def simulator():
conf = Configuration('confpython.rmto')
pools = dict()
pools[0] = MotorUnitPool(conf, 'SOL')
pools[1] = InterneuronPool(conf, 'RC', 'ext')
Syn = SynapsesFactory(conf, pools)
t = np.arange(0.0, conf.simDuration_ms, conf.timeStep_ms)
tic = time.clock()
for i in xrange(0, len(t)):
# Corrent injectada no soma dos MNs
for j in xrange(1, len(pools[0].iInjected), 2):
pools[0].iInjected[j] = 10
pools[0].atualizeMotorUnitPool(t[i]) # MN pool
pools[2].atualizePool(t[i]) # RC synaptic Noise
pools[1].atualizeInterneuronPool(t[i]) # RC pool
toc = time.clock()
print str(toc - tic) + ' seconds'
pools[0].listSpikes()
plt.figure()
plt.plot(pools[0].poolSomaSpikes[:, 0], pools[0].poolSomaSpikes[:, 1] + 1,
'.')
plt.figure()
plt.plot(t, pools[0].Muscle.force, '-')
plt.xlabel('t (ms)')
plt.ylabel('Muscle Force (N)')
def simulator():
conf = Configuration('confMNRC.rmto')
pools = dict()
pools[0] = MotorUnitPool(conf, 'SOL')
pools[1] = InterneuronPool(conf, 'RC', 'ext')
Syn = SynapsesFactory(conf, pools)
t = np.arange(0.0, conf.simDuration_ms, conf.timeStep_ms)
RC_mV = np.zeros_like(t)
MN_mV = np.zeros_like(t)
for i in xrange(0, len(t)):
# t=step*i [ms]
# Current in nA, adjusted to reproduce approximately the experimental 3mV
pools[0].iInjected[1] = 13
pools[0].atualizeMotorUnitPool(t[i]) # MN pool
pools[2].atualizePool(t[i]) # RC synaptic Noise
pools[1].atualizeInterneuronPool(t[i]) # RC pool
RC_mV[i] = pools[1].unit[0].v_mV[0]
MN_mV[i] = pools[0].unit[0].v_mV[1]
fs = 1 / (conf.timeStep_ms * 1e-3)
window = 1000
f, Cxy = signal.coherence(MN_mV, RC_mV, fs, nperseg=window)
plt.figure()
plt.plot(f, Cxy)
plt.xlabel('f (Hz)')
plt.ylabel('Coherence')
plt.xlim((0, 500))
def simulator():
conf = Configuration('confTest.rmto')
#idx = np.where(conf.confArray['f0']=='MUnumber_SOL-S')[0][0]
#conf.confArray['f1'][idx] = nMN
pools = dict()
pools[0] = MotorUnitPool(conf, 'SOL')
pools[1] = NeuralTract(conf, 'CMExt')
#pools[2] = InterneuronPool(conf, 'RC', 'ext')
#ankle = jointAnkleForceTask(conf, pools)
Syn = SynapsesFactory(conf, pools)
del Syn
#conf.simDuration_ms = duration
t = np.arange(0.0, conf.simDuration_ms, conf.timeStep_ms)
tic = time.time()
for i in xrange(0, len(t)):
pools[1].atualizePool(t[i])
pools[0].atualizeMotorUnitPool(t[i])
#pools[3].atualizePool(t[i]) # RC synaptic Noise
#pools[2].atualizeInterneuronPool(t[i]) # RC pool
toc = time.time()
print str(toc - tic) + ' seconds'
pools[0].listSpikes()
pools[1].listSpikes()
'''
def simulator1():
conf = Configuration('confGH.rmto')
pools = dict()
pools[0] = MotorUnitPool(conf, 'SOL')
pools[1] = InterneuronPool(conf, 'RC', 'ext')
pools[2] = NeuralTract(conf, 'CMExt')
Syn = SynapsesFactory(conf, pools)
t = np.arange(0.0, conf.simDuration_ms, conf.timeStep_ms)
RC_mV = np.zeros_like(t)
MN_mV = np.zeros_like(t)
for i in xrange(0, len(t)):
pools[2].atualizePool(t[i]) # Neural tract
pools[0].atualizeMotorUnitPool(t[i]) # MN pool
pools[3].atualizePool(t[i]) # RC synaptic Noise
pools[1].atualizeInterneuronPool(t[i]) # RC pool
RC_mV[i] = pools[1].unit[0].v_mV[0]
MN_mV[i] = pools[0].unit[0].v_mV[1]
pools[0].listSpikes()
pools[1].listSpikes()
pools[2].listSpikes()
# Calculate feedback gain
## Calculate number of spikes
numRecruitedUnits = int(max(pools[0].poolSomaSpikes[:, 1]))
numberSpikes = [None] * numRecruitedUnits
firingRate = [None] * numRecruitedUnits
for i in xrange(numRecruitedUnits):
# Count number of spikes for each recruited unit
numberSpikes[i] = pools[0].poolSomaSpikes[:, 1].tolist().count(i)
firingRate[i] = numberSpikes[i] / conf.simDuration_ms
## Calcultate average MN activity
activity = np.mean(firingRate) * numRecruitedUnits
plt.figure()
plt.plot(pools[0].poolSomaSpikes[:, 0], pools[0].poolSomaSpikes[:, 1] + 1,
'.')
plt.figure()
plt.plot(t, MN_mV)
print '{} recruited units, with average activity of {}'.format(
numRecruitedUnits, activity)
def initialize():
conf = Configuration('confTest.rmto')
pools = dict()
pools[0] = MotorUnitPool(conf, 'SOL')
pools[1] = NeuralTract(conf, 'CMExt')
pools[2] = InterneuronPool(conf, 'RC', 'ext')
for i in xrange(0, len(pools[0].unit)):
pools[0].unit[i].createStimulus()
Syn = SynapsesFactory(conf, pools)
t = np.arange(0.0, conf.simDuration_ms, conf.timeStep_ms)
return pools, t
def simulator():
conf = Configuration('confnlinIO.rmto')
pools = dict()
pools[0] = MotorUnitPool(conf, 'SOL')
pools[1] = InterneuronPool(conf, 'RC', 'ext')
for i in xrange(0, len(pools[0].unit)):
pools[0].unit[i].createStimulus()
Syn = SynapsesFactory(conf, pools)
t = np.arange(0.0, conf.simDuration_ms, conf.timeStep_ms)
RC_mV = np.zeros_like(t)
MN_mV = np.zeros_like(t)
tic = time.clock()
for i in xrange(0, len(t)):
pools[0].atualizeMotorUnitPool(t[i]) # MN pool
pools[2].atualizePool(t[i]) # RC synaptic Noise
pools[1].atualizeInterneuronPool(t[i]) # RC pool
RC_mV[i] = pools[1].v_mV[0]
MN_mV[i] = pools[0].v_mV[1]
toc = time.clock()
print str(toc - tic) + ' seconds'
pools[0].listSpikes()
plt.figure()
plt.plot(t, pools[0].unit[0].nerveStimulus_mA)
plt.figure()
plt.plot(t, MN_mV, '-')
#plt.figure()
#plt.plot(pools[0].poolSomaSpikes[:, 0],
# pools[0].poolSomaSpikes[:, 1]+1, '.')
plt.figure()
plt.plot(t, RC_mV, '-')
def simulator():
conf = Configuration('confTest.rmto')
pools = dict()
pools[0] = MotorUnitPool(conf, 'SOL')
pools[1] = NeuralTract(conf, 'CMExt')
#pools.append(InterneuronPool(conf, 'RC'))
#ankle = jointAnkleForceTask(conf, pools)
Syn = SynapsesFactory(conf, pools)
del Syn
t = np.arange(0.0, conf.simDuration_ms, conf.timeStep_ms)
dendV = np.zeros_like(t)
somaV = np.zeros_like(t)
internodeV = np.zeros_like(t)
nodeV = np.zeros_like(t)
FR_neuralTract = 80
GammaOrder_neuralTract = 10
tic = time.time()
for i in xrange(0, len(t)):
#for j in xrange(len(pools[0].unit)):
# pools[0].unit[j].iInjected[1] = 10
pools[1].atualizePool(t[i], FR_neuralTract, GammaOrder_neuralTract)
pools[0].atualizeMotorUnitPool(t[i])
dendV[i] = pools[0].unit[2].v_mV[0]
somaV[i] = pools[0].unit[2].v_mV[1]
toc = time.time()
print str(toc - tic) + ' seconds'
pools[0].listSpikes()
pools[1].listSpikes()
'''