# Only keep 10 weights of interest
relevant_features = np.random.randint(0, n_features, 10)
for i in relevant_features:
w[i] = stats.norm.rvs(loc=0, scale=1. / np.sqrt(lambda_))
# Create noite with a precision alpha of 50.
alpha_ = 50.
noise = stats.norm.rvs(loc=0, scale=1. / np.sqrt(alpha_), size=n_samples)
# Create the target
y = np.dot(X, w) + noise
################################################################################
# Fit the ARD Regression
clf = ARDRegression(compute_score = True)
clf.fit(X, y)
ols = LinearRegression()
ols.fit(X, y)
################################################################################
# Plot the true weights, the estimated weights and the histogram of the
# weights
pl.figure(figsize=(6, 5))
pl.title("Weights of the model")
pl.plot(clf.coef_, 'b-', label="ARD estimate")
pl.plot(ols.coef_, 'r--', label="OLS estimate")
pl.plot(w, 'g-', label="Ground truth")
pl.xlabel("Features")
pl.ylabel("Values of the weights")
pl.legend(loc=1)
pl.figure(figsize=(6, 5))
def test_contrast(p_intra, p_inter, num_trials, data_path, muscimol_amount=0*nS, injection_site=0, single_inh_pop=False):
num_groups=2
trial_duration=1.0*second
wta_params=default_params()
wta_params.p_b_e=0.1
wta_params.p_x_e=0.1
wta_params.p_e_e=p_intra
wta_params.p_e_i=p_inter
wta_params.p_i_i=p_intra
wta_params.p_i_e=p_inter
input_sum=40.0
contrast_range=[0.0, 0.0625, 0.125, 0.25, 0.5, 1.0]
trial_contrast=np.zeros([len(contrast_range)*num_trials,1])
trial_max_bold=np.zeros(len(contrast_range)*num_trials)
trial_max_exc_bold=np.zeros(len(contrast_range)*num_trials)
for i,contrast in enumerate(contrast_range):
print('Testing contrast %0.4f' % contrast)
inputs=np.zeros(2)
inputs[0]=(input_sum*(contrast+1.0)/2.0)
inputs[1]=input_sum-inputs[0]
for j in range(num_trials):
print('Trial %d' % j)
trial_contrast[i*num_trials+j]=contrast
np.random.shuffle(inputs)
input_freq=np.zeros(num_groups)
for k in range(num_groups):
input_freq[k]=float(inputs[k])*Hz
file='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f.contrast.%0.4f.trial.%d.h5' %\
(num_groups, trial_duration, wta_params.p_b_e, wta_params.p_x_e, wta_params.p_e_e, wta_params.p_e_i,
wta_params.p_i_i, wta_params.p_i_e, contrast, j)
out_file=None
if data_path is not None:
out_file=os.path.join(data_path,file)
wta_monitor=run_wta(wta_params, num_groups, input_freq, trial_duration, record_neuron_state=True,
output_file=out_file, muscimol_amount=muscimol_amount, injection_site=injection_site, single_inh_pop=single_inh_pop)
trial_max_bold[i*num_trials+j]=np.max(wta_monitor.voxel_monitor['y'].values)
trial_max_exc_bold[i*num_trials+j]=np.max(wta_monitor.voxel_exc_monitor['y'].values)
x_min=np.min(contrast_range)
x_max=np.max(contrast_range)
fig=plt.figure()
clf=LinearRegression()
clf.fit(trial_contrast,trial_max_bold)
a=clf.coef_[0]
b=clf.intercept_
plt.plot(trial_contrast, trial_max_bold, 'x')
plt.plot([x_min,x_max],[a*x_min+b,a*x_max+b],'--')
plt.xlabel('Input Contrast')
plt.ylabel('Max BOLD')
plt.show()
fig=plt.figure()
clf=LinearRegression()
clf.fit(trial_contrast,trial_max_exc_bold)
a=clf.coef_[0]
b=clf.intercept_
plt.plot(trial_contrast, trial_max_exc_bold, 'o')
plt.plot([x_min,x_max],[a*x_min+b,a*x_max+b],'--')
plt.xlabel('Input Contrast')
plt.ylabel('Max BOLD (exc only)')
plt.show()
def test_contrast(p_intra,
p_inter,
num_trials,
data_path,
muscimol_amount=0 * nS,
injection_site=0,
single_inh_pop=False):
num_groups = 2
trial_duration = 1.0 * second
wta_params = default_params()
wta_params.p_b_e = 0.1
wta_params.p_x_e = 0.1
wta_params.p_e_e = p_intra
wta_params.p_e_i = p_inter
wta_params.p_i_i = p_intra
wta_params.p_i_e = p_inter
input_sum = 40.0
contrast_range = [0.0, 0.0625, 0.125, 0.25, 0.5, 1.0]
trial_contrast = np.zeros([len(contrast_range) * num_trials, 1])
trial_max_bold = np.zeros(len(contrast_range) * num_trials)
trial_max_exc_bold = np.zeros(len(contrast_range) * num_trials)
for i, contrast in enumerate(contrast_range):
print('Testing contrast %0.4f' % contrast)
inputs = np.zeros(2)
inputs[0] = (input_sum * (contrast + 1.0) / 2.0)
inputs[1] = input_sum - inputs[0]
for j in range(num_trials):
print('Trial %d' % j)
trial_contrast[i * num_trials + j] = contrast
np.random.shuffle(inputs)
input_freq = np.zeros(num_groups)
for k in range(num_groups):
input_freq[k] = float(inputs[k]) * Hz
file='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f.contrast.%0.4f.trial.%d.h5' %\
(num_groups, trial_duration, wta_params.p_b_e, wta_params.p_x_e, wta_params.p_e_e, wta_params.p_e_i,
wta_params.p_i_i, wta_params.p_i_e, contrast, j)
out_file = None
if data_path is not None:
out_file = os.path.join(data_path, file)
wta_monitor = run_wta(wta_params,
num_groups,
input_freq,
trial_duration,
record_neuron_state=True,
output_file=out_file,
muscimol_amount=muscimol_amount,
injection_site=injection_site,
single_inh_pop=single_inh_pop)
trial_max_bold[i * num_trials + j] = np.max(
wta_monitor.voxel_monitor['y'].values)
trial_max_exc_bold[i * num_trials + j] = np.max(
wta_monitor.voxel_exc_monitor['y'].values)
x_min = np.min(contrast_range)
x_max = np.max(contrast_range)
fig = plt.figure()
clf = LinearRegression()
clf.fit(trial_contrast, trial_max_bold)
a = clf.coef_[0]
b = clf.intercept_
plt.plot(trial_contrast, trial_max_bold, 'x')
plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
plt.xlabel('Input Contrast')
plt.ylabel('Max BOLD')
plt.show()
fig = plt.figure()
clf = LinearRegression()
clf.fit(trial_contrast, trial_max_exc_bold)
a = clf.coef_[0]
b = clf.intercept_
plt.plot(trial_contrast, trial_max_exc_bold, 'o')
plt.plot([x_min, x_max], [a * x_min + b, a * x_max + b], '--')
plt.xlabel('Input Contrast')
plt.ylabel('Max BOLD (exc only)')
plt.show()
def test_contrast_lesion(p_intra, p_inter, trial_numbers, data_path, muscimol_amount=0*nS, injection_site=0,
single_inh_pop=False, plot_summary=True):
num_groups=2
trial_duration=1.0*second
wta_params=default_params()
wta_params.p_b_e=0.1
wta_params.p_x_e=0.1
wta_params.p_e_e=p_intra
wta_params.p_e_i=p_inter
wta_params.p_i_i=p_intra
wta_params.p_i_e=p_inter
input_sum=40.0
contrast_range=[0.0, 0.0625, 0.125, 0.25, 0.5, 1.0]
num_trials=len(trial_numbers)
trial_contrast=np.zeros([len(contrast_range)*num_trials,1])
trial_max_bold=np.zeros(len(contrast_range)*num_trials)
trial_max_exc_bold=np.zeros(len(contrast_range)*num_trials)
for i,contrast in enumerate(contrast_range):
print('Testing contrast %0.4f' % contrast)
inputs=np.zeros(2)
inputs[0]=(input_sum*(contrast+1.0)/2.0)
inputs[1]=input_sum-inputs[0]
for j,trial_idx in enumerate(trial_numbers):
print('Trial %d' % trial_idx)
trial_contrast[i*num_trials+j]=contrast
np.random.shuffle(inputs)
input_freq=np.zeros(num_groups)
for k in range(num_groups):
input_freq[k]=float(inputs[k])*Hz
file='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f.contrast.%0.4f.trial.%d.h5' %\
(num_groups, trial_duration, wta_params.p_b_e, wta_params.p_x_e, wta_params.p_e_e, wta_params.p_e_i,
wta_params.p_i_i, wta_params.p_i_e, contrast, trial_idx)
out_file=None
if not data_path is None:
out_file=os.path.join(data_path,file)
wta_monitor=run_wta(wta_params, num_groups, input_freq, trial_duration, output_file=out_file,
single_inh_pop=single_inh_pop, record_spikes=False, record_lfp=False, save_summary_only=True)
trial_max_bold[i*num_trials+j]=np.max(wta_monitor.voxel_monitor['y'].values)
trial_max_exc_bold[i*num_trials+j]=np.max(wta_monitor.voxel_exc_monitor['y'].values)
lesioned_trial_max_bold=np.zeros(len(contrast_range)*num_trials)
lesioned_trial_max_exc_bold=np.zeros(len(contrast_range)*num_trials)
for i,contrast in enumerate(contrast_range):
print('Testing contrast %0.4f' % contrast)
inputs=np.zeros(2)
inputs[0]=(input_sum*(contrast+1.0)/2.0)
inputs[1]=input_sum-inputs[0]
for j,trial_idx in enumerate(trial_numbers):
print('Trial %d' % j)
trial_contrast[i*num_trials+j]=contrast
np.random.shuffle(inputs)
input_freq=np.zeros(num_groups)
for k in range(num_groups):
input_freq[k]=float(inputs[k])*Hz
file='lesioned.wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f.contrast.%0.4f.trial.%d.h5' %\
(num_groups, trial_duration, wta_params.p_b_e, wta_params.p_x_e, wta_params.p_e_e, wta_params.p_e_i,
wta_params.p_i_i, wta_params.p_i_e, contrast, trial_idx)
out_file=None
if not data_path is None:
out_file=os.path.join(data_path,file)
wta_monitor=run_wta(wta_params, num_groups, input_freq, trial_duration, output_file=out_file,
muscimol_amount=muscimol_amount, injection_site=injection_site, single_inh_pop=single_inh_pop,
record_spikes=False, record_lfp=False, save_summary_only=True)
lesioned_trial_max_bold[i*num_trials+j]=np.max(wta_monitor.voxel_monitor['y'].values)
lesioned_trial_max_exc_bold[i*num_trials+j]=np.max(wta_monitor.voxel_exc_monitor['y'].values)
if plot_summary:
x_min=np.min(contrast_range)
x_max=np.max(contrast_range)
fig=plt.figure()
control_clf=LinearRegression()
control_clf.fit(trial_contrast,trial_max_bold)
control_a=control_clf.coef_[0]
control_b=control_clf.intercept_
lesion_clf=LinearRegression()
lesion_clf.fit(trial_contrast,lesioned_trial_max_bold)
lesion_a=lesion_clf.coef_[0]
lesion_b=lesion_clf.intercept_
plt.plot(trial_contrast, trial_max_bold, 'xb')
plt.plot(trial_contrast, lesioned_trial_max_bold, 'xr')
plt.plot([x_min,x_max],[control_a*x_min+control_b,control_a*x_max+control_b],'--b',label='Control')
plt.plot([x_min,x_max],[lesion_a*x_min+lesion_b,lesion_a*x_max+lesion_b],'--r',label='Lesioned')
plt.xlabel('Input Contrast')
plt.ylabel('Max BOLD')
plt.legend()
plt.show()
fig=plt.figure()
control_exc_clf=LinearRegression()
control_exc_clf.fit(trial_contrast,trial_max_exc_bold)
control_exc_a=control_exc_clf.coef_[0]
control_exc_b=control_exc_clf.intercept_
lesion_exc_clf=LinearRegression()
lesion_exc_clf.fit(trial_contrast,lesioned_trial_max_exc_bold)
lesion_exc_a=lesion_exc_clf.coef_[0]
lesion_exc_b=lesion_exc_clf.intercept_
plt.plot(trial_contrast, trial_max_exc_bold, 'ob')
plt.plot(trial_contrast, lesioned_trial_max_exc_bold, 'or')
plt.plot([x_min,x_max],[control_exc_a*x_min+control_exc_b,control_exc_a*x_max+control_exc_b],'--b',label='Control')
plt.plot([x_min,x_max],[lesion_exc_a*x_min+lesion_exc_b,lesion_exc_a*x_max+lesion_exc_b],'--r',label='Lesioned')
plt.xlabel('Input Contrast')
plt.ylabel('Max BOLD (exc only)')
plt.legend()
plt.show()
def test_contrast_lesion(p_intra,
p_inter,
trial_numbers,
data_path,
muscimol_amount=0 * nS,
injection_site=0,
single_inh_pop=False,
plot_summary=True):
num_groups = 2
trial_duration = 1.0 * second
wta_params = default_params()
wta_params.p_b_e = 0.1
wta_params.p_x_e = 0.1
wta_params.p_e_e = p_intra
wta_params.p_e_i = p_inter
wta_params.p_i_i = p_intra
wta_params.p_i_e = p_inter
input_sum = 40.0
contrast_range = [0.0, 0.0625, 0.125, 0.25, 0.5, 1.0]
num_trials = len(trial_numbers)
trial_contrast = np.zeros([len(contrast_range) * num_trials, 1])
trial_max_bold = np.zeros(len(contrast_range) * num_trials)
trial_max_exc_bold = np.zeros(len(contrast_range) * num_trials)
for i, contrast in enumerate(contrast_range):
print('Testing contrast %0.4f' % contrast)
inputs = np.zeros(2)
inputs[0] = (input_sum * (contrast + 1.0) / 2.0)
inputs[1] = input_sum - inputs[0]
for j, trial_idx in enumerate(trial_numbers):
print('Trial %d' % trial_idx)
trial_contrast[i * num_trials + j] = contrast
np.random.shuffle(inputs)
input_freq = np.zeros(num_groups)
for k in range(num_groups):
input_freq[k] = float(inputs[k]) * Hz
file='wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f.contrast.%0.4f.trial.%d.h5' %\
(num_groups, trial_duration, wta_params.p_b_e, wta_params.p_x_e, wta_params.p_e_e, wta_params.p_e_i,
wta_params.p_i_i, wta_params.p_i_e, contrast, trial_idx)
out_file = None
if not data_path is None:
out_file = os.path.join(data_path, file)
wta_monitor = run_wta(wta_params,
num_groups,
input_freq,
trial_duration,
output_file=out_file,
single_inh_pop=single_inh_pop,
record_spikes=False,
record_lfp=False,
save_summary_only=True)
trial_max_bold[i * num_trials + j] = np.max(
wta_monitor.voxel_monitor['y'].values)
trial_max_exc_bold[i * num_trials + j] = np.max(
wta_monitor.voxel_exc_monitor['y'].values)
lesioned_trial_max_bold = np.zeros(len(contrast_range) * num_trials)
lesioned_trial_max_exc_bold = np.zeros(len(contrast_range) * num_trials)
for i, contrast in enumerate(contrast_range):
print('Testing contrast %0.4f' % contrast)
inputs = np.zeros(2)
inputs[0] = (input_sum * (contrast + 1.0) / 2.0)
inputs[1] = input_sum - inputs[0]
for j, trial_idx in enumerate(trial_numbers):
print('Trial %d' % j)
trial_contrast[i * num_trials + j] = contrast
np.random.shuffle(inputs)
input_freq = np.zeros(num_groups)
for k in range(num_groups):
input_freq[k] = float(inputs[k]) * Hz
file='lesioned.wta.groups.%d.duration.%0.3f.p_b_e.%0.3f.p_x_e.%0.3f.p_e_e.%0.3f.p_e_i.%0.3f.p_i_i.%0.3f.p_i_e.%0.3f.contrast.%0.4f.trial.%d.h5' %\
(num_groups, trial_duration, wta_params.p_b_e, wta_params.p_x_e, wta_params.p_e_e, wta_params.p_e_i,
wta_params.p_i_i, wta_params.p_i_e, contrast, trial_idx)
out_file = None
if not data_path is None:
out_file = os.path.join(data_path, file)
wta_monitor = run_wta(wta_params,
num_groups,
input_freq,
trial_duration,
output_file=out_file,
muscimol_amount=muscimol_amount,
injection_site=injection_site,
single_inh_pop=single_inh_pop,
record_spikes=False,
record_lfp=False,
save_summary_only=True)
lesioned_trial_max_bold[i * num_trials + j] = np.max(
wta_monitor.voxel_monitor['y'].values)
lesioned_trial_max_exc_bold[i * num_trials + j] = np.max(
wta_monitor.voxel_exc_monitor['y'].values)
if plot_summary:
x_min = np.min(contrast_range)
x_max = np.max(contrast_range)
fig = plt.figure()
control_clf = LinearRegression()
control_clf.fit(trial_contrast, trial_max_bold)
control_a = control_clf.coef_[0]
control_b = control_clf.intercept_
lesion_clf = LinearRegression()
lesion_clf.fit(trial_contrast, lesioned_trial_max_bold)
lesion_a = lesion_clf.coef_[0]
lesion_b = lesion_clf.intercept_
plt.plot(trial_contrast, trial_max_bold, 'xb')
plt.plot(trial_contrast, lesioned_trial_max_bold, 'xr')
plt.plot(
[x_min, x_max],
[control_a * x_min + control_b, control_a * x_max + control_b],
'--b',
label='Control')
plt.plot([x_min, x_max],
[lesion_a * x_min + lesion_b, lesion_a * x_max + lesion_b],
'--r',
label='Lesioned')
plt.xlabel('Input Contrast')
plt.ylabel('Max BOLD')
plt.legend()
plt.show()
fig = plt.figure()
control_exc_clf = LinearRegression()
control_exc_clf.fit(trial_contrast, trial_max_exc_bold)
control_exc_a = control_exc_clf.coef_[0]
control_exc_b = control_exc_clf.intercept_
lesion_exc_clf = LinearRegression()
lesion_exc_clf.fit(trial_contrast, lesioned_trial_max_exc_bold)
lesion_exc_a = lesion_exc_clf.coef_[0]
lesion_exc_b = lesion_exc_clf.intercept_
plt.plot(trial_contrast, trial_max_exc_bold, 'ob')
plt.plot(trial_contrast, lesioned_trial_max_exc_bold, 'or')
plt.plot([x_min, x_max], [
control_exc_a * x_min + control_exc_b,
control_exc_a * x_max + control_exc_b
],
'--b',
label='Control')
plt.plot([x_min, x_max], [
lesion_exc_a * x_min + lesion_exc_b,
lesion_exc_a * x_max + lesion_exc_b
],
'--r',
label='Lesioned')
plt.xlabel('Input Contrast')
plt.ylabel('Max BOLD (exc only)')
plt.legend()
plt.show()
