def compute_fig5_data(model_class=Model, δ_J_stim=(1, 1),
δ_J_nmda=(1, 1), δ_J_gaba=(1, 1, 1), desc=''):
"""Compute the data for Figure 5."""
model = model_class(n=n, ΔA=ΔA, ΔB=ΔB, random_seed=1,
δ_J_stim=δ_J_stim, δ_J_gaba=δ_J_gaba, δ_J_nmda=δ_J_nmda)
filename='data/fig5_{}[{}]{}.pickle'.format(desc, n, model.desc)
run_model(model, offers, history_keys=(), filename=filename)
def compute_fig4_data(model_class=Model):
model = model_class(n=n, ΔA=ΔA, ΔB=ΔB, random_seed=1)
filename = 'data/fig4[{}]{}.pickle'.format(n, model.desc)
return run_model(model,
offers,
history_keys=('r_ovb', 'r_2', 'r_I'),
smooth=smooth,
filename=filename)
def compute_fig7_data(model_class=Model, w_p=1.82):
model = model_class(n=n,
ΔA=ΔA,
ΔB=ΔB,
random_seed=1,
w_p=1.82,
hysteresis=True)
filename = 'data/fig7_{}[{}]{}.pickle'.format(w_p, n, model.desc)
return run_model(model, offers, history_keys=('r_2', ), filename=filename)
def compute_fig4_data(model_class=Model):
"""Compute the Figure 4 data used in the Figure_4 notebook.
If the result filename already exists, the computation will be skipped.
:param model_class: set to ReplicatedModel if you want to replicate the published figures.
set to Model to use the 'corrected' model, as described in the article.
"""
model = model_class(n=n, random_seed=1, ΔA=ΔA, ΔB=ΔB)
filename='data/fig4[{}]{}.pickle'.format(n, model.desc)
return run_model(model, offers, history_keys=('r_ovb', 'r_2', 'r_I'), filename=filename)
def compute_fig10_data(model_class=Model, network='symmetric'):
δ_J_stim = {'symmetric': (1 , 1),
'asymmetric': (1.2, 1)}[network]
model = model_class(n=n, ΔA=ΔA, ΔB=ΔB, random_seed=1,
r_o=6, w_p=1.65, J_ampa_rec_in=J_ampa_rec_in,
J_nmda_rec_in=J_nmda_rec_in, J_gaba_rec_in=J_gaba_rec_in,
δ_J_stim=δ_J_stim)
filename = 'data/fig10_{}[{}]{}.pickle'.format(network, n, model.desc)
return run_model(model, offers, history_keys=('r_ovb', 'r_2', 'r_I'), filename=filename)
def compute_fig9_data(model_class, w_p=1.75, ΔJ=30):
"""
If the result filename already exists, the computation will be skipped.
:param model_class: set to ReplicatedModel if you want to replicate the published figures.
set to Model to use the 'corrected' model, as described in the article.
"""
model = model_class(n=n,
ΔA=ΔA,
ΔB=ΔB,
random_seed=1,
δ_J_stim=(1, 1),
w_p=w_p,
ΔJ=ΔJ)
filename = 'data/fig9_{}_{}[{}]{}.pickle'.format(w_p, ΔJ, n, model.desc)
return run_model(model,
offers,
history_keys=('r_1', 'r_2'),
filename=filename,
preprocess=False)
def compute_fig6_data(model_class, w_p):
model = model_class(n=n, ΔA=ΔA, ΔB=ΔB, random_seed=1, w_p=w_p)
filename = 'data/fig6_{}[{}]{}.pickle'.format(w_p, n, model.desc)
return run_model(model, offers, history_keys=('r_2', 'r_I'),
smooth=smooth, filename=filename)