def fire_area_analysis(fireRange, numberOfNodes, numberOfIterations):
coverageList = []
for f in fireRange:
small = SmallWorldBrain(numberOfNodes=numberOfNodes,
averageDegree=5,
rewireProbability=0.15,
neuronThreshold=1,
refractoryPeriod=1,
numberOfIterations=numberOfIterations,
fractionToFire=f,
inhibitoryEdgeMultiplier=12,
fireRate=0.01,
directed=False)
small.execute(makeRandom=False)
coverageList.append(
check_coverage(brain=small, numberOfNodes=numberOfNodes))
plt.figure()
plt.plot(
np.linspace(0, numberOfIterations, len(coverageList)),
coverageList,
'.',
label='Nodes=%d, ref=%d, threshold=%d, rewire=%.2f, time=%d' %
(small.numberOfNodes, small.refractoryPeriod, small.neuronThreshold,
small.rewireProbability, small.numberOfIterations))
plt.xlabel('Fraction of Nodes Fired Selectively')
plt.ylabel('Coverage of Network')
plt.legend()
def fire_rate_analysis(drivingRates, numberOfNodes, numberOfIterations):
coverageList = []
for r in drivingRates:
small = SmallWorldBrain(numberOfNodes=numberOfNodes,
averageDegree=5,
rewireProbability=0.15,
neuronThreshold=1,
refractoryPeriod=1,
numberOfIterations=numberOfIterations,
fractionToFire=0.05,
inhibitoryEdgeMultiplier=12,
fireRate=r,
directed=False)
small.execute()
coverageList.append(
check_coverage(brain=small, numberOfNodes=numberOfNodes))
plt.figure()
plt.plot(
drivingRates,
coverageList,
'.',
label='Nodes=%d, ref=%d, threshold=%d, rewire=%.2f, time=%d' %
(small.numberOfNodes, small.refractoryPeriod, small.neuronThreshold,
small.rewireProbability, small.numberOfIterations))
plt.xlabel('Driving Rate')
plt.ylabel('Coverage of Network')
plt.legend()
def plot_topology_EEGs(N, t, save=False, load=False, filepath=None):
if load:
res = np.load(filepath)
elif not load:
small = SmallWorldBrain(numberOfNodes=N, averageDegree=5, rewireProbability=0, neuronThreshold=1, refractoryPeriod=1,
numberOfIterations=t, fractionToFire=0.05, inhibitoryEdgeMultiplier=0, fireRate=0.01, directed=False)
small.execute()
lattice = small.get_number_of_excited_neurons()
small = SmallWorldBrain(numberOfNodes=N, averageDegree=5, rewireProbability=0.1, neuronThreshold=1, refractoryPeriod=1,
numberOfIterations=t, fractionToFire=0.05, inhibitoryEdgeMultiplier=0, fireRate=0.01, directed=False)
small.execute()
smallworld = small.get_number_of_excited_neurons()
small = SmallWorldBrain(numberOfNodes=N, averageDegree=5, rewireProbability=0.8, neuronThreshold=1, refractoryPeriod=1,
numberOfIterations=t, fractionToFire=0.05, inhibitoryEdgeMultiplier=0, fireRate=0.01, directed=False)
small.execute()
random = small.get_number_of_excited_neurons()
if save:
np.save(filepath, np.array([lattice, smallworld, random]))
res = np.array([lattice, smallworld, random])
lattice = []
for _, ye in zip(np.arange(t), [list(i) for i in res[0]]):
lattice.append(len(ye))
small = []
for _, ye in zip(np.arange(t), [list(i) for i in res[1]]):
small.append(len(ye))
rand = []
for _, ye in zip(np.arange(t), [list(i) for i in res[2]]):
rand.append(len(ye))
plt.figure()
plt.plot(np.arange(t), np.array(lattice)/10000, label='Lattice', color='black')
plt.plot(np.arange(t), np.array(small)/10000, label='Small-World', color='blue')
plt.plot(np.arange(t), np.array(rand)/10000, label='Random', color='green')
plt.xlabel('Timestep', size=16)
plt.tick_params(labelsize=16)
plt.ylabel('$N_{excited}/N_{total}$', size=16)
plt.legend(prop={'size': 16}, loc='best')
def plot_coverage_variation(N, t, multiplierRegimes=[0, 35, 60]):
small1 = SmallWorldBrain(numberOfNodes=N,
averageDegree=5,
rewireProbability=0.1,
neuronThreshold=1,
refractoryPeriod=1,
numberOfIterations=t,
fractionToFire=0.05,
inhibitoryEdgeMultiplier=multiplierRegimes[0],
fireRate=0.01,
directed=False)
small1.execute(movie=False)
small1Array = [
len(
np.unique(
np.concatenate(small1.get_number_of_excited_neurons()[:i],
axis=0))) / 1000 for i in np.arange(1, t)
]
small2 = SmallWorldBrain(numberOfNodes=N,
averageDegree=5,
rewireProbability=0.1,
neuronThreshold=1,
refractoryPeriod=1,
numberOfIterations=t,
fractionToFire=0.05,
inhibitoryEdgeMultiplier=multiplierRegimes[1],
fireRate=0.01,
directed=False)
small2.execute(movie=False)
small2Array = [
len(
np.unique(
np.concatenate(small2.get_number_of_excited_neurons()[:i],
axis=0))) / 1000 for i in np.arange(1, t)
]
small3 = SmallWorldBrain(numberOfNodes=N,
averageDegree=5,
rewireProbability=0.1,
neuronThreshold=1,
refractoryPeriod=1,
numberOfIterations=t,
fractionToFire=0.05,
inhibitoryEdgeMultiplier=multiplierRegimes[2],
fireRate=0.01,
directed=False)
small3.execute(movie=False)
small3Array = [
len(
np.unique(
np.concatenate(small3.get_number_of_excited_neurons()[:i],
axis=0))) / 1000 for i in np.arange(1, t)
]
plt.figure()
plt.plot(np.arange(1, t),
np.array(small1Array),
label='Supercritical',
color='blue')
plt.plot(np.arange(1, t),
np.array(small2Array),
label='Critical',
color='red')
plt.plot(np.arange(1, t),
np.array(small3Array),
label='Subcritical',
color='black')
#plt.title('Nodes: %d, <k>:%d, Refractory: %d, Threshold: %d' % (small1.numberOfNodes, small1.averageDegree, small1.refractoryPeriod, small1.neuronThreshold))
plt.xlabel('Timestep', size=16)
plt.tick_params(labelsize=16)
plt.ylabel('Coverage', size=16)
plt.xlim([0, t])
plt.legend()
def plot_critical_regimes(N, t, multiplerRegimes=[0, 35, 60]):
small1 = SmallWorldBrain(numberOfNodes=N,
averageDegree=5,
rewireProbability=0.1,
neuronThreshold=1,
refractoryPeriod=1,
numberOfIterations=t,
fractionToFire=0.05,
inhibitoryEdgeMultiplier=multiplerRegimes[0],
fireRate=0.01,
directed=False)
small1.execute(movie=False)
small1Array = []
for _, ye in zip(np.arange(small1.numberOfIterations),
[list(i) for i in small1.numberOfExcitedNeurons]):
small1Array.append(len(ye))
ex, inhib, q1 = small1.get_effective_charge()
#np.save('results/meanQ/subcritical', [ex, inhib, q1])
print('m=%d, <Q>=%.3f:' %
(multiplerRegimes[0], np.mean(np.array(ex) - np.array(inhib))))
small2 = SmallWorldBrain(numberOfNodes=N,
averageDegree=5,
rewireProbability=0.1,
neuronThreshold=1,
refractoryPeriod=1,
numberOfIterations=t,
fractionToFire=0.05,
inhibitoryEdgeMultiplier=multiplerRegimes[1],
fireRate=0.01,
directed=False)
small2.execute(movie=False)
small2Array = []
for _, ye in zip(np.arange(small2.numberOfIterations),
[list(i) for i in small2.numberOfExcitedNeurons]):
small2Array.append(len(ye))
ex2, inhib2, q2 = small2.get_effective_charge()
#np.save('results/meanQ/critical', [ex2, inhib2, q2])
print('m=%d, <Q>=%.3f:' %
(multiplerRegimes[1], np.mean(np.array(ex2) - np.array(inhib2))))
small3 = SmallWorldBrain(numberOfNodes=N,
averageDegree=5,
rewireProbability=0.1,
neuronThreshold=1,
refractoryPeriod=1,
numberOfIterations=t,
fractionToFire=0.05,
inhibitoryEdgeMultiplier=multiplerRegimes[2],
fireRate=0.01,
directed=False)
small3.execute(movie=False)
small3Array = []
for _, ye in zip(np.arange(small3.numberOfIterations),
[list(i) for i in small3.numberOfExcitedNeurons]):
small3Array.append(len(ye))
ex3, inhib3, q3 = small3.get_effective_charge()
#np.save('results/meanQ/supercritical', [ex3, inhib3, q3])
print('m=%d, <Q>=%.3f:' %
(multiplerRegimes[2], np.mean(np.array(ex3) - np.array(inhib3))))
plt.figure()
plt.plot(np.arange(t),
np.array(small1Array) / small1.numberOfNodes,
label='Supercritical',
color='blue')
plt.plot(np.arange(t),
np.array(small2Array) / small1.numberOfNodes,
label='Critical',
color='red')
plt.plot(np.arange(t),
np.array(small3Array) / small1.numberOfNodes,
label='Subcritical',
color='black')
#plt.title('Nodes: %d, <k>:%d, Refractory: %d, Threshold: %d' % (small1.numberOfNodes, small1.averageDegree, small1.refractoryPeriod, small1.neuronThreshold))
plt.xlabel('Timestep', size=16)
plt.tick_params(labelsize=16)
plt.ylabel('$N_{excited}/N_{total}$', size=16)
plt.legend(prop={'size': 16})