pietromarchesi

import numpy as np

n_stim                = 3
n_responses           = 2
trial_size            = 10
n_trials_per_stimtype = 50
n_neurons             = 4
single_trial_data = np.zeros([n_trials_per_stimtype, n_neurons, n_stim, n_responses, trial_size])
single_trial_data.fill(np.NaN)

pietromarchesi

import numpy as np

n_stim                = 3
n_responses           = 2
trial_size            = 10
n_trials_per_stimtype = 50
n_neurons             = 84
single_trial_data = np.zeros([n_trials_per_stimtype, n_neurons, n_stim, n_responses, trial_size])
single_trial_data.fill(np.NaN)

pietromarchesi

pietromarchesi

pietromarchesi

library(psych)

"
Analyze the difference between change and initial value. 

Source:
Tu, Y. K. (2016). Testing the relation between percentage change 
and baseline value. Scientific reports, 6.
"

pietromarchesi

'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

These computations rely on nearest-neighbor statistics
'''
import numpy as np