from tvb.simulator.models.base import Model, ModelNumbaDfun
import numpy
from numpy import *
from numba import guvectorize, float64
from tvb.basic.neotraits.api import NArray, Final, List, Range
[docs]
class RwongwangT(ModelNumbaDfun):
a_E = NArray(
label=":math:`a_E`",
default=numpy.array([310.0]),
domain=Range(lo=0., hi=500., step=1.),
doc=""""""
)
b_E = NArray(
label=":math:`b_E`",
default=numpy.array([125.0]),
domain=Range(lo=0., hi=200., step=1.),
doc=""""""
)
d_E = NArray(
label=":math:`d_E`",
default=numpy.array([0.160]),
domain=Range(lo=0.0, hi=0.2, step=0.001),
doc=""""""
)
a_I = NArray(
label=":math:`a_I`",
default=numpy.array([615.0]),
domain=Range(lo=0., hi=1000., step=1.),
doc=""""""
)
b_I = NArray(
label=":math:`b_I`",
default=numpy.array([177.0]),
domain=Range(lo=0.0, hi=200.0, step=1.0),
doc=""""""
)
d_I = NArray(
label=":math:`d_I`",
default=numpy.array([0.087]),
domain=Range(lo=0.01, hi=0.2, step=0.001),
doc=""""""
)
gamma_E = NArray(
label=":math:`gamma_E`",
default=numpy.array([0.641 / 1000.0]),
domain=Range(lo=0.0, hi=0.001, step=0.00001),
doc=""""""
)
tau_E = NArray(
label=":math:`tau_E`",
default=numpy.array([100.0]),
domain=Range(lo=50., hi=150., step=1.),
doc=""""""
)
w_plus = NArray(
label=":math:`w_plus`",
default=numpy.array([1.4]),
domain=Range(lo=0.0, hi=2.0, step=0.01),
doc=""""""
)
w_E = NArray(
label=":math:`w_E`",
default=numpy.array([1.0]),
domain=Range(lo=0.0, hi=2.0, step=0.01),
doc=""""""
)
gamma_I = NArray(
label=":math:`gamma_I`",
default=numpy.array([1.0 / 1000.0]),
domain=Range(lo=0.0, hi=0.002, step=0.0001),
doc=""""""
)
tau_I = NArray(
label=":math:`tau_I`",
default=numpy.array([10.0]),
domain=Range(lo=5., hi=150., step=1.0),
doc=""""""
)
w_I = NArray(
label=":math:`w_I`",
default=numpy.array([0.7]),
domain=Range(lo=0.0, hi=1.0, step=0.01),
doc=""""""
)
I_0 = NArray(
label=":math:`I_0`",
default=numpy.array([0.382]),
domain=Range(lo=0.0, hi=1.0, step=0.001),
doc=""""""
)
J_N = NArray(
label=":math:`J_N`",
default=numpy.array([0.15]),
doc=""""""
)
J_I = NArray(
label=":math:`J_I`",
default=numpy.array([1.0]),
doc=""""""
)
G = NArray(
label=":math:`G`",
default=numpy.array([2.0]),
doc=""""""
)
lamda = NArray(
label=":math:`lamda`",
default=numpy.array([0.0]),
doc=""""""
)
J_NMDA = NArray(
label=":math:`J_NMDA`",
default=numpy.array([0.15]),
doc=""""""
)
JI = NArray(
label=":math:`JI`",
default=numpy.array([1.0]),
doc=""""""
)
state_variable_range = Final(
label="State Variable ranges [lo, hi]",
default={"V": numpy.array([0.0, 1.0]),
"W": numpy.array([0.0, 1.0])},
doc="""state variables"""
)
state_variable_boundaries = Final(
label="State Variable boundaries [lo, hi]",
default={"V": numpy.array([0.0, 1.0]),
"W": numpy.array([0.0, 1.0])},
)
variables_of_interest = List(
of=str,
label="Variables or quantities available to Monitors",
choices=('V', 'W', ),
default=('V', 'W', ),
doc="Variables to monitor"
)
state_variables = ['V', 'W']
_nvar = 2
cvar = numpy.array([0,1,], dtype = numpy.int32)
[docs]
def dfun(self, vw, c, local_coupling=0.0):
vw_ = vw.reshape(vw.shape[:-1]).T
c_ = c.reshape(c.shape[:-1]).T
deriv = _numba_dfun_RwongwangT(vw_, c_, self.a_E, self.b_E, self.d_E, self.a_I, self.b_I, self.d_I, self.gamma_E, self.tau_E, self.w_plus, self.w_E, self.gamma_I, self.tau_I, self.w_I, self.I_0, self.J_N, self.J_I, self.G, self.lamda, self.J_NMDA, self.JI, local_coupling)
return deriv.T[..., numpy.newaxis]
@guvectorize([(float64[:], float64[:], float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64, float64[:])], '(n),(m)' + ',()'*21 + '->(n)', nopython=True)
def _numba_dfun_RwongwangT(vw, coupling, a_E, b_E, d_E, a_I, b_I, d_I, gamma_E, tau_E, w_plus, w_E, gamma_I, tau_I, w_I, I_0, J_N, J_I, G, lamda, J_NMDA, JI, local_coupling, dx):
"Gufunc for RwongwangT model equations."
# long-range coupling
c_pop0 = coupling[0]
c_pop1 = coupling[1]
V = vw[0]
W = vw[1]
# derived variables
tmp_I_E = a_E * ((w_E * I_0) + (w_plus * J_NMDA) * V + c_pop0 - JI*W) - b_E
tmp_H_E = tmp_I_E/(1.0-exp((-1.0 * d_E) * tmp_I_E))
tmp_I_I = (a_I*(((w_I * I_0)+(J_NMDA * V))-W))-b_I
tmp_H_I = tmp_I_I/(1.0-exp((-1.0 * d_I)*tmp_I_I))
dx[0] = ((-1.0 / tau_E)* V)+(tmp_H_E*(1-V)*gamma_E)
dx[1] = ((-1.0 / tau_I)* W)+(tmp_H_I*gamma_I)
