madara-1112
commented
3 weeks ago And I may also encountered another bug. I tried to comulate the firing rate of HH neuron using the following code. When I set the strength of input to be 0.280 and duration to be 1000, the figure was obviously incorrectly.
And when I set the strength to 0.278 or 0.280, they both worked successfully as follow.
`import brainpy as bp import brainpy.math as bm import numpy as np import matplotlib.pyplot as plt
class HH(bp.dyn.NeuGroup):
def __init__(self,size,ENa=50.,gNa=1.2,EK=-77.
,gK=0.36,EL=-54.387,gL=0.003,V_th=0.,C=0.01):
super(HH,self).__init__(size=size)
self.ENa=ENa
self.EK=EK
self.EL=EL
self.gNa=gNa
self.gK=gK
self.gL=gL
self.C=C
self.V_th=V_th
self.V = bm.Variable(-65 * bm.ones(self.num))
self.m = bm.Variable(0.0529 * bm.ones(self.num))
self.h = bm.Variable(0.5961 * bm.ones(self.num))
self.n = bm.Variable(0.3177 * bm.ones(self.num))
self.gNa_=bm.Variable(0 * bm.ones(self.num))
self.gK_ = bm.Variable(0 * bm.ones(self.num))
self.input = bm.Variable(bm.zeros(self.num))
self.spike = bm.Variable(bm.zeros(self.num,dtype=bool))
self.t_last_spike=bm.Variable(bm.ones(self.num)*-1e7)
self.intergral = bp.odeint(f=self.derivative,method='exp_auto')
@property
def derivative(self):
return bp.JointEq(self.dV, self.dm, self.dh, self.dn)
def dm(self, m, t, V):
alpha = 0.1 * (V + 40) / (1 - bm.exp(-(V + 40) / 10))
beta = 4.0 * bm.exp(-(V + 65) / 18)
dmdt = alpha * (1 - m) - beta * m
return dmdt
def dh(self, h, t, V):
alpha = 0.07 * bm.exp(-(V + 65) / 20.)
beta = 1 / (1 + bm.exp(-(V + 35) / 10))
dhdt = alpha * (1 - h) - beta * h
return dhdt
def dn(self, n, t, V):
alpha = 0.01 * (V + 55) / (1 - bm.exp(-(V + 55) / 10))
beta = 0.125 * bm.exp(-(V + 65) / 80)
dndt = alpha * (1 - n) - beta * n
return dndt
def dV(self, V, t, h, n, m):
I_Na = (self.gNa * m ** 3.0 * h) * (V - self.ENa)
I_K = (self.gK * n ** 4.0) * (V - self.EK)
I_leak = self.gL * (V - self.EL)
dVdt = (- I_Na - I_K - I_leak + self.input) / self.C
return dVdt
def update(self, tdi):
t, dt = tdi.t, tdi.dt
V, m, h, n = self.intergral(self.V, self.m, self.h, self.n, t, dt=dt)
self.spike.value = bm.logical_and(self.V < self.V_th, V >= self.V_th)
self.t_last_spike.value = bm.where(self.spike, t, self.t_last_spike)
self.V.value = V
self.m.value = m
self.h.value = h
self.n.value = n
self.gNa_.value=self.gNa * m ** 3.0 * h #记录钠电导变化
self.gK_.value=self.gK * n ** 4.0 #记录钾电导变化
self.input[:] = 0. # 重置神经元接收到的输入currents, length = bp.inputs.section_input(values=[0.280], durations=[1000],returnlength=True) hh = HH(1) runner = bp.dyn.DSRunner(hh,monitors=['V', 'm', 'h', 'n','gNa','gK_'], inputs=['input', currents, 'iter']) runner.run(length) fig,axe=plt.subplots(2,1) axe[0].plot(runner.mon.ts, runner.mon.V,linewidth=2) print(runner.mon.V.shape) axe[0].set_ylabel('V(mV)')
axe[1].plot(runner.mon.ts, runner.mon.gNa_,linewidth=2,color='blue')
axe[1].plot(runner.mon.ts, runner.mon.gK_,linewidth=2,color='red')
axe[1].set_ylabel('Conductance')
axe[1].plot(runner.mon.ts, runner.mon.m,linewidth=2,color='blue',label='m') axe[1].plot(runner.mon.ts, runner.mon.n,linewidth=2,color='red',label='n') axe[1].plot(runner.mon.ts, runner.mon.h,linewidth=2,color='orange',label='h') axe[1].set_ylabel('Channel') plt.legend() plt.tight_layout() plt.show()`
Routhleck
ztqakita
Hello, I have a question about the function pre2post_event_sum. The documentation says "The pre-to-post event-driven synaptic summation with CSR synapse structure. When values is a scalar, this function is equivalent to post_val = np.zeros(post_num) post_ids, idnptr = pre2post for i in range(pre_num): if events[i]: for j in range(idnptr[i], idnptr[i+1]): post_val[post_ids[i]] += values" But I wonder if “post_val[post_ids[i]] += values” was written incorrectly and should be changed to "post_val[post_ids[j]] += values".