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REVIEW: helpers_{analysis, network}.py #205

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Sep 25, 2023
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REVIEW: helpers_{analysis, network}.py #205

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15e15bb
Write test for adjust_ext_indegrees_to_preserve_mean_input()
jhnnsnk Sep 25, 2023
3c04b09
Remove parameters from testing values from first arxiv preprint in ne…
jhnnsnk Sep 25, 2023
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2 changes: 1 addition & 1 deletion mesocircuit/parameterization/base_network_params.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,7 +110,7 @@
4.20258522768281,
478.851911854799,
220.365998044097]]),
# use the total number of synapses from the arxiv preprint
# legacy parameter to be removed
'use_old_full_num_synapses': False,
# connection probabilities from thalamus
'conn_probs_th_1mm2':
Expand Down
77 changes: 8 additions & 69 deletions mesocircuit/parameterization/helpers_network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,7 +129,7 @@ def derive_dependent_parameters(base_net_dict):
net_dict['full_num_neurons_sum'] = \
np.round(np.sum(full_num_neurons)).astype(int)

# TODO adjust when parameters are final
# in-degrees from connectivity matrix of 1mm2 model
if net_dict['base_model'] == 'PD2014':
conn_probs_1mm2 = np.zeros(pop_shape)
conn_probs_1mm2[:, :-1] = net_dict['conn_probs_1mm2_PD2014']
Expand Down Expand Up @@ -180,8 +180,6 @@ def derive_dependent_parameters(base_net_dict):
net_dict['K_area_scaling'] = scale_indegrees_to_mask_radius(
mask_radius=net_dict['mask_radius'],
decay=net_dict['beta'], profile='exponential')
elif net_dict['K_area_scale_method'] == 'old':
net_dict['K_area_scaling'] = old_indegree_scaling()
else:
raise Exception('K_area_scale_method incorrect.')

Expand All @@ -197,41 +195,13 @@ def derive_dependent_parameters(base_net_dict):
full_indegrees[int(target)][int(source)] *= factor

# adjust external indegrees to compensate for changed interal indegrees
# (either from upscaling or from specific modifications),
# this does not apply to thalamus
if net_dict['use_old_external_indegrees']:
full_ext_indegrees = np.array([1701.752083840871, 1621.1260232237535,
1864.2670612706677, 2443.1073800264094,
1939.3421758918564, 1724.031970381723,
3051.092229479683, 2245.9301959805216])
else:
full_ext_indegrees = adjust_ext_indegrees_to_preserve_mean_input(
indegrees_1mm2[:, :-1], full_indegrees[:, :-1],
ext_indegrees_1mm2,
net_dict['mean_rates_' + net_dict['base_model']],
net_dict['bg_rate'],
PSC_matrix_mean_tau_syn_default[:, :-1],
PSC_ext_tau_syn_default)

if net_dict['use_old_full_num_synapses']:
full_num_synapses = np.array(
[[828882273.000247, 392373667.961514, 379559653.820154, 177882649.346106, 62060854.238871, -0.0, 43300793.76242, -0.0, 0.],
[312286600.613029, 89730383.554041, 77382503.429251,
31553447.415552, 40984338.606205, -0.0, 6748860.463846, -0.0, 0.],
[66788887.061005, 14434115.3848, 457490148.137505, 264727072.297861,
13627055.533483, 133952.826066, 273856633.522986, -0.0, 37310957.265509],
[150193546.203107, 1773566.976223, 182931727.34158, 106203163.37557,
1677819.868631, -0.0, 160119167.736337, -0.0, 5865966.713957],
[193400955.023919, 33747733.417738, 102879922.954544, 2898312.47049,
37512090.889222, 39236078.439484, 27267785.471299, -0.0, 0.],
[23140305.947658, 3200662.840475, 11486196.96599, 245608.859384,
4751682.267154, 6233644.993468, 2523097.347322, -0.0, 0.],
[88907168.023082, 10606303.565132, 127442417.574458, 25062536.950038, 76562189.60893,
5782052.956883, 157215604.064241, 184383740.844691, 12742511.126558],
[42517558.793266, 329037.040456, 4202790.94696, 154504.650571, 7584616.411093, 480652.990745, 53551271.623572, 24121713.759947, 997799.00007]])

full_indegrees = (full_num_synapses /
full_num_neurons[:-1][:, np.newaxis])
full_ext_indegrees = adjust_ext_indegrees_to_preserve_mean_input(
indegrees_1mm2[:, :-1], full_indegrees[:, :-1],
ext_indegrees_1mm2,
net_dict['mean_rates_' + net_dict['base_model']],
net_dict['bg_rate'],
PSC_matrix_mean_tau_syn_default[:, :-1],
PSC_ext_tau_syn_default)

net_dict['full_indegrees'] = np.round(full_indegrees).astype(int)
full_num_synapses = full_indegrees * full_num_neurons[:-1][:, np.newaxis]
Expand Down Expand Up @@ -355,22 +325,6 @@ def derive_dependent_parameters(base_net_dict):
return net_dict


def old_indegree_scaling():
# includes old conn_prob_modifications
# TC set manually
t = 1.2
K_indegree_scaling = \
np.array([[1.146, 1.105, 1.179, 1.157, 1.185, 1.000, 1.199, 1.000, t],
[1.126, 1.124, 1.185, 1.174, 1.16, 1.000, 1.201, 1.000, t],
[1.199, 1.2, 1.175, 0.956, 1.199, 1.203, 1.178, 1.000, t],
[1.164, 1.202, 1.158, 1.279, 1.201, 1.000, 1.143, 1.000, t],
[1.146, 1.168, 1.175, 1.2, 1.156, 1.025, 1.192, 1.000, t],
[1.172, 1.188, 1.189, 1.202, 0.935, 0.911, 1.198, 1.000, t],
[1.194, 1.199, 1.191, 1.194, 1.171, 1.192, 1.181, 1.071, t],
[1.183, 1.203, 1.201, 1.203, 1.188, 1.199, 1.166, 1.120, t]])
return K_indegree_scaling


def scale_indegrees_to_mask_radius(mask_radius, decay, profile):
"""
Computes a matrix of factors to scale indegrees from a disc of area 1mm2 to
Expand Down Expand Up @@ -456,21 +410,6 @@ def adjust_ext_indegrees_to_preserve_mean_input(

full_ext_indegrees = ext_indegrees_1mm2 + sum_diff_rec_inputs

# TODO could be converted into a unit test
# full = np.zeros_like(mean_rates)
# mm2 = np.zeros_like(mean_rates)
# for i in np.arange(len(PSC_matrix_mean)): # target
# rec_input_full = 0.
# rec_input_1mm2 = 0.
# for j in np.arange(len(PSC_matrix_mean[i])): # source
# rec_input_full += full_indegrees[i][j] * PSC_matrix_mean[i][j] * mean_rates[j]
# rec_input_1mm2 += indegrees_1mm2[i][j] * PSC_matrix_mean[i][j] * mean_rates[j]
#
# diff_rec_input = rec_input_1mm2 - rec_input_full
#
# full[i] = rec_input_full + full_ext_indegrees[i] * PSC_ext * bg_rate
# mm2[i] = rec_input_1mm2 + ext_indegrees_1mm2[i] * PSC_ext * bg_rate

return full_ext_indegrees


Expand Down
71 changes: 71 additions & 0 deletions tests/test_suite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -501,3 +501,74 @@ def mean_distance_sampled(radius, beta, num_nodes, num_distances):
mean_delay_sampled,
1.,
places=1) # one decimal place (10%)

def test_adjust_ext_indegrees_to_preserve_mean_input(self):
'''Test adjusting the external in-degrees to preserve the mean input.'''

# in-degrees here rounded to 0 decimals
indegrees_1mm2 = np.array(
[[1062., 517., 482., 228., 77., 0., 57., 0.],
[1441., 414., 349., 143., 184., 0., 28., 0.],
[76., 17., 548., 383., 17., 0., 324., 0.],
[718., 9., 876., 458., 7., 0., 781., 0.],
[1042., 182., 558., 16., 204., 227., 143., 0.],
[568., 78., 283., 5., 146., 188., 64., 0.],
[159., 20., 227., 46., 139., 11., 288., 356.],
[369., 3., 32., 3., 67., 4., 479., 220.]])
full_indegrees = np.array(
[[1459., 573., 662., 253., 106., 0., 78., 0.],
[1472., 419., 356., 144., 188., 0., 29., 0.],
[105., 19., 753., 425., 23., 0., 445., 0.],
[733., 9., 895., 834., 7., 0., 797., 0.],
[1432., 201., 767., 18., 280., 227., 197., 0.],
[580., 79., 289., 5., 149., 190., 65., 0.],
[219., 22., 312., 51., 191., 12., 395., 296.],
[377., 3., 33., 3., 69., 4., 367., 201.]])
ext_indegrees_1mm2 = np.array(
[1584., 1564., 1569., 1558., 1788., 1562., 2221., 1568.])

# rates rounded to 2 decimals
mean_rates = np.array([0.16, 1.68, 2.41, 2.8, 3.59, 4.23, 2.22, 4.16])
bg_rate = 8.

# PSCs rounded to 0 decimals
PSC_matrix_mean = np.array(
[[88., -966., 176., -966., 88., -966., 88., -966.],
[88., -966., 88., -966., 88., -966., 88., -966.],
[88., -966., 88., -966., 88., -966., 88., -966.],
[88., -966., 88., -966., 88., -966., 88., -966.],
[88., -966., 88., -966., 88., -966., 88., -966.],
[88., -966., 88., -966., 88., -966., 88., -966.],
[88., -966., 88., -966., 88., -966., 88., -966.],
[88., -966., 88., -966., 88., -966., 88., -966.]])
PSC_ext = 88.

# function to be tested
full_ext_indegrees = helpnet.adjust_ext_indegrees_to_preserve_mean_input(
indegrees_1mm2,
full_indegrees,
ext_indegrees_1mm2,
mean_rates,
bg_rate,
PSC_matrix_mean,
PSC_ext)

# check that the total mean input to a neuron is equal in the 1 mm2
# network and the full network
full = np.zeros_like(mean_rates)
mm2 = np.zeros_like(mean_rates)
for i in np.arange(len(PSC_matrix_mean)): # target
rec_input_full = 0.
rec_input_1mm2 = 0.
for j in np.arange(len(PSC_matrix_mean[i])): # source
rec_input_full += full_indegrees[i][j] * \
PSC_matrix_mean[i][j] * mean_rates[j]
rec_input_1mm2 += indegrees_1mm2[i][j] * \
PSC_matrix_mean[i][j] * mean_rates[j]

mm2[i] = rec_input_1mm2 + \
ext_indegrees_1mm2[i] * PSC_ext * bg_rate
full[i] = rec_input_full + \
full_ext_indegrees[i] * PSC_ext * bg_rate

self.assertAlmostEqual(mm2[i], full[i], places=2)