def minimizehist(norm1, norm2, norm3, eped, eped_sigma, spe, spe_sigma,
lambda_1, lambda_2, lambda_3, opct, pap, dap):
p1, p2, p3 = self.fit_function(x, norm1, norm2, norm3, eped,
eped_sigma, spe, spe_sigma,
lambda_1, lambda_2, lambda_3, opct,
pap, dap)
like1 = -2 * poisson.logpmf(y[0], p1)
like2 = -2 * poisson.logpmf(y[1], p2)
like3 = -2 * poisson.logpmf(y[2], p3)
like = np.hstack([like1, like2, like3])
return np.nansum(like)
def minimizehist(norm, eped, eped_sigma, spe, spe_sigma, lambda_, opct,
pap, dap):
p = self.fit_function(x, norm, eped, eped_sigma, spe, spe_sigma,
lambda_, opct, pap, dap)
like = -2 * poisson.logpmf(y, p)
return np.nansum(like)
def remapping_surprise(experiment, distribution):
surprise_list = []
surprise_list_2 = []
surprise_list_neurons_1_2 = []
surprise_list_neurons_2_3 = []
for i, session in enumerate(experiment):
aligned_spikes = session.aligned_rates
n_trials, n_neurons, n_timepoints = aligned_spikes.shape
# Trial indices of choices
predictor_A_Task_1, predictor_A_Task_2, predictor_A_Task_3,\
predictor_B_Task_1, predictor_B_Task_2, predictor_B_Task_3, reward,\
predictor_a_good_task_1,predictor_a_good_task_2, predictor_a_good_task_3 = re.predictors_pokes(session)
t_out = session.t_out
initiate_choice_t = session.target_times #Initiation and Choice Times
ind_choice = (np.abs(t_out - initiate_choice_t[-2])
).argmin() # Find firing rates around choice
ind_after_choice = ind_choice + 7 # 1 sec after choice
spikes_around_choice = aligned_spikes[:, :, ind_choice - 2:
ind_after_choice] # Find firing rates only around choice
mean_spikes_around_choice = np.mean(spikes_around_choice, axis=2)
a_1 = np.where(predictor_A_Task_1 == 1)
a_2 = np.where(predictor_A_Task_2 == 1)
a_3 = np.where(predictor_A_Task_3 == 1)
baseline_mean_trial = np.mean(aligned_spikes, axis=2)
baseline_mean_all_trials = np.mean(baseline_mean_trial, axis=0)
std_all_trials = np.std(baseline_mean_trial, axis=1)
choice_a1 = mean_spikes_around_choice[a_1]
choice_a2 = mean_spikes_around_choice[a_2]
choice_a3 = mean_spikes_around_choice[a_3]
choice_a1_mean = np.mean(choice_a1, axis=0)
choice_a2_mean = np.mean(choice_a2, axis=0)
choice_a3_mean = np.mean(choice_a3, axis=0)
for neuron in range(n_neurons):
trials_firing = mean_spikes_around_choice[:, neuron]
if choice_a1_mean[neuron] > baseline_mean_all_trials[neuron] + 3*std_all_trials[neuron] \
or choice_a2_mean[neuron] > baseline_mean_all_trials[neuron] + 3*std_all_trials[neuron] \
or choice_a3_mean[neuron] > baseline_mean_all_trials[neuron] + 3*std_all_trials[neuron] :
a1_fr = trials_firing[
a_1] # Firing rates on poke A choices in Task 1
a1_poisson = a1_fr.astype(int)
a2_fr = trials_firing[a_2]
a2_poisson = a2_fr.astype(int)
a3_fr = trials_firing[a_3]
a3_poisson = a3_fr.astype(int)
a1_mean = np.nanmean(a1_fr)
a1_poisson = np.nanmean(a1_poisson)
a1_std = np.nanstd(a1_fr)
a2_mean = np.nanmean(a2_fr)
a2_poisson = np.nanmean(a2_poisson)
a2_std = np.nanstd(a2_fr)
a3_poisson = np.nanmean(a3_poisson)
a3_mean = np.nanmean(a3_fr)
a1_fr_last = a1_fr[-15:]
a1_fr_last_poisson = a1_fr_last.astype(int)
a2_fr_first = a2_fr[:15]
a2_fr_first_poisson = a2_fr_first.astype(int)
a2_fr_last = a2_fr[-15:]
a2_fr_last_poisson = a2_fr_last.astype(int)
a3_fr_first = a3_fr[:15]
a3_fr_first_poisson = a3_fr_first.astype(int)
if a1_mean > 0.1 and a2_mean > 0.1 and a3_mean > 0.1:
if distribution == 'Normal':
surprise_a1 = -norm.logpdf(a1_fr_last, a1_mean, a1_std)
surprise_a2 = -norm.logpdf(a2_fr_first, a1_mean,
a1_std)
surprise_a2_last = -norm.logpdf(
a2_fr_last, a2_mean, a2_std)
surprise_a3_first = -norm.logpdf(
a3_fr_first, a2_mean, a2_std)
elif distribution == 'Poisson':
surprise_a1 = -poisson.logpmf(a1_fr_last_poisson,
mu=a1_poisson)
surprise_a2 = -poisson.logpmf(a2_fr_first_poisson,
mu=a1_poisson)
surprise_a2_last = -poisson.logpmf(a2_fr_last_poisson,
mu=a2_poisson)
surprise_a3_first = -poisson.logpmf(
a3_fr_first_poisson, mu=a2_poisson)
surprise_array_t1_2 = np.concatenate(
[surprise_a1, surprise_a2])
surprise_array_t2_3 = np.concatenate(
[surprise_a2_last, surprise_a3_first])
if len(surprise_array_t1_2) > 0 and len(
surprise_array_t2_3) > 0:
surprise_list_neurons_1_2.append(surprise_array_t1_2)
surprise_list_neurons_2_3.append(surprise_array_t2_3)
surprise_list.append(surprise_list_neurons_1_2)
surprise_list_2.append(surprise_list_neurons_2_3)
surprise_list_neurons_1_2 = np.array(surprise_list_neurons_1_2)
surprise_list_neurons_2_3 = np.array(surprise_list_neurons_2_3)
mean_1_2 = np.nanmean(surprise_list_neurons_1_2, axis=0)
std_1_2 = np.nanstd(surprise_list_neurons_1_2, axis=0)
serr_1_2 = std_1_2 / np.sqrt(len(surprise_list_neurons_1_2))
mean_2_3 = np.nanmean(surprise_list_neurons_2_3, axis=0)
std_2_3 = np.nanstd(surprise_list_neurons_2_3, axis=0)
serr_2_3 = std_2_3 / np.sqrt(len(surprise_list_neurons_2_3))
x_pos = np.arange(len(mean_2_3))
task_change = 15
allmeans = mean_1_2 + mean_2_3 / 2
allserr = serr_1_2 + serr_2_3 / 2
plt.figure()
plt.plot(x_pos, mean_1_2)
plt.fill_between(x_pos,
mean_1_2 - serr_1_2,
mean_1_2 + serr_1_2,
alpha=0.2)
plt.axvline(task_change, color='k', linestyle=':')
plt.title('Task 1 and 2')
plt.ylabel('-log(p(X))')
plt.xlabel('Trial #')
plt.figure()
plt.plot(x_pos, mean_2_3)
plt.fill_between(x_pos,
mean_2_3 - serr_2_3,
mean_2_3 + serr_2_3,
alpha=0.2)
plt.axvline(task_change, color='k', linestyle=':')
plt.title('Task 2 and 3')
plt.ylabel('-log(p(X))')
plt.xlabel('Trial #')
plt.figure()
plt.plot(x_pos, allmeans)
plt.fill_between(x_pos, allmeans - allserr, allmeans + allserr, alpha=0.2)
plt.axvline(task_change, color='k', linestyle=':')
plt.title('Combined')
plt.ylabel('-log(p(X))')
plt.xlabel('Trial #')