def __init__(self, tau=10.0, B=0.0, T=0.0, sum='sum(exc) - sum(inh)', noise=None):
# Create the arguments
parameters = """
tau = %(tau)s : population
B = %(B)s
T = %(T)s : population
""" % {'tau': tau, 'B': B, 'T': T}
# Equations for the variables
if not noise:
noise_def = ''
else:
noise_def = '+ ' + noise
equations="""
tau * dv/dt + v = %(sum)s + B %(noise)s : exponential
r = pos(v - T)
""" % { 'sum' : sum, 'noise': noise_def}
Neuron.__init__(self, parameters=parameters, equations=equations,
name="Leaky-Integrator", description="Leaky-Integrator with positive transfer function and additive noise.")
# For reporting
self._instantiated.append(True)
def __init__(self, v_rest=-65.0, cm=1.0, tau_m=20.0, tau_refrac=0.0, tau_syn_E=5.0, tau_syn_I=5.0, v_thresh=-50.0, v_reset=-65.0, i_offset=0.0):
# Create the arguments
parameters = """
v_rest = %(v_rest)s
cm = %(cm)s
tau_m = %(tau_m)s
tau_refrac = %(tau_refrac)s
tau_syn_E = %(tau_syn_E)s
tau_syn_I = %(tau_syn_I)s
v_thresh = %(v_thresh)s
v_reset = %(v_reset)s
i_offset = %(i_offset)s
""" % {'v_rest':v_rest, 'cm':cm, 'tau_m':tau_m, 'tau_refrac':tau_refrac, 'tau_syn_E':tau_syn_E, 'tau_syn_I':tau_syn_I, 'v_thresh':v_thresh, 'v_reset':v_reset, 'i_offset':i_offset}
# Equations for the variables
equations="""
gmax_exc = exp((tau_syn_E - dt/2.0)/tau_syn_E)
gmax_inh = exp((tau_syn_I - dt/2.0)/tau_syn_I)
cm * dv/dt = cm/tau_m*(v_rest -v) + alpha_exc - alpha_inh + i_offset : exponential, init=%(v_reset)s
tau_syn_E * dg_exc/dt = - g_exc : exponential
tau_syn_I * dg_inh/dt = - g_inh : exponential
tau_syn_E * dalpha_exc/dt = gmax_exc * g_exc - alpha_exc : exponential
tau_syn_I * dalpha_inh/dt = gmax_inh * g_inh - alpha_inh : exponential
""" % {'v_reset':v_reset}
spike = """
v > v_thresh
"""
reset = """
v = v_reset
"""
Neuron.__init__(self, parameters=parameters, equations=equations, spike=spike, reset=reset, refractory='tau_refrac',
name="Integrate-and-Fire", description="Leaky integrate-and-fire model with fixed threshold and alpha post-synaptic currents.")
# For reporting
self._instantiated.append(True)
def __init__(self,
parameters,
equations,
inputs,
output=["BOLD"],
name="Custom BOLD model",
description=""):
"""
See ANNarchy.extensions.bold.PredefinedModels.py for some example models.
:param parameters: parameters of the model and their initial value.
:param equations: equations defining the temporal evolution of variables.
:param inputs: single variable or list of input signals (e.g. 'I_CBF' or ['I_CBF', 'I_CMRO2']).
:param output: output variable of the model (default is 'BOLD').
:param name: optional model name.
:param description: optional model description.
"""
# The processing in BoldMonitor expects lists, but the interface
# should allow also single strings (if only one variable is considered)
self._inputs = [inputs] if isinstance(inputs, str) else inputs
self._output = [output] if isinstance(output, str) else output
Neuron.__init__(self,
parameters=parameters,
equations=equations,
name=name,
description=description)
self._model_instantiated = False # activated by BoldMonitor
def __init__(self, a=0.2, b=0.2, c=-65.0, d=2.0, v_thresh=30.0, i_offset=0.0, noise=0.0, tau_refrac=0.0, conductance="g_exc - g_inh"):
# Extract which targets are defined in the conductance
import re
targets = re.findall(r'g_([\w]+)', conductance)
# Create the arguments
parameters = """
noise = %(noise)s
a = %(a)s
b = %(b)s
c = %(c)s
d = %(d)s
v_thresh = %(v_thresh)s
i_offset = %(i_offset)s
tau_refrac = %(tau_refrac)s
""" % {'a': a, 'b':b, 'c':c, 'd':d, 'v_thresh':v_thresh, 'i_offset':i_offset, 'noise':noise, 'tau_refrac':tau_refrac}
# Equations for the variables
equations="""
I = %(conductance)s + noise * Normal(0.0, 1.0) + i_offset
dv/dt = 0.04 * v^2 + 5.0 * v + 140.0 - u + I : init = %(c)s
du/dt = a * (b*v - u) : init= %(u)s
""" % { 'conductance' : conductance, 'c':c , 'u': b*c}
# # Default behavior for the conductances (avoid warning)
# for target in targets:
# equations += """
# g_%(target)s = 0.0""" % {'target' : target}
spike = """
v > v_thresh
"""
reset = """
v = c
u += d
"""
Neuron.__init__(self, parameters=parameters, equations=equations, spike=spike, reset=reset, refractory='tau_refrac',
name="Izhikevich", description="Quadratic integrate-and-fire spiking neuron with adaptation.")
# For reporting
self._instantiated.append(True)
def __init__(self,
v_rest=-70.6,
cm=0.281,
tau_m=9.3667,
tau_refrac=0.1,
tau_syn_E=5.0,
tau_syn_I=5.0,
e_rev_E=0.0,
e_rev_I=-80.0,
tau_w=144.0,
a=4.0,
b=0.0805,
i_offset=0.0,
delta_T=2.0,
v_thresh=-50.4,
v_reset=-70.6,
v_spike=-40.0):
# Create the arguments
parameters = """
v_rest = %(v_rest)s
cm = %(cm)s
tau_m = %(tau_m)s
tau_refrac = %(tau_refrac)s
tau_syn_E = %(tau_syn_E)s
tau_syn_I = %(tau_syn_I)s
e_rev_E = %(e_rev_E)s
e_rev_I = %(e_rev_I)s
tau_w = %(tau_w)s
a = %(a)s
b = %(b)s
i_offset = %(i_offset)s
delta_T = %(delta_T)s
v_thresh = %(v_thresh)s
v_reset = %(v_reset)s
v_spike = %(v_spike)s
""" % {
'v_rest': v_rest,
'cm': cm,
'tau_m': tau_m,
'tau_refrac': tau_refrac,
'tau_syn_E': tau_syn_E,
'tau_syn_I': tau_syn_I,
'e_rev_E': e_rev_E,
'e_rev_I': e_rev_I,
'tau_w': tau_w,
'a': a,
'b': b,
'i_offset': i_offset,
'delta_T': delta_T,
'v_thresh': v_thresh,
'v_reset': v_reset,
'v_spike': v_spike,
}
# Equations for the variables
equations = """
gmax_exc = exp((tau_syn_E - dt/2.0)/tau_syn_E)
gmax_inh = exp((tau_syn_I - dt/2.0)/tau_syn_I)
I = alpha_exc * (e_rev_E - v) + alpha_inh * (e_rev_I - v) + i_offset
tau_m * dv/dt = (v_rest - v + delta_T * exp((v-v_thresh)/delta_T)) + tau_m/cm*(I - w) : init=%(v_reset)s
tau_w * dw/dt = a * (v - v_rest) / 1000.0 - w
tau_syn_E * dg_exc/dt = - g_exc : exponential
tau_syn_I * dg_inh/dt = - g_inh : exponential
tau_syn_E * dalpha_exc/dt = gmax_exc * g_exc - alpha_exc : exponential
tau_syn_I * dalpha_inh/dt = gmax_inh * g_inh - alpha_inh : exponential
""" % {
'v_reset': v_reset
}
spike = """
v > v_spike
"""
reset = """
v = v_reset
w += b
"""
Neuron.__init__(
self,
parameters=parameters,
equations=equations,
spike=spike,
reset=reset,
refractory='tau_refrac',
name="Adaptive exponential Integrate-and-Fire",
description=
"Exponential integrate-and-fire neuron with spike triggered and sub-threshold adaptation conductances (isfa, ista reps.)."
)
# For reporting
self._instantiated.append(True)
def __init__(self,
gbar_Na=20.0,
gbar_K=6.0,
gleak=0.01,
cm=0.2,
v_offset=-63.0,
e_rev_Na=50.0,
e_rev_K=-90.0,
e_rev_leak=-65.0,
e_rev_E=0.0,
e_rev_I=-80.0,
tau_syn_E=0.2,
tau_syn_I=2.0,
i_offset=0.0,
v_thresh=0.0):
parameters = """
gbar_Na = %(gbar_Na)s
gbar_K = %(gbar_K)s
gleak = %(gleak)s
cm = %(cm)s
v_offset = %(v_offset)s
e_rev_Na = %(e_rev_Na )s
e_rev_K = %(e_rev_K)s
e_rev_leak = %(e_rev_leak)s
e_rev_E = %(e_rev_E)s
e_rev_I = %(e_rev_I)s
tau_syn_E = %(tau_syn_E)s
tau_syn_I = %(tau_syn_I)s
i_offset = %(i_offset)s
v_thresh = %(v_thresh)s
""" % {
'gbar_Na': gbar_Na,
'gbar_K': gbar_K,
'gleak': gleak,
'cm': cm,
'v_offset': v_offset,
'e_rev_Na': e_rev_Na,
'e_rev_K': e_rev_K,
'e_rev_leak': e_rev_leak,
'e_rev_E': e_rev_E,
'e_rev_I': e_rev_I,
'tau_syn_E': tau_syn_E,
'tau_syn_I': tau_syn_I,
'i_offset': i_offset,
'v_thresh': v_thresh
}
equations = """
# Previous membrane potential
prev_v = v
# Voltage-dependent rate constants
an = 0.032 * (15.0 - v + v_offset) / (exp((15.0 - v + v_offset)/5.0) - 1.0)
am = 0.32 * (13.0 - v + v_offset) / (exp((13.0 - v + v_offset)/4.0) - 1.0)
ah = 0.128 * exp((17.0 - v + v_offset)/18.0)
bn = 0.5 * exp ((10.0 - v + v_offset)/40.0)
bm = 0.28 * (v - v_offset - 40.0) / (exp((v - v_offset - 40.0)/5.0) - 1.0)
bh = 4.0/(1.0 + exp (( 10.0 - v + v_offset )) )
# Activation variables
dn/dt = an * (1.0 - n) - bn * n : init = 0.0, exponential
dm/dt = am * (1.0 - m) - bm * m : init = 0.0, exponential
dh/dt = ah * (1.0 - h) - bh * h : init = 1.0, exponential
# Membrane equation
cm * dv/dt = gleak*(e_rev_leak -v) + gbar_K * n**4 * (e_rev_K - v) + gbar_Na * m**3 * h * (e_rev_Na - v)
+ g_exc * (e_rev_E - v) + g_inh * (e_rev_I - v) + i_offset: exponential, init=%(e_rev_leak)s
# Exponentially-decaying conductances
tau_syn_E * dg_exc/dt = - g_exc : exponential
tau_syn_I * dg_inh/dt = - g_inh : exponential
""" % {
'e_rev_leak': e_rev_leak
}
spike = "(v > v_thresh) and (prev_v <= v_thresh)"
reset = ""
Neuron.__init__(
self,
parameters=parameters,
equations=equations,
spike=spike,
reset=reset,
name="Hodgkin-Huxley",
description=
"Single-compartment Hodgkin-Huxley-type neuron with transient sodium and delayed-rectifier potassium currents."
)
# For reporting
self._instantiated.append(True)
