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()