def __init__(self, params=zheng_params, network=None):
eqs=Equations('''
G_total : siemens
G_total_exc : siemens
cmr_o : 1
cb : 1
g : 1
c_ab : 1
cb_0 : 1
g_0 : 1
''')
NeuronGroup.__init__(self, 1, model=eqs, compile=True, freeze=True)
self.params=params
self.c_ab=self.params.c_ab
self.cb_0=self.params.cb_0
self.g_0=self.params.g_0
self.cb=self.cb_0
self.g=self.g_0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, params=zheng_params, network=None):
eqs = Equations('''
G_total : siemens
G_total_exc : siemens
cmr_o : 1
cb : 1
g : 1
c_ab : 1
cb_0 : 1
g_0 : 1
''')
NeuronGroup.__init__(self, 1, model=eqs, compile=True, freeze=True)
self.params = params
self.c_ab = self.params.c_ab
self.cb_0 = self.params.cb_0
self.g_0 = self.params.g_0
self.cb = self.cb_0
self.g = self.g_0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, clock, params=default_params, network=None):
eqs = Equations('''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
o_e=1-(1-e_base)**(1/f_in) : 1
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_base*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
e_base : 1
v_base : 1
k1 : 1
k2 : 1
k3 : 1
''')
NeuronGroup.__init__(self,
1,
model=eqs,
clock=clock,
compile=True,
freeze=True)
self.params = params
self.G_base = params.G_base
self.eta = params.eta
self.tau_s = params.tau_s
self.tau_f = params.tau_f
self.alpha = params.alpha
self.tau_o = params.tau_o
self.e_base = params.e_base
self.v_base = params.v_base
self.k1 = params.k1
self.params.s_e = params.s_e_0 * exp(-params.TE / params.T_2E)
self.params.s_i = params.s_i_0 * exp(-params.TE / params.T_2I)
self.params.beta = self.params.s_e / self.params.s_i
self.k2 = self.params.beta * params.r_0 * self.e_base * params.TE
self.k3 = self.params.beta - 1
self.f_in = 1
self.s = 0
self.v = 1
self.q = 1
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, clock, params=default_params, network=None):
eqs=Equations('''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
o_e=1-(1-e_base)**(1/f_in) : 1
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_base*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
e_base : 1
v_base : 1
k1 : 1
k2 : 1
k3 : 1
''')
NeuronGroup.__init__(self, 1, model=eqs, clock=clock, compile=True, freeze=True)
self.params=params
self.G_base=params.G_base
self.eta=params.eta
self.tau_s=params.tau_s
self.tau_f=params.tau_f
self.alpha=params.alpha
self.tau_o=params.tau_o
self.e_base=params.e_base
self.v_base=params.v_base
self.k1=params.k1
self.params.s_e=params.s_e_0*exp(-params.TE/params.T_2E)
self.params.s_i=params.s_i_0*exp(-params.TE/params.T_2I)
self.params.beta=self.params.s_e/self.params.s_i
self.k2=self.params.beta*params.r_0*self.e_base*params.TE
self.k3=self.params.beta-1
self.f_in=1
self.s=0
self.v=1
self.q=1
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, pyramidal_group, clock=defaultclock):
eqs = Equations('''
LFP : amp
''')
NeuronGroup.__init__(self,
1,
model=eqs,
compile=True,
freeze=True,
clock=clock)
self.LFP = linked_var(pyramidal_group, 'I_abs', func=sum)
def __init__(self, pyramidal_group, clock=defaultclock):
eqs=Equations('''
LFP : amp
''')
NeuronGroup.__init__(self, 1, model=eqs, compile=True, freeze=True, clock=clock)
self.LFP=linked_var(pyramidal_group, 'I_abs', func=sum)
def __init__(self, clock, params=zheng_params, network=None):
eqs=Equations('''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1.0
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
do_e/dt=1.0/(phi/f_in)*(-o_e+(1.0-g)*(1.0-(1.0-e_base/(1.0-g_0))**(1.0/f_in))) : %.4f
dcb/dt=1.0/(phi/f_in)*(-cb-(c_ab*o_e)/oe_log+c_ab*g) : 1
oe_log : 1
cmr_o=(cb-g*c_ab)/(cb_0-g_0*c_ab) : 1
dg/dt=1.0/(j*v_ratio*((r*transitTime)/e_base))*((cmr_o-1.0)-k*s) : %.4f
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_0*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
v_0 : 1
k1 : 1
k2 : 1
k3 : 1
phi : %.4f*second
e_base : %.4f
g_0 : %.4f
c_ab : 1
cb_0 : 1
v_ratio : 1
j : 1
transitTime : second
k : 1
r : 1
''' % (params.e_base, params.g_0, params.phi, params.e_base, params.g_0))
NeuronGroup.__init__(self, 1, model=eqs, clock=clock, compile=True, freeze=True)
self.params=params
self.G_base=params.G_base
self.eta=params.eta
self.tau_s=params.tau_s
self.tau_f=params.tau_f
self.alpha=params.alpha
self.tau_o=params.tau_o
self.e_base=params.e_base
self.v_0=params.v_0
self.k1=params.k1
self.params.s_e=params.s_e_0*exp(-params.TE/params.T_2E)
self.params.s_i=params.s_i_0*exp(-params.TE/params.T_2I)
self.params.beta=self.params.s_e/self.params.s_i
self.k2=self.params.beta*params.r_0*self.e_base*params.TE
self.k3=self.params.beta-1.0
self.c_ab=self.params.c_ab
self.cb_0=self.params.cb_0
self.g_0=self.params.g_0
self.phi=self.params.phi
self.v_ratio=self.params.v_ratio
self.j=self.params.j
self.transitTime=self.params.transitTime
self.k=self.params.k
self.r=self.params.r
self.f_in=1.0
self.s=0.0
self.v=1.0
self.o_e=self.e_base
self.cb=self.cb_0
self.g=self.g_0
self.oe_log=np.log(1.0-self.o_e/(1.0-self.g))
self.q=1.0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, clock, params=zheng_params, network=None):
eqs = Equations(
'''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1.0
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
do_e/dt=1.0/(phi/f_in)*(-o_e+(1.0-g)*(1.0-(1.0-e_base/(1.0-g_0))**(1.0/f_in))) : %.4f
dcb/dt=1.0/(phi/f_in)*(-cb-(c_ab*o_e)/oe_log+c_ab*g) : 1
oe_log : 1
cmr_o=(cb-g*c_ab)/(cb_0-g_0*c_ab) : 1
dg/dt=1.0/(j*v_ratio*((r*transitTime)/e_base))*((cmr_o-1.0)-k*s) : %.4f
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_0*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
v_0 : 1
k1 : 1
k2 : 1
k3 : 1
phi : %.4f*second
e_base : %.4f
g_0 : %.4f
c_ab : 1
cb_0 : 1
v_ratio : 1
j : 1
transitTime : second
k : 1
r : 1
''' %
(params.e_base, params.g_0, params.phi, params.e_base, params.g_0))
NeuronGroup.__init__(self,
1,
model=eqs,
clock=clock,
compile=True,
freeze=True)
self.params = params
self.G_base = params.G_base
self.eta = params.eta
self.tau_s = params.tau_s
self.tau_f = params.tau_f
self.alpha = params.alpha
self.tau_o = params.tau_o
self.e_base = params.e_base
self.v_0 = params.v_0
self.k1 = params.k1
self.params.s_e = params.s_e_0 * exp(-params.TE / params.T_2E)
self.params.s_i = params.s_i_0 * exp(-params.TE / params.T_2I)
self.params.beta = self.params.s_e / self.params.s_i
self.k2 = self.params.beta * params.r_0 * self.e_base * params.TE
self.k3 = self.params.beta - 1.0
self.c_ab = self.params.c_ab
self.cb_0 = self.params.cb_0
self.g_0 = self.params.g_0
self.phi = self.params.phi
self.v_ratio = self.params.v_ratio
self.j = self.params.j
self.transitTime = self.params.transitTime
self.k = self.params.k
self.r = self.params.r
self.f_in = 1.0
self.s = 0.0
self.v = 1.0
self.o_e = self.e_base
self.cb = self.cb_0
self.g = self.g_0
self.oe_log = np.log(1.0 - self.o_e / (1.0 - self.g))
self.q = 1.0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
