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# National Institute on Deafness and Other Communication Disorders
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# This software/database is a "United States Government Work" under the
# terms of the United States Copyright Act. It was written as part of
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# ***************************************************************************
#
# Large-Scale Neural Modeling software (LSNM)
#
# Section on Brain Imaging and Modeling
# Voice, Speech and Language Branch
# National Institute on Deafness and Other Communication Disorders
# National Institutes of Health
#
# This file (plot_neural_tvb.py) was created on September 13, 2015.
#
#
# Author: Antonio Ulloa. Last updated by Antonio Ulloa on August 29, 2016
# **************************************************************************/
# plot_neural_tvb.py
#
# Plot output data files of Hagmann's brain simulation using TVB and Hybrid TVB/LSNM
# simulation (side to side)
# what are the locations of relevant TVB nodes within TVB array?
# the following are the so-called 'host nodes'
v1_loc = 345
v4_loc = 393
it_loc = 413
fs_loc = 47
d1_loc = 74
d2_loc = 41
fr_loc = 125
# what other TVB nodes are the TVB host nodes connected to (with weights above 0.5)?
# the nodes below were taken from an output given by the script:
# "display_hagmanns_brain_connectivity.py"
v1_cxn = [334, 339, 340, 341, 342, 343, 344, 346, 347, 348, 350, 351, 352, 353,
363, 373, 374, 375, 376, 842, 848, 874, 877]
v4_cxn = [379, 391, 392, 397, 398, 401, 423]
it_cxn = [380, 401, 402, 412]
fs_cxn = [39, 43, 44, 45, 48, 49, 50, 51, 52, 53, 107]
d1_cxn = [41, 42, 44, 48, 53, 54, 55, 67, 71, 72, 73, 75, 76, 77, 129, 130]
d2_cxn = [1, 19, 20, 21, 22, 39, 40, 42, 43, 44, 45, 53, 54, 64, 65, 66, 73, 74, 75]
fr_cxn = [71, 78, 93, 104, 105, 106, 114, 115, 126, 128, 129, 130, 131, 132, 133]
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
# load file containing TVB nodes electrical activity
tvb = np.load('output.36trials/tvb_neuronal.npy')
hybrid_tvb_lsnm = np.load('output.36trials.with_feedback/tvb_neuronal.npy')
# the following nodes have the strongest connections with host nodes
# ... as determined by script "display_hagmann_brain_connectivity"
tvb_v1 = tvb[:, 0, 346, 0]
tvb_v4 = tvb[:, 0, 391, 0]
tvb_it = tvb[:, 0, 412, 0]
tvb_fs = tvb[:, 0, 44, 0]
tvb_d1 = tvb[:, 0, 42, 0]
tvb_d2 = tvb[:, 0, 66, 0]
tvb_fr = tvb[:, 0, 105, 0]
hybrid_v1 = hybrid_tvb_lsnm[:, 0, 346, 0]
hybrid_v4 = hybrid_tvb_lsnm[:, 0, 391, 0]
hybrid_it = hybrid_tvb_lsnm[:, 0, 412, 0]
hybrid_fs = hybrid_tvb_lsnm[:, 0, 44, 0]
hybrid_d1 = hybrid_tvb_lsnm[:, 0, 42, 0]
hybrid_d2 = hybrid_tvb_lsnm[:, 0, 66, 0]
hybrid_fr = hybrid_tvb_lsnm[:, 0, 105, 0]
# Extract number of timesteps from one of the matrices
timesteps = tvb_v1.shape[0]
# what was the duration of simulation in real time (in ms)?
real_duration = 198
print timesteps
# Contruct a numpy array of timesteps (data points provided in data file)
real_time = np.linspace(0, real_duration, num=timesteps)
# increase font size
plt.rcParams.update({'font.size': 20})
# Set up plot
plt.figure()
# Plot V1 module
ax = plt.subplot(7,1,7)
ax.plot(real_time, tvb_v1, color='k', linestyle='--', linewidth=2)
ax.plot(real_time, hybrid_v1, color='k', linewidth=2)
ax.set_yticks([])
ax.set_xlim([0, 5])
#ax.set_ylim([0, 1])
#ax.set_title('SIMULATED ELECTRICAL ACTIVITY, HAGMANNS BRAIN')
plt.ylabel('V1', rotation='horizontal', horizontalalignment='right')
ax = plt.subplot(7,1,6)
ax.plot(real_time, tvb_v4, color='k', linestyle='--', linewidth=2)
ax.plot(real_time, hybrid_v4, color='k', linewidth=2)
ax.set_yticks([])
ax.set_xticks([])
ax.set_xlim(0, 5)
#ax.set_ylim([0, 1])
plt.ylabel('V4', rotation='horizontal', horizontalalignment='right')
ax = plt.subplot(7,1,5)
ax.plot(real_time, tvb_it, color='k', linestyle='--', linewidth=2)
ax.plot(real_time, hybrid_it, color='k', linewidth=2)
ax.set_yticks([])
ax.set_xticks([])
ax.set_xlim(0, 5)
#ax.set_ylim([0, 1])
plt.ylabel('IT', rotation='horizontal', horizontalalignment='right')
ax = plt.subplot(7,1,4)
ax.plot(real_time, tvb_fs, color='k', linestyle='--', linewidth=2)
ax.plot(real_time, hybrid_fs, color='k', linewidth=2)
ax.set_yticks([])
ax.set_xticks([])
ax.set_xlim(0, 5)
#ax.set_ylim([0, 1])
plt.ylabel('FS', rotation='horizontal', horizontalalignment='right')
ax = plt.subplot(7,1,3)
ax.plot(real_time, tvb_d1, color='k', linestyle='--', linewidth=2)
ax.plot(real_time, hybrid_d1, color='k', linewidth=2)
ax.set_yticks([])
ax.set_xticks([])
ax.set_xlim(0, 5)
#ax.set_ylim([0, 1])
plt.ylabel('D1', rotation='horizontal', horizontalalignment='right')
ax = plt.subplot(7,1,2)
ax.plot(real_time, tvb_d2, color='k', linestyle='--', linewidth=2)
ax.plot(real_time, hybrid_d2, color='k', linewidth=2)
ax.set_yticks([])
ax.set_xticks([])
ax.set_xlim(0, 5)
#ax.set_ylim([0, 1])
plt.ylabel('D2', rotation='horizontal', horizontalalignment='right')
ax = plt.subplot(7,1,1)
ax.plot(real_time, tvb_fr, color='k', linestyle='--', linewidth=2)
ax.plot(real_time, hybrid_fr, color='k', linewidth=2)
ax.set_yticks([])
ax.set_xticks([])
ax.set_xlim(0, 5)
#ax.set_ylim([0, 1])
plt.ylabel('FR', rotation='horizontal', horizontalalignment='right')
#plt.tight_layout()
# Show the plot on the screen
plt.show()