def mutual_information(net_file, net_key, readout_window, nbins=350):
(samps, all_stim_classes, Ninternal) = get_samples(net_file, net_key, readout_window)
nsamps = len(samps)
all_samps = np.array([x[0] for x in samps])
class_indx = np.array([x[1] for x in samps])
Hc = conditional_entropy(all_samps, class_indx, nbins=nbins)
H = entropy(all_samps)
return (H - Hc, H)
def plot_readout_data(net_file, net_key, readout_window):
clrs = ['r', 'g', 'b', 'k', 'c']
(samps, all_stim_classes, Ninternal) = get_samples(net_file, net_key, readout_window)
states = np.array([x[0] for x in samps])
stim_classes = np.array([x[1] for x in samps])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')
for k,sc in enumerate(all_stim_classes):
sc_states = states[stim_classes == sc, :]
ax.scatter(sc_states[:, 0], sc_states[:, 1], sc_states[:, 2], c=clrs[k])
ax.set_title('Post-Stimulus Network State')
plt.axis('tight')
plt.show()
def entropy_ratio(net_file, net_key, readout_window, nbins=27):
(samps, all_stim_classes, Ninternal) = get_samples(net_file, net_key, readout_window)
nsamps = len(samps)
all_samps = np.zeros([nsamps, Ninternal+1])
for k,(state,sc) in enumerate(samps):
all_samps[k, 0:Ninternal] = state
all_samps[k, -1] = sc
#compute unconditional entropy
uc_entropy = entropy(all_samps[:, 0:Ninternal], nbins=nbins)
#compute entropy conditioned on each class
sc_entropy = {}
for sc in all_stim_classes:
indx = all_samps[:, -1] == sc
sc_entropy[sc] = entropy(all_samps[indx, 0:Ninternal], nbins=nbins)
sc_entropies = np.array(sc_entropy.values())
sc_entropy_all = sc_entropies.sum() / len(all_stim_classes)
entropy_ratio = sc_entropy_all / uc_entropy
return (entropy_ratio, uc_entropy, sc_entropy)
