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Commit e34657eb authored by Didi Hou's avatar Didi Hou Committed by Administrator
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.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
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figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
+ 59
− 59
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......@@ -18,59 +18,59 @@ from scipy import stats
# rc_file('plotstyle.rc')
def visualize_interareal_connectivity(M):
# scale_down_to = 1
# cc_weights_factor = 1.0
# 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']
# replace_non_simulated_areas = 'het_poisson_stat'
scale_down_to = 1
cc_weights_factor = 1.0
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']
replace_non_simulated_areas = 'het_poisson_stat'
# conn_params = {
# 'replace_non_simulated_areas': 'het_poisson_stat',
# 'g': -11.,
# 'K_stable': 'K_stable.npy',
# 'fac_nu_ext_TH': 1.2,
# 'fac_nu_ext_5E': 1.125,
# 'fac_nu_ext_6E': 1.41666667,
# 'av_indegree_V1': 3950.
# }
# input_params = {
# 'rate_ext': 10.
# }
# neuron_params = {
# 'V0_mean': -150.,
# 'V0_sd': 50.}
# 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',
# 'input_params': input_params, # Input parameters
# 'connection_params': conn_params, # Connection parameters
# 'neuron_params': neuron_params # Neuron parameters
# }
# sim_params = {
# 'areas_simulated': areas_simulated,
# 't_sim': 2000., # 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_params = {
# 'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01
# }
# M_full_scale = MultiAreaModel(network_params,
# simulation=True,
# sim_spec=sim_params,
# theory=True,
# theory_spec=theory_params)
conn_params = {
'replace_non_simulated_areas': 'het_poisson_stat',
'g': -11.,
'K_stable': 'K_stable.npy',
'fac_nu_ext_TH': 1.2,
'fac_nu_ext_5E': 1.125,
'fac_nu_ext_6E': 1.41666667,
'av_indegree_V1': 3950.
}
input_params = {
'rate_ext': 10.
}
neuron_params = {
'V0_mean': -150.,
'V0_sd': 50.}
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',
'input_params': input_params, # Input parameters
'connection_params': conn_params, # Connection parameters
'neuron_params': neuron_params # Neuron parameters
}
sim_params = {
'areas_simulated': areas_simulated,
't_sim': 2000., # 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_params = {
'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01
}
M_full_scale = MultiAreaModel(network_params,
simulation=True,
sim_spec=sim_params,
theory=True,
theory_spec=theory_params)
"""
Figure layout
......@@ -87,7 +87,7 @@ def visualize_interareal_connectivity(M):
pl.rcParams['figure.figsize'] = (width, height)
fig = pl.figure()
fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=12, y=1.05)
fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=15, y=1.05)
axes = {}
# gs1 = gridspec.GridSpec(2, 2)
......@@ -223,8 +223,8 @@ def visualize_interareal_connectivity(M):
conn_matrix_full_scale = np.zeros((32, 32))
for i, area1 in enumerate(area_list[::-1]):
for j, area2 in enumerate(area_list):
conn_matrix_full_scale[i][j] = M.K_areas[area1][
area2] / np.sum(list(M.K_areas[area1].values()))
conn_matrix_full_scale[i][j] = M_full_scale.K_areas[area1][
area2] / np.sum(list(M_full_scale.K_areas[area1].values()))
ax = axes['B']
ax.yaxis.set_ticks_position("none")
......@@ -241,10 +241,10 @@ def visualize_interareal_connectivity(M):
X, Y = np.meshgrid(x, y)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_ylabel('Target area')
ax.set_xlabel('Source area')
......@@ -366,10 +366,10 @@ def visualize_interareal_connectivity(M):
X, Y = np.meshgrid(x, y)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_ylabel('Target area')
ax.set_xlabel('Source area')
......
figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
+ 59
− 59
View file @ e34657eb
......@@ -18,59 +18,59 @@ from scipy import stats
# rc_file('plotstyle.rc')
def visualize_interareal_connectivity(M):
# scale_down_to = 1
# cc_weights_factor = 1.0
# 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']
# replace_non_simulated_areas = 'het_poisson_stat'
scale_down_to = 1
cc_weights_factor = 1.0
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']
replace_non_simulated_areas = 'het_poisson_stat'
# conn_params = {
# 'replace_non_simulated_areas': 'het_poisson_stat',
# 'g': -11.,
# 'K_stable': 'K_stable.npy',
# 'fac_nu_ext_TH': 1.2,
# 'fac_nu_ext_5E': 1.125,
# 'fac_nu_ext_6E': 1.41666667,
# 'av_indegree_V1': 3950.
# }
# input_params = {
# 'rate_ext': 10.
# }
# neuron_params = {
# 'V0_mean': -150.,
# 'V0_sd': 50.}
# 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',
# 'input_params': input_params, # Input parameters
# 'connection_params': conn_params, # Connection parameters
# 'neuron_params': neuron_params # Neuron parameters
# }
# sim_params = {
# 'areas_simulated': areas_simulated,
# 't_sim': 2000., # 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_params = {
# 'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01
# }
# M_full_scale = MultiAreaModel(network_params,
# simulation=True,
# sim_spec=sim_params,
# theory=True,
# theory_spec=theory_params)
conn_params = {
'replace_non_simulated_areas': 'het_poisson_stat',
'g': -11.,
'K_stable': 'K_stable.npy',
'fac_nu_ext_TH': 1.2,
'fac_nu_ext_5E': 1.125,
'fac_nu_ext_6E': 1.41666667,
'av_indegree_V1': 3950.
}
input_params = {
'rate_ext': 10.
}
neuron_params = {
'V0_mean': -150.,
'V0_sd': 50.}
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',
'input_params': input_params, # Input parameters
'connection_params': conn_params, # Connection parameters
'neuron_params': neuron_params # Neuron parameters
}
sim_params = {
'areas_simulated': areas_simulated,
't_sim': 2000., # 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_params = {
'dt': 0.1 # The time step of the mean-field theory integration, by default: 0.01
}
M_full_scale = MultiAreaModel(network_params,
simulation=True,
sim_spec=sim_params,
theory=True,
theory_spec=theory_params)
"""
Figure layout
......@@ -87,7 +87,7 @@ def visualize_interareal_connectivity(M):
pl.rcParams['figure.figsize'] = (width, height)
fig = pl.figure()
fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=12, y=1.05)
fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=15, y=1.05)
axes = {}
# gs1 = gridspec.GridSpec(2, 2)
......@@ -223,8 +223,8 @@ def visualize_interareal_connectivity(M):
conn_matrix_full_scale = np.zeros((32, 32))
for i, area1 in enumerate(area_list[::-1]):
for j, area2 in enumerate(area_list):
conn_matrix_full_scale[i][j] = M.K_areas[area1][
area2] / np.sum(list(M.K_areas[area1].values()))
conn_matrix_full_scale[i][j] = M_full_scale.K_areas[area1][
area2] / np.sum(list(M_full_scale.K_areas[area1].values()))
ax = axes['B']
ax.yaxis.set_ticks_position("none")
......@@ -241,10 +241,10 @@ def visualize_interareal_connectivity(M):
X, Y = np.meshgrid(x, y)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_ylabel('Target area')
ax.set_xlabel('Source area')
......@@ -366,10 +366,10 @@ def visualize_interareal_connectivity(M):
X, Y = np.meshgrid(x, y)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_ylabel('Target area')
ax.set_xlabel('Source area')
......
multi-area-model.ipynb
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− 22
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multiarea_model/data_multiarea/viscortex_processed_data.json
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− 1
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