local nodes
Memory after FEF : 2423.93 MB
Rank 0: created area TF with 0 local nodes
Memory after TF : 2439.57 MB
Rank 0: created area AITv with 0 local nodes
Memory after AITv : 2462.29 MB
Rank 0: created area FST with 0 local nodes
Memory after FST : 2479.02 MB
Rank 0: created area 7a with 0 local nodes
Memory after 7a : 2500.19 MB
Rank 0: created area STPp with 0 local nodes
Memory after STPp : 2518.91 MB
Rank 0: created area STPa with 0 local nodes
Memory after STPa : 2538.05 MB
Rank 0: created area 46 with 0 local nodes
Memory after 46 : 2553.50 MB
Rank 0: created area AITd with 0 local nodes
Memory after AITd : 2576.05 MB
Rank 0: created area TH with 0 local nodes
Memory after TH : 2588.76 MB
Created areas and internal connections in 2.33 seconds.
Created cortico-cortical connections in 22.84 seconds.
Simulated network in 74.35 seconds.
%% Cell type:markdown id:fd6e3232 tags:
Go back to [Notebook structure](#toc)
%% Cell type:markdown id:57ff902c-d6ce-4f96-9e4f-8e3e7166ab66 tags:
## S3. Data Loading and Processing <a class="anchor" id="section_3"></a>
%% Cell type:code id:f5b58845-4d1a-430f-83f4-402fdf918aef tags:
``` python
label_spikes = M.simulation.label
label = M.simulation.label
from MAM2EBRAINS_LOAD_DATA import load_data
A, tsteps, firing_rate = load_data(M)
```
%% Output
loading spikes
Loading data from file
Computing population rates done
Loading data from file
Computing synchrony done
Loading data from file
Computing population LvR done
Loading data from file
Loading data from file
Computing rate time series done
pop_LvR
pop_rates
synchrony
python3: can't open file './../Schmidt2018_dyn/compute_bold_signal.py': [Errno 2] No such file or directory
%% Cell type:markdown id:2da9728d-4481-4a15-b810-d125e39cbe4e tags:
Go back to [Notebook structure](#toc)
%% Cell type:markdown id:bb71c922 tags:
## S4. Simulation Results Visualziation <a class="anchor" id="section_4"></a>
%% Cell type:markdown id:38ddd973 tags:
### 4.1. Instantaneous and mean firing rate across all populations <a class="anchor" id="section_4_1"></a>
%% Cell type:code id:bea30fc8 tags:
``` python
from M2E_VISUALIZATION import plot_instan_mean_firing_rate
plot_instan_mean_firing_rate(M)
```
%% Output
%% Cell type:markdown id:e91c436e-db94-4cd7-a531-29c032efeeae tags:
### 4.2 Resting state plots <a class="anchor" id="section_4_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 MAM2EBRAINS_VISUALIZATION import plot_resting_state
plot_resting_state(M, A, label_spikes, data_path, sim_params)
```
%% Output
%% Cell type:markdown id:473d0882-8e45-4330-bfa2-2c7e1af0dac4 tags:
### 4.3 Time-averaged population rates <a class="anchor" id="section_4_3"></a>
Plot 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
# 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']
# output = {'pdf', 'png', 'eps'}, optional
A.show_rates()
```
%% Output
0 V1
1 V2
2 VP
3 V3
4 PIP
5 V3A
6 MT
7 V4t
8 V4
9 PO
10 VOT
11 DP
12 MIP
13 MDP
14 MSTd
15 VIP
16 LIP
17 PITv
18 PITd
19 AITv
20 MSTl
21 FST
22 CITv
23 CITd
24 7a
25 STPp
26 STPa
27 FEF
28 46
29 TF
30 TH
31 AITd
['23E', '23I', '4E', '4I', '5E', '5I', '6E', '6I']
%% Cell type:markdown id:ef74ca3e-98dc-49c9-a4a0-2c640e29b1d9 tags:
Go back to [Notebook structure](#toc)
......
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