def test_cls_func_hh1_2():
class HH(bp.NeuGroup):
def __init__(self,
size,
ENa=50.,
EK=-77.,
EL=-54.387,
C=1.0,
gNa=120.,
gK=36.,
gL=0.03,
V_th=20.,
**kwargs):
super(HH, self).__init__(size=size, **kwargs)
# parameters
self.ENa = ENa
self.EK = EK
self.EL = EL
self.C = C
self.gNa = gNa
self.gK = gK
self.gL = gL
self.V_th = V_th
# variable
self.input = bp.math.numpy.Variable(np.zeros(size))
# integral
self.integral = bp.odeint(method='rk4', f=self.derivaitve)
def derivaitve(self, V, m, h, n, t):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
dmdt = alpha * (1 - m) - beta * m
alpha = 0.07 * np.exp(-(V + 65) / 20.)
beta = 1 / (1 + np.exp(-(V + 35) / 10))
dhdt = alpha * (1 - h) - beta * h
alpha = 0.01 * (V + 55) / (1 - np.exp(-(V + 55) / 10))
beta = 0.125 * np.exp(-(V + 65) / 80)
dndt = alpha * (1 - n) - beta * n
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, dmdt, dhdt, dndt
hh = HH(10)
r = _jit_cls_func(hh.derivaitve, show_code=True)
print(r)
def test_cls_func_hh1():
class HH(bp.NeuGroup):
target_backend = 'numpy'
def __init__(self,
size,
ENa=50.,
EK=-77.,
EL=-54.387,
C=1.0,
gNa=120.,
gK=36.,
gL=0.03,
V_th=20.,
**kwargs):
super(HH, self).__init__(size=size, **kwargs)
# parameters
self.ENa = ENa
self.EK = EK
self.EL = EL
self.C = C
self.gNa = gNa
self.gK = gK
self.gL = gL
self.V_th = V_th
# variables
self.V = bp.math.numpy.Variable(np.ones(size) * -65.)
self.m = bp.math.numpy.Variable(np.ones(size) * 0.5)
self.h = bp.math.numpy.Variable(np.ones(size) * 0.6)
self.n = bp.math.numpy.Variable(np.ones(size) * 0.32)
self.spike = bp.math.numpy.Variable(np.zeros(size, dtype=bool))
self.input = bp.math.numpy.Variable(np.zeros(size))
# integral
self.integral = bp.odeint(method='rk4', f=self.derivaitve)
def derivaitve(self, V, m, h, n, t, Iext):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
dmdt = alpha * (1 - m) - beta * m
alpha = 0.07 * np.exp(-(V + 65) / 20.)
beta = 1 / (1 + np.exp(-(V + 35) / 10))
dhdt = alpha * (1 - h) - beta * h
alpha = 0.01 * (V + 55) / (1 - np.exp(-(V + 55) / 10))
beta = 0.125 * np.exp(-(V + 65) / 80)
dndt = alpha * (1 - n) - beta * n
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 + Iext) / self.C
return dVdt, dmdt, dhdt, dndt
def update(self, _t, _i):
V, m, h, n = self.integral(self.V, self.m, self.h, self.n, _t,
self.input)
self.spike[:] = (self.V < self.V_th) * (V >= self.V_th)
self.V[:] = V
self.m[:] = m
self.h[:] = h
self.n[:] = n
self.input[:] = 0.
hh = HH(10)
r = _jit_cls_func(hh.update, show_code=True)
pprint(r['func'])
pprint('arguments:')
pprint(r['arguments'])
pprint('arg2call:')
pprint(r['arg2call'])
pprint('nodes:')
pprint(r['nodes'])
def test_container1():
class HH(bp.NeuGroup):
def __init__(self,
size,
ENa=50.,
EK=-77.,
EL=-54.387,
C=1.0,
gNa=120.,
gK=36.,
gL=0.03,
V_th=20.,
**kwargs):
# parameters
self.ENa = ENa
self.EK = EK
self.EL = EL
self.C = C
self.gNa = bp.math.numpy.Variable(gNa)
self.gK = gK
self.gL = gL
self.V_th = bp.math.numpy.Variable(V_th)
# variables
self.V = bp.math.numpy.Variable(np.ones(size) * -65.)
self.m = bp.math.numpy.Variable(np.ones(size) * 0.5)
self.h = bp.math.numpy.Variable(np.ones(size) * 0.6)
self.n = bp.math.numpy.Variable(np.ones(size) * 0.32)
self.spike = bp.math.numpy.Variable(np.zeros(size, dtype=bool))
self.input = bp.math.numpy.Variable(np.zeros(size))
super(HH, self).__init__(size=size, **kwargs)
@bp.odeint(method='rk4')
def integral(self, V, m, h, n, t, Iext):
alpha = 0.1 * (V + 40) / (1 - np.exp(-(V + 40) / 10))
beta = 4.0 * np.exp(-(V + 65) / 18)
dmdt = alpha * (1 - m) - beta * m
alpha = 0.07 * np.exp(-(V + 65) / 20.)
beta = 1 / (1 + np.exp(-(V + 35) / 10))
dhdt = alpha * (1 - h) - beta * h
alpha = 0.01 * (V + 55) / (1 - np.exp(-(V + 55) / 10))
beta = 0.125 * np.exp(-(V + 65) / 80)
dndt = alpha * (1 - n) - beta * n
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 + Iext) / self.C
return dVdt, dmdt, dhdt, dndt
def update(self, _t, _i):
V, m, h, n = self.integral(self.V, self.m, self.h, self.n, _t,
self.input)
self.spike[:] = (self.V < self.V_th) * (V >= self.V_th)
self.V[:] = V
self.m[:] = m
self.h[:] = h
self.n[:] = n
self.input[:] = 0.
class AMPA_vec(bp.TwoEndConn):
def __init__(self,
pre,
post,
conn,
delay=0.,
g_max=0.10,
E=0.,
tau=2.0,
**kwargs):
super(AMPA_vec, self).__init__(pre=pre, post=post, **kwargs)
# parameters
self.g_max = g_max
self.E = E
self.tau = tau
self.delay = delay
# connections
self.conn = conn(pre.size, post.size)
self.pre_ids, self.post_ids = conn.requires('pre_ids', 'post_ids')
self.size = len(self.pre_ids)
# data
self.s = bp.math.numpy.Variable(np.zeros(self.size))
self.g = self.register_constant_delay('g',
size=self.size,
delay=delay)
@bp.odeint
def int_s(self, s, t):
return -s / self.tau
def update(self, _t, _i):
for i in range(self.size):
pre_id = self.pre_ids[i]
self.s[i] = self.int_s(self.s[i], _t)
self.s[i] += self.pre.spike[pre_id]
self.g.push(i, self.g_max * self.s[i])
post_id = self.post_ids[i]
self.post.input[post_id] -= self.g.pull(i) * (
self.post.V[post_id] - self.E)
hh = HH(10)
ampa = AMPA_vec(pre=hh, post=hh, conn=bp.connect.All2All(), delay=10.)
net = bp.Network(hh, ampa)
r = _jit_cls_func(net.update, show_code=True)
pprint(r['func'])
pprint('arguments:')
pprint(r['arguments'])
pprint('arg2call:')
pprint(r['arg2call'])
pprint('nodes:')
pprint(r['nodes'])