diff --git a/figures/Schmidt2017/Fig2_anatomy.py b/figures/Schmidt2017/Fig2_anatomy.py
index 24347e0f91e284bbf74eec2d03cc3904ef1ebba4..545406860e41b1eab75a8b5c249362383bb9523f 100644
--- a/figures/Schmidt2017/Fig2_anatomy.py
+++ b/figures/Schmidt2017/Fig2_anatomy.py
@@ -16,7 +16,6 @@ if NEURON_DENSITIES_AVAILABLE:
neuron_densities = proc['neuronal_densities']
architecture_completed = proc['architecture_completed']
-
categories = {}
for ii in np.arange(0, 9, 1):
categories[ii] = []
@@ -193,15 +192,14 @@ if NEURON_DENSITIES_AVAILABLE:
ax.legend(loc=(0.035, 0.45), edgecolor='k')
-
##################################################
barbas_array = np.zeros(len(area_list))
for i, area in enumerate(area_list):
barbas_array[i] = total_thickness_data[area] / 1000.
-
gradient, intercept, r_value, p_value, std_err = stats.linregress(
- log_density_array[np.isfinite(barbas_array)], barbas_array[np.isfinite(barbas_array)])
+ log_density_array[np.isfinite(barbas_array)],
+ barbas_array[np.isfinite(barbas_array)])
print('total thicknesses from Barbas lab vs log. densities:')
print('gradient: ', gradient)
@@ -209,7 +207,6 @@ if NEURON_DENSITIES_AVAILABLE:
print('r-value: ', r_value)
print('p-value: ', p_value)
-
ax = axes['B']
ax.plot(log_density_array, barbas_array, '.', ms=6, color='k')
@@ -221,9 +218,6 @@ if NEURON_DENSITIES_AVAILABLE:
ax.set_yticks(np.arange(1., 3., 0.5))
-
- ############################################################
-
ax = axes['C']
print('fractions of total thickness vs log. densities')
@@ -247,4 +241,3 @@ if NEURON_DENSITIES_AVAILABLE:
else:
print("Figure 2 can currently not be produced because"
"we cannot publish the underlying raw data.")
-
diff --git a/figures/Schmidt2017/Fig3_construction.py b/figures/Schmidt2017/Fig3_construction.py
index 3c6d4f0f2254396cb508e76f8603139b02a20380..75ff2da8bf5d9c89d096048ee422fe25bdc3184c 100644
--- a/figures/Schmidt2017/Fig3_construction.py
+++ b/figures/Schmidt2017/Fig3_construction.py
@@ -75,7 +75,8 @@ ax.set_ylabel(r'Average indegree $(\times 10^3)$')
ax.set_yticks([5000., 10000., 15000., 20000.])
ax.set_yticklabels([r'5', r'10', r'15', r'20'])
-print(("Average indegree across all areas: {}".format(np.average(indegrees, weights=num_list))))
+print(("Average indegree across all areas: {}".format(np.average(indegrees,
+ weights=num_list))))
p.savefig('Fig3_construction_mpl.eps')
diff --git a/figures/Schmidt2017/Fig6_connectivity_measures.py b/figures/Schmidt2017/Fig6_connectivity_measures.py
index daa9600f2d403e6c2e3c8ac7df302eb98f67abdd..c3d8234da3cf58908ec9e0334bb1c9e90a94c0c6 100644
--- a/figures/Schmidt2017/Fig6_connectivity_measures.py
+++ b/figures/Schmidt2017/Fig6_connectivity_measures.py
@@ -147,7 +147,8 @@ ax_hist.yaxis.set_ticks_position("none")
ax_hist.xaxis.set_ticks_position("none")
colors = sm.to_rgba(bins[:-1])
-ax_hist.bar(bins[:-1], vals, width=np.diff(bins), color=colors, edgecolor='k', align='edge')
+ax_hist.bar(bins[:-1], vals, width=np.diff(bins), color=colors,
+ edgecolor='k', align='edge')
ax_hist.set_xscale('Log')
ax_hist.set_xlim(cb.get_clim())
ax_hist.set_xticks([])
@@ -194,7 +195,8 @@ ax_hist.yaxis.set_ticks_position("none")
ax_hist.xaxis.set_ticks_position("none")
colors = sm.to_rgba(bins[:-1])
-ax_hist.bar(bins[:-1], vals, width=np.diff(bins), color=colors, edgecolor='k', align='edge')
+ax_hist.bar(bins[:-1], vals, width=np.diff(bins), color=colors,
+ edgecolor='k', align='edge')
ax_hist.set_xscale('Log')
ax_hist.set_xlim(cb.get_clim())
ax_hist.set_xticks([])
diff --git a/figures/Schmidt2017/Fig7_community_structure.py b/figures/Schmidt2017/Fig7_community_structure.py
index e121ca4a0f19b5dc95753f8f2f831681954aee0c..a52dca32b7636a6918730366852918f4ed7c7579 100644
--- a/figures/Schmidt2017/Fig7_community_structure.py
+++ b/figures/Schmidt2017/Fig7_community_structure.py
@@ -106,7 +106,8 @@ mod_list = []
# Shuffling of the connectivity
# In the connectivity matrix, rows == targets, columns == sources
-# For each column, we shuffle the rows and therefore conserve the total outdegree of each area.
+# For each column, we shuffle the rows and therefore conserve the
+# total outdegree of each area.
for ii in range(1000):
for jj in range(32):
diff --git a/multiarea_model/analysis.py b/multiarea_model/analysis.py
index ea4b9079e75b500a738efc6cc53d9d7f775bfc97..faec444d6d83db3bc4258c0ae4deaec04a003813 100644
--- a/multiarea_model/analysis.py
+++ b/multiarea_model/analysis.py
@@ -847,11 +847,14 @@ class Analysis:
if pop:
plt.savefig(os.path.join(self.output_dir,
'{}_power_spectrum_{}_{}.{}'.format(self.simulation.label,
- area, pop, keywords['output'])))
+ area,
+ pop,
+ keywords['output'])))
else:
plt.savefig(os.path.join(self.output_dir,
'{}_power_spectrum_{}.{}'.format(self.simulation.label,
- area, keywords['output'])))
+ area,
+ keywords['output'])))
else:
fig.show()
diff --git a/multiarea_model/analysis_helpers.py b/multiarea_model/analysis_helpers.py
index f8f3eaa713c6132f4646206dd48aab1cd253857c..eba90f12a7e996adcde752105f01ac94ec96a992 100644
--- a/multiarea_model/analysis_helpers.py
+++ b/multiarea_model/analysis_helpers.py
@@ -11,8 +11,10 @@ macaque visual cortex (Schmidt et al. 2018).
Functions
--------
-_create_parameter_dict : Create parameter dict for functions of data class.
-_check_stored_data : Check if stored data was computed with the correct Parameters.
+_create_parameter_dict : Create parameter dict for functions
+ of data class.
+_check_stored_data : Check if stored data was computed
+ with the correct Parameters.
online_hist : Compute spike histogram on a spike file line by line.
pop_rate : Compute average firing rate.
pop_rate_distribution : Compute distribution of single-cell firing rates.
@@ -38,7 +40,7 @@ Sacha van Albada
"""
-from copy import copy, deepcopy
+from copy import copy
from nested_dict import nested_dict
import numpy as np
import json
@@ -53,7 +55,9 @@ area_list = ['V1', 'V2', 'VP', 'V3', 'PIP', 'V3A', 'MT', 'V4t', 'V4',
pop_list = ['23E', '23I', '4E', '4I', '5E', '5I', '6E', '6I']
-def model_iter(mode='single', areas=None, pops='complete', areas2=None, pops2='complete'):
+def model_iter(mode='single',
+ areas=None, pops='complete',
+ areas2=None, pops2='complete'):
"""
Helper function to create a an iterator over all possible pairs of
populations in the model, possible restricted by specifying areas
@@ -68,13 +72,14 @@ def model_iter(mode='single', areas=None, pops='complete', areas2=None, pops2='c
Defaults to 'single'.
areas, areas2 : list, optional
- If specified, loop only over these areas as target and source areas.
- Defaults to None, which correspons to taking all areas into account.
+ If specified, loop only over these areas as target and source
+ areas. Defaults to None, which correspons to taking all areas
+ into account.
pops, pops2 : string or list, optional
- If specified, loop only over these populations as target and source
- populations. Defaults to 'complete', which corresponds to taking all
- areas into account.
- If None, loop only over areas.
+ If specified, loop only over these populations as target and
+ source populations. Defaults to 'complete', which corresponds
+ to taking all areas into account. If None, loop only over
+ areas.
Returns
-------
@@ -158,7 +163,8 @@ def sort_gdf_by_id(data, idmin=None, idmax=None):
----------
data: numpy.array (dtype=object) with lists ['int', 'float']
- The nest output loaded from gdf format. Each row contains a global id
+ The nest output loaded from gdf format. Each row contains a
+ global id
idmin, idmax : int, optional
The minimum/maximum neuron id to be considered.
@@ -167,7 +173,8 @@ def sort_gdf_by_id(data, idmin=None, idmax=None):
ids : list of ints
Neuron ids, e.g., [id1,id2,...]
srt : list of lists of floats
- Spike trains corresponding to the neuron ids, e.g., [[t1,t2,...],...]
+ Spike trains corresponding to the neuron ids, e.g.,
+ [[t1,t2,...],...]
"""
assert((idmin is None and idmax is None)
@@ -251,9 +258,11 @@ def _check_stored_data(fp, fn_iter, param_dict):
param_dict_copy = copy(param_dict)
param_dict2_copy = copy(param_dict2)
for k in param_dict:
- if isinstance(param_dict_copy[k], list) or isinstance(param_dict_copy[k], np.ndarray):
+ if (isinstance(param_dict_copy[k], list) or
+ isinstance(param_dict_copy[k], np.ndarray)):
param_dict_copy[k] = set(param_dict_copy[k])
- if isinstance(param_dict2_copy[k], list) or isinstance(param_dict2_copy[k], np.ndarray):
+ if (isinstance(param_dict2_copy[k], list) or
+ isinstance(param_dict2_copy[k], np.ndarray)):
param_dict2_copy[k] = set(param_dict2_copy[k])
if param_dict_copy == param_dict2_copy:
print("Loading data from file")
@@ -360,8 +369,9 @@ def online_hist(fname, tmin, tmax, resolution=1.):
def pop_rate(data_array, t_min, t_max, num_neur):
"""
Calculates firing rate of a given array of spikes.
- Rates are calculated in spikes/s. Assumes spikes are sorted according to time.
- First calculates rates for individual neurons and then averages over neurons.
+ Rates are calculated in spikes/s. Assumes spikes are sorted
+ according to time. First calculates rates for individual neurons
+ and then averages over neurons.
Parameters
----------
@@ -397,8 +407,8 @@ def pop_rate(data_array, t_min, t_max, num_neur):
if num_spikes > 0:
rates.append(num_spikes / ((t_max - t_min) / 1000.))
else: # we arrived at the last neuron
- rates.append((data_array[:, 0].size - last_index) / ((t_max -
- t_min) / 1000.))
+ rates.append((data_array[:, 0].size - last_index) /
+ ((t_max - t_min) / 1000.))
last_index += num_spikes
while len(rates) < num_neur:
rates.append(0.0)
@@ -413,9 +423,10 @@ def pop_rate(data_array, t_min, t_max, num_neur):
def pop_rate_distribution(data_array, t_min, t_max, num_neur):
"""
- Calculates firing rate distribution over neurons in a given array of spikes.
- Rates are calculated in spikes/s. Assumes spikes are sorted according to time.
- First calculates rates for individual neurons and then averages over neurons.
+ Calculates firing rate distribution over neurons in a given array
+ of spikes. Rates are calculated in spikes/s. Assumes spikes are
+ sorted according to time. First calculates rates for individual
+ neurons and then averages over neurons.
Parameters
----------
@@ -461,7 +472,8 @@ def pop_rate_distribution(data_array, t_min, t_max, num_neur):
rates /= (t_max - t_min) / 1000.
vals, bins = np.histogram(rates, bins=100)
vals = vals / float(np.sum(vals))
- if num_neur > 0. and t_max != t_min and len(data_array) > 0 and len(indices) > 0:
+ if (num_neur > 0. and t_max != t_min
+ and len(data_array) > 0 and len(indices) > 0):
return bins[0:-1], vals, np.mean(rates), np.std(rates)
else:
return np.arange(0, 20., 20. / 100.), np.zeros(100), 0.0, 0.0
@@ -470,7 +482,8 @@ def pop_rate_distribution(data_array, t_min, t_max, num_neur):
def pop_rate_time_series(data_array, num_neur, t_min, t_max,
resolution=10., kernel='binned'):
"""
- Computes time series of the population-averaged rates of a group of neurons.
+ Computes time series of the population-averaged rates of a group
+ of neurons.
Parameters
----------
@@ -485,9 +498,11 @@ def pop_rate_time_series(data_array, num_neur, t_min, t_max,
Number of recorded neurons. Needs to provided explicitly
to avoid corruption of results by silent neurons not
present in the given data.
- kernel : {'gauss_time_window', 'alpha_time_window', 'rect_time_window'}, optional
- Specifies the kernel to be convolved with the spike histogram.
- Defaults to 'binned', which corresponds to no convolution.
+ kernel : {'gauss_time_window', 'alpha_time_window',
+ 'rect_time_window'}, optional
+ Specifies the kernel to be
+ convolved with the spike histogram. Defaults to 'binned',
+ which corresponds to no convolution.
resolution: float, optional
Width of the convolution kernel. Specifically it correponds to:
- 'binned' : bin width of the histogram
diff --git a/multiarea_model/data_multiarea/VisualCortex_Data.py b/multiarea_model/data_multiarea/VisualCortex_Data.py
index 445d0cb1a2e48c460a8f174f11d9140b1874917b..8aa29389b459dc7a9460fbf016dc9d6a162c999e 100644
--- a/multiarea_model/data_multiarea/VisualCortex_Data.py
+++ b/multiarea_model/data_multiarea/VisualCortex_Data.py
@@ -3,11 +3,13 @@
VisualCortexData
================
-This script provides the function process_raw_data which fulfils two tasks:
-1) Load the experimental data from the raw data files stored in raw_data/ and stores
- it to viscorted_raw_data.json.
-2) Process the data to derive complete sets of FLN, SLN, neuronal densities
- and laminar thicknesses and store these values to viscortex_processed_data.json.
+This script provides the function process_raw_data which fulfils two
+tasks:
+1) Load the experimental data from the raw data files stored in
+ raw_data/ and stores it to viscorted_raw_data.json.
+2) Process the data to derive complete sets of FLN, SLN, neuronal
+ densities and laminar thicknesses and store these values to
+ viscortex_processed_data.json.
All details of the procedures in this scripts are described in
Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ (2017)
@@ -50,8 +52,8 @@ will be stored in the corresponding dictionaries:
----> SLN_Data
10. Intrinsic connection probabilities from Potjans et al. (2012)
----> intrinsic_connectivity
-11. Layer-specific number of neurons for cat V1 from Potjans et al. (2012)
- constructed from Binzegger et al. (2004)
+11. Layer-specific number of neurons for cat V1 from Potjans et al.
+ (2012) constructed from Binzegger et al. (2004)
----> Num_V1
12a. Thickness of layers from Beaulieu et al. (1983)
---> laminar_Thickness_cat
@@ -60,7 +62,8 @@ will be stored in the corresponding dictionaries:
13. Total cortical thicknesses from Barbas lab
---> total_thickness_data
14. Translation from different schemes to FV91 scheme
-15. Binzegger Data about relating synapse location to location of cell bodies
+15. Binzegger Data about relating synapse location to location of cell
+ bodies
16. Average In-Degree from Cragg 1967 ---> AvInDegree
@@ -772,7 +775,8 @@ def process_raw_data():
if architecture_completed[area] in range(1, 9, 1):
if area in list(neuronal_density_data_FV91_4layers.keys()):
for key in list(neuronal_density_data_FV91_4layers[area].keys()):
- neuronal_densities[area][key] = neuronal_density_data_FV91_4layers[area][key]
+ neuronal_densities[area][key] = neuronal_density_data_FV91_4layers[
+ area][key]
else:
neuronal_densities[area] = category_density[architecture_completed[area]]
else:
@@ -913,7 +917,6 @@ def process_raw_data():
laminar_thicknesses_completed[area][layer] = 0.001 * relative_layer_thicknesses[
area][layer] * total_thicknesses[area] / sum_rel_thick
-
"""
Finally, we compute neuron numbers for each population.
We assume a laminar-specific ratio of excitatory
@@ -921,9 +924,12 @@ def process_raw_data():
"""
EI_ratio = {'23': num_V1['23E']['neurons'] / (
num_V1['23E']['neurons'] + num_V1['23I']['neurons']),
- '4': num_V1['4E']['neurons'] / (num_V1['4E']['neurons'] + num_V1['4I']['neurons']),
- '5': num_V1['5E']['neurons'] / (num_V1['5E']['neurons'] + num_V1['5I']['neurons']),
- '6': num_V1['6E']['neurons'] / (num_V1['6E']['neurons'] + num_V1['6I']['neurons'])}
+ '4': num_V1['4E']['neurons'] / (num_V1['4E']['neurons'] +
+ num_V1['4I']['neurons']),
+ '5': num_V1['5E']['neurons'] / (num_V1['5E']['neurons'] +
+ num_V1['5I']['neurons']),
+ '6': num_V1['6E']['neurons'] / (num_V1['6E']['neurons'] +
+ num_V1['6I']['neurons'])}
"""
Then, we compute the number of neurons in
@@ -933,20 +939,22 @@ def process_raw_data():
realistic_neuronal_numbers = nested_dict()
for area in list(neuronal_densities.keys()):
S = surface_data[area]
- realistic_neuronal_numbers[area]['23E'] = (S * laminar_thicknesses_completed[area]['23'] *
- neuronal_densities[area]['23'] * EI_ratio['23'])
+ ltc = laminar_thicknesses_completed[area]
+ nd = neuronal_densities[area]
+ realistic_neuronal_numbers[area]['23E'] = (S * ltc['23'] *
+ nd['23'] * EI_ratio['23'])
realistic_neuronal_numbers[area]['23I'] = (realistic_neuronal_numbers[area]['23E'] *
(1. - EI_ratio['23']) / EI_ratio['23'])
- realistic_neuronal_numbers[area]['4E'] = (S * laminar_thicknesses_completed[area]['4'] *
- neuronal_densities[area]['4'] * EI_ratio['4'])
+ realistic_neuronal_numbers[area]['4E'] = (S * ltc['4'] *
+ nd['4'] * EI_ratio['4'])
realistic_neuronal_numbers[area]['4I'] = (realistic_neuronal_numbers[area]['4E'] *
(1. - EI_ratio['4']) / EI_ratio['4'])
- realistic_neuronal_numbers[area]['5E'] = (S * laminar_thicknesses_completed[area]['5'] *
- neuronal_densities[area]['5'] * EI_ratio['5'])
+ realistic_neuronal_numbers[area]['5E'] = (S * ltc['5'] *
+ nd['5'] * EI_ratio['5'])
realistic_neuronal_numbers[area]['5I'] = (realistic_neuronal_numbers[area]['5E'] *
(1. - EI_ratio['5']) / EI_ratio['5'])
- realistic_neuronal_numbers[area]['6E'] = (S * laminar_thicknesses_completed[area]['6'] *
- neuronal_densities[area]['6'] * EI_ratio['6'])
+ realistic_neuronal_numbers[area]['6E'] = (S * ltc['6'] *
+ nd['6'] * EI_ratio['6'])
realistic_neuronal_numbers[area]['6I'] = (realistic_neuronal_numbers[area]['6E'] *
(1. - EI_ratio['6']) / EI_ratio['6'])
realistic_neuronal_numbers[area]['total'] = sum(
@@ -1347,8 +1355,10 @@ def process_raw_data():
value = SLN_Data_FV91_mapped[target][source]
s += 1
else:
- x = R_fit[1] * float(np.log(neuronal_densities[target]['overall']) -
- np.log(neuronal_densities[source]['overall'])) + R_fit[0]
+ nd_target = neuronal_densities[target]
+ nd_source = neuronal_densities[source]
+ x = R_fit[1] * float(np.log(nd_target['overall']) -
+ np.log(nd_source['overall'])) + R_fit[0]
value = probit(x)
s2 += 1
SLN_completed[target][source] = value
diff --git a/multiarea_model/data_multiarea/viscortex_processed_data.json b/multiarea_model/data_multiarea/viscortex_processed_data.json
index cccde1b1268150c04c050f5caa39e47a34cb26fd..64ba79c82eff2b6504d6efe13bc1b78a4335f666 100644
--- a/multiarea_model/data_multiarea/viscortex_processed_data.json
+++ b/multiarea_model/data_multiarea/viscortex_processed_data.json
@@ -1 +1 @@
-{"cocomac_completed": {"V1": {"7a": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "IT": {"source_pattern": ["0", "0", 2, "0", "0", "3"], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "LIP": {"source_pattern": ["0", "0", "0", "0", "0", "2"], "target_pattern": null}, "MT": {"source_pattern": ["0", "0", 2, "0", 2, 2], "target_pattern": [2.0, 2.0, 2.0, "0", 2.0, 2.0]}, "PIP": {"source_pattern": null, "target_pattern": null}, "PO": {"source_pattern": null, "target_pattern": null}, "STP": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "STPp": {"source_pattern": ["0", "0", "0", "0", "0", "1"], "target_pattern": null}, "TF": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": [2.0, "0", "0", "0", "0", "0"]}, "TH": {"source_pattern": ["0", "0", "0", "0", "0", 2], "target_pattern": null}, "V1": {"source_pattern": null, "target_pattern": [2.0, 2.0, 2.0, "0", "3", "3"]}, "V2": {"source_pattern": ["0", 2, 2, "0", "3", 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0.25591814163985166, "23": 0.9150072767426801, "4": 0.24388555598467354, "5": 0.299925440925703, "6": 0.35582507159450977}, "VOT": {"1": 0.2270973574098532, "23": 0.8119617203671147, "4": 0.283257043447467, "5": 0.2661486779239949, "6": 0.31575304877370874}, "DP": {"1": 0.25583852489499187, "23": 0.9147226157943377, "4": 0.23389235234048408, "5": 0.29983213340496956, "6": 0.3557143735652168}, "MIP": {"1": 0.19533598932900173, "23": 0.8516649134744475, "4": 0.17189567060952152, "5": 0.15626879146320138, "6": 0.6953961220112461}, "MDP": {"1": 0.25591814163985166, "23": 0.9150072767426801, "4": 0.24388555598467354, "5": 0.299925440925703, "6": 0.35582507159450977}, "MSTd": {"1": 0.25591814163985166, "23": 0.9150072767426801, "4": 0.24388555598467354, "5": 0.299925440925703, "6": 0.35582507159450977}, "VIP": {"1": 0.25289300603205106, "23": 1.1696301528982362, "4": 0.2845046317860575, "5": 0.2054755674010415, "6": 0.15805812877003192}, "LIP": {"1": 0.2523199861163701, "23": 1.0042360848163574, "4": 0.239843388447137, "5": 0.23718840716900164, "6": 0.5664121334511337}, "PITv": {"1": 0.2270973574098532, "23": 0.8119617203671147, "4": 0.283257043447467, "5": 0.2661486779239949, "6": 0.31575304877370874}, "PITd": {"1": 0.2270973574098532, "23": 0.8119617203671147, "4": 0.283257043447467, "5": 0.2661486779239949, "6": 0.31575304877370874}, "AITv": {"1": 0.3366353068216317, "23": 1.2036026573832084, "4": 0.22718631698840488, "5": 0.3945226086071063, "6": 0.46805311019964885}, "MSTl": {"1": 0.25591814163985166, "23": 0.9150072767426801, "4": 0.24388555598467354, "5": 0.299925440925703, "6": 0.35582507159450977}, "FST": {"1": 0.5126175393099899, "23": 0.9007008741548409, "4": 0.17666480168310383, "5": 0.30065493337157506, "6": 0.35669052569196447}, "CITv": {"1": 0.28514775842325774, "23": 1.0195145691207823, "4": 0.19243872440273863, "5": 0.33418133871269773, "6": 0.39646552958623793}, "CITd": {"1": 0.28514775842325774, "23": 1.0195145691207823, "4": 0.19243872440273863, "5": 0.33418133871269773, "6": 0.39646552958623793}, "7a": {"1": 0.34637158771533116, "23": 1.2384136630003206, "4": 0.20769132202217178, "5": 0.4059331257408577, "6": 0.4815903015213188}, "STPp": {"1": 0.2889353100093555, "23": 1.0330565448482978, "4": 0.18498493804617222, "5": 0.33862019198120835, "6": 0.40173168932644016}, "STPa": {"1": 0.2889353100093555, "23": 1.0330565448482978, "4": 0.18498493804617222, "5": 0.33862019198120835, "6": 0.40173168932644016}, "FEF": {"1": 0.21710327455919398, "23": 0.9240806045340052, "4": 0.3451385390428212, "5": 0.37297229219143585, "6": 0.3507052896725441}, "46": {"1": 0.2195874325610917, "23": 0.8222722945096794, "4": 0.17708663916217074, "5": 0.2762551570929863, "6": 0.36479847667407167}, "TF": {"1": 0.2334764331779098, "23": 0.6571929230193017, "4": 0.20753460726925316, "5": 0.23865769578911442, "6": 0.28313834074442085}, "TH": {"1": 0.27703703703703697, "23": 0.6464197530864197, "4": 0.11543209876543209, "5": 0.5694650205761316, "6": 0.2616460905349794}, "AITd": {"1": 0.3366353068216317, "23": 1.2036026573832084, "4": 0.22718631698840488, "5": 0.3945226086071063, "6": 0.46805311019964885}}, "category_density": null}
\ No newline at end of file
diff --git a/multiarea_model/default_params.py b/multiarea_model/default_params.py
index d76b50508d2cdcd968474230126d050c7e82b41b..6d18b3dde74d439c9826b0ff2b1e9f6618e6c34a 100644
--- a/multiarea_model/default_params.py
+++ b/multiarea_model/default_params.py
@@ -184,7 +184,8 @@ connection_params = {
# scaling factor for cortico-cortical connections (chi)
'cc_weights_factor': 1.,
- # factor to scale cortico-cortical inh. weights in relation to exc. weights (chi_I)
+ # factor to scale cortico-cortical inh. weights in relation
+ # to exc. weights (chi_I)
'cc_weights_I_factor': 1.,
# 'switch whether to distribute weights lognormally
diff --git a/multiarea_model/simulation.py b/multiarea_model/simulation.py
index d0b3d478b36625dad6f8f5742e7800b9522d53dd..aceaa2ed0334df49638d2fc0980792f4d13c1e54 100644
--- a/multiarea_model/simulation.py
+++ b/multiarea_model/simulation.py
@@ -8,13 +8,12 @@ Schmidt et al. (2018).
Classes
-------
-Simulation : Loads a parameter file that specifies simulation parameters for a
-simulation of the instance of the model. A simulation is identified by a unique
-hash label.
+Simulation : Loads a parameter file that specifies simulation
+parameters for a simulation of the instance of the model. A simulation
+is identified by a unique hash label.
"""
-import h5py_wrapper as h5w
import json
import nest
import numpy as np
@@ -42,7 +41,8 @@ class Simulation:
"""
Simulation class.
An instance of the simulation class with the given parameters.
- Can be created as a member class of a multiarea_model instance or standalone.
+ Can be created as a member class of a multiarea_model instance
+ or standalone.
Parameters
----------
@@ -227,13 +227,15 @@ class Simulation:
# Loop source area though complete list of areas
for source_area_name in self.network.area_list:
if target_area.name != source_area_name:
- # If source_area is part of the simulated network, connect it to target_area
+ # If source_area is part of the simulated network,
+ # connect it to target_area
if source_area_name in self.areas:
source_area = self.areas[self.areas.index(source_area_name)]
connect(self,
target_area,
source_area)
- # Else, replace the input from source_area with the chosen method
+ # Else, replace the input from source_area with the
+ # chosen method
else:
target_area.create_additional_input(replace_non_simulated_areas,
source_area_name,
@@ -374,8 +376,9 @@ class Area:
return s
def __eq__(self, other):
- # If other is an instance of area, it should be the exact same area
- # This opens the possibility to have multiple instance of one cortical areas
+ # If other is an instance of area, it should be the exact same
+ # area This opens the possibility to have multiple instance of
+ # one cortical areas
if isinstance(other, Area):
return self.name == other.name and self.gids == other.gids
elif isinstance(other, str):
@@ -437,8 +440,8 @@ class Area:
def create_devices(self):
"""
- Create devices for all populations. Depending on the configuration, this
- will create:
+ Create devices for all populations. Depending on the
+ configuration, this will create:
- spike detectors
- voltmeters
- Poisson generators
@@ -499,15 +502,18 @@ class Area:
Parameters
----------
- input_type : str, {'het_current_nonstat', 'hom_poisson_stat', 'het_poisson_stat'}
- Type of input to replace source area. The source area can be replaced by
- Poisson sources with the same global rate rate_ext ('hom_poisson_stat') or
- by specific rates ('het_poisson_stat') or by time-varying specific current
+ input_type : str, {'het_current_nonstat', 'hom_poisson_stat',
+ 'het_poisson_stat'}
+ Type of input to replace source area. The source area can
+ be replaced by Poisson sources with the same global rate
+ rate_ext ('hom_poisson_stat') or by specific rates
+ ('het_poisson_stat') or by time-varying specific current
('het_current_nonstat')
source_area_name: str
Name of the source area to be replaced.
cc_input : dict
- Dictionary of cortico-cortical input of the process replacing the source area.
+ Dictionary of cortico-cortical input of the process
+ replacing the source area.
"""
synapses = extract_area_dict(self.network.synapses,
self.network.structure,
diff --git a/multiarea_model/theory.py b/multiarea_model/theory.py
index e6ef7acbc5ca943340e291efeaca4e5f81a3e7e7..f6535054b485fdd464288e6c63f6e633acb5a5f0 100644
--- a/multiarea_model/theory.py
+++ b/multiarea_model/theory.py
@@ -309,13 +309,16 @@ class Theory():
self.NP['V_reset'],
mu[ii], sigma[ii]) for ii in range(N.size)])
+ # Unit: 1/(mV)**2
slope_sigma = np.array([d_nu_d_sigma_fb_numeric(1.e-3*self.NP['tau_m'],
1.e-3*self.NP['tau_syn'],
1.e-3*self.NP['t_ref'],
self.NP['V_th'],
self.NP['V_reset'],
mu[ii], sigma[ii])*1/(
- 2. * sigma[ii]) for ii in range(N.size)]) # 1/(mV)**2
+ 2. * sigma[ii])
+ for ii in range(N.size)])
+
slope_matrix = np.zeros_like(J)
slope_sigma_matrix = np.zeros_like(J)
for ii in range(N.size):
@@ -337,11 +340,12 @@ class Theory():
Computes radius of eigenvalue spectrum of the stability matrix.
"""
if full_output:
- M, slope, slope_sigma, M, EV, C, V, G_N = self.stability_matrix(rates,
- matrix_filter=matrix_filter,
- vector_filter=vector_filter,
- full_output=full_output,
- replace_cc=replace_cc)
+ (M, slope, slope_sigma,
+ M, EV, C, V, G_N) = self.stability_matrix(rates,
+ matrix_filter=matrix_filter,
+ vector_filter=vector_filter,
+ full_output=full_output,
+ replace_cc=replace_cc)
else:
M = self.stability_matrix(rates, matrix_filter=matrix_filter,
vector_filter=vector_filter,
@@ -349,6 +353,6 @@ class Theory():
EV = np.linalg.eig(M)
lambda_max = np.sqrt(np.max(np.real(EV[0])))
if full_output:
- return lambda_max, slope, slope_sigma, M, EV, C, V, G
+ return lambda_max, slope, slope_sigma, M, EV, C, V
else:
return lambda_max
diff --git a/run_example.py b/run_example.py
index 40251bf2e45cac06ac20dd1b0d4019116a968897..912ec7eb7ff6920270d9519ec5d0fda1264176ba 100644
--- a/run_example.py
+++ b/run_example.py
@@ -1,4 +1,4 @@
-from multiarea_model import MultiAreaModel
+From multiarea_model import MultiAreaModel
from start_jobs import start_job
from config import submit_cmd, jobscript_template