def __init__(self, K):
self.K = K
self.f = (1.5 / K) * np.ones(self.K)
self.g = (1.5 / K) * np.ones(self.K)
self.connectivity = np.outer(self.f, self.g)
self.myneuron = morrislecar()
self.thestim = lambda x: 0.
def __init__(self,K): self.K=K self.f=(1.5/K)*np.ones(self.K) self.g=(1.5/K)*np.ones(self.K) self.connectivity=np.outer(self.f,self.g) self.myneuron=morrislecar() self.thestim=lambda x: 0.
power spectra is calculated and extracted the maximum. This is
repeated in the loop to create a complete power spectra'''
#stimulation function
def mysin(x):
if x > tstart:
return amp * np.sin(2 * np.pi * freq * (x - tstart)**2 /
(2 * period)) + I0
else:
return I0
#intializing the model
#morris lecar
myneuron = morrislecar()
myneuron.thestim(mysin)
x0 = np.array([-1., myneuron.ninf(-1.)])
#initializing the simulation parameters
dt = 1e-2
fs = 1 / dt
amp = 0.005 #ampplitude of stimulation
tstart = 100. #begining of stimulation
nstart = tstart / dt
I0 = 0. #dc
period = 3000
freq = 0.4
delta_freq = 0.01
N = nstart + period / dt
after stimulate for a certain number of cycles of a single mode the
power spectra is calculated and extracted the maximum. This is
repeated in the loop to create a complete power spectra'''
#stimulation function
def mysin(x):
if x>tstart:
return amp*np.sin(2*np.pi*freq*(x-tstart)**2/(2*period))+I0
else:
return I0
#intializing the model
#morris lecar
myneuron=morrislecar()
myneuron.thestim(mysin)
x0=np.array([-1.,myneuron.ninf(-1.)])
#initializing the simulation parameters
dt=1e-2
fs=1/dt
amp=0.005 #ampplitude of stimulation
tstart=100. #begining of stimulation
nstart=tstart/dt
I0=0. #dc
period=3000
freq=0.4
delta_freq=0.01
