def __init__(self, tau_plus=20.0, tau_minus=20.0, A_plus=0.01, A_minus=0.01, w_min=0.0, w_max=1.0):
parameters="""
tau_plus = %(tau_plus)s : postsynaptic
tau_minus = %(tau_minus)s : postsynaptic
A_plus = %(A_plus)s : postsynaptic
A_minus = %(A_minus)s : postsynaptic
w_min = %(w_min)s : postsynaptic
w_max = %(w_max)s : postsynaptic
""" % {'tau_plus': tau_plus, 'tau_minus':tau_minus, 'A_plus':A_plus, 'A_minus': A_minus, 'w_min': w_min, 'w_max': w_max}
equations = """
tau_plus * dx/dt = -x : event-driven
tau_minus * dy/dt = -y : event-driven
"""
pre_spike="""
g_target += w
x += A_plus * w_max
w = clip(w + y, w_min , w_max)
"""
post_spike="""
y -= A_minus * w_max
w = clip(w + x, w_min , w_max)
"""
Synapse.__init__(self, parameters=parameters, equations=equations, pre_spike=pre_spike, post_spike=post_spike,
name="Spike-timing dependent plasticity", description="Synapse exhibiting spike-timing dependent plasticity.")
# For reporting
self._instantiated.append(True)
def __init__(self, tau_rec=100.0, tau_facil=0.01, U=0.5):
if tau_facil<= 0.0:
_error('tau_facil must be positive. Choose a very small value if you have to, or derive a new synapse.')
exit(0)
parameters = """
tau_rec = %(tau_rec)s
tau_facil = %(tau_facil)s
U = %(U)s
""" % {'tau_rec': tau_rec, 'tau_facil': tau_facil, 'U': U}
equations = """
dx/dt = (1 - x)/tau_rec : init = 1.0, event-driven
du/dt = (U - u)/tau_facil : init = %(U)s, event-driven
""" % {'tau_rec': tau_rec, 'tau_facil': tau_facil, 'U': U}
pre_spike="""
g_target += w * u * x
x *= (1 - u)
u += U * (1 - u)
"""
Synapse.__init__(self, parameters=parameters, equations=equations, pre_spike=pre_spike,
name="Short-term plasticity", description="Synapse exhibiting short-term facilitation and depression, implemented using the model of Tsodyks, Markram et al.")
# For reporting
self._instantiated.append(True)
def __init__(self):
Synapse.__init__(self,
equations="",
psp="w * pre.r",
name="-",
description="Weighted sum of firing rates.")
# For reporting
self._instantiated.append(True)
def __init__(self):
Synapse.__init__(self,
pre_spike = "g_target += w",
name="Event-driven synapse",
description="Increases the post-synaptic conductance from the synaptic efficency after each pre-synaptic spike."
)
# For reporting
self._instantiated.append(True)
def __init__(self, psp, operation):
Synapse.__init__(self,
psp=psp, operation=operation,
name="Shared Weight",
description="Weight shared over all synapses of the projection."
)
# For reporting
self._instantiated.append(True)
def __init__(self):
Synapse.__init__(self,
equations="",
psp="w * pre.r",
name="Static weight",
description="Standard weighted sum of firing rates."
)
# For reporting
self._instantiated.append(True)
def __init__(self, psp, operation):
Synapse.__init__(
self,
psp=psp,
operation=operation,
name="Shared Weight",
description="Weight shared over all synapses of the projection.")
# For reporting
self._instantiated.append(True)
def __init__(self):
Synapse.__init__(
self,
pre_spike="g_target += w",
name="Event-driven synapse",
description=
"Increases the post-synaptic conductance from the synaptic efficency after each pre-synaptic spike."
)
# For reporting
self._instantiated.append(True)
def __init__(self, eta=0.01):
parameters = """
eta = %(eta)s
""" % {'eta': eta}
equations = """
dw/dt = eta * pre.r * post.r : min=0.0, explicit
"""
Synapse.__init__(self, parameters=parameters, equations=equations,
name="Hebbian Plasticity", description="Simple Hebbian learning rule")
# For reporting
self._instantiated.append(True)
def __init__(self,
tau_plus=20.0,
tau_minus=20.0,
A_plus=0.01,
A_minus=0.01,
w_min=0.0,
w_max=1.0):
parameters = """
tau_plus = %(tau_plus)s : projection
tau_minus = %(tau_minus)s : projection
A_plus = %(A_plus)s : projection
A_minus = %(A_minus)s : projection
w_min = %(w_min)s : projection
w_max = %(w_max)s : projection
""" % {
'tau_plus': tau_plus,
'tau_minus': tau_minus,
'A_plus': A_plus,
'A_minus': A_minus,
'w_min': w_min,
'w_max': w_max
}
equations = """
tau_plus * dx/dt = -x : event-driven
tau_minus * dy/dt = -y : event-driven
"""
pre_spike = """
g_target += w
x += A_plus * w_max
w = clip(w + y, w_min , w_max)
"""
post_spike = """
y -= A_minus * w_max
w = clip(w + x, w_min , w_max)
"""
Synapse.__init__(
self,
parameters=parameters,
equations=equations,
pre_spike=pre_spike,
post_spike=post_spike,
name="Spike-timing dependent plasticity",
description="Synapse exhibiting spike-timing dependent plasticity."
)
# For reporting
self._instantiated.append(True)
def __init__(self, eta=0.01, alpha=1.0):
parameters = """
eta = %(eta)s
alpha = %(alpha)s
""" % {'eta': eta, 'alpha': alpha}
equations = """
dw/dt = eta * ( pre.r * post.r - alpha * post.r^2 * w ) : min=0.0, explicit
"""
Synapse.__init__(self, parameters=parameters, equations=equations,
name="Oja plasticity", description="Regularized Hebbian learning rule.")
# For reporting
self._instantiated.append(True)
def __init__(self, eta=0.01):
parameters = """
eta = %(eta)s
tau = %(tau)s
""" % {'eta': eta, 'tau': tau}
equations = """
tau * dtheta/dt + theta = post.r^2 : postsynaptic, exponential
dw/dt = eta * post.r * (post.r - theta) * pre.r : min=0.0, explicit
"""
Synapse.__init__(self, parameters=parameters, equations=equations,
name="IBCM", description="Intrator and Cooper (1992) learning rule.")
# For reporting
self._instantiated.append(True)
def __init__(
self,
psp,
operation,
name="Shared synapse",
description="Weight shared over all synapses of the projection."):
"""
"""
# Shared synapses are non-plastic.
Synapse.__init__(self,
psp=psp,
operation=operation,
name=name,
description=description)
# For reporting
self._instantiated.append(True)
def __init__(self, eta=0.01):
parameters = """
eta = %(eta)s : projection
""" % {'eta': eta}
equations = """
dw/dt = eta * pre.r * post.r : min=0.0, explicit
"""
Synapse.__init__(self,
parameters=parameters,
equations=equations,
name="Hebbian Plasticity",
description="Simple Hebbian learning rule")
# For reporting
self._instantiated.append(True)
def __init__(self, tau_rec=100.0, tau_facil=0.01, U=0.5):
if tau_facil <= 0.0:
_error(
'tau_facil must be positive. Choose a very small value if you have to, or derive a new synapse.'
)
exit(0)
parameters = """
tau_rec = %(tau_rec)s
tau_facil = %(tau_facil)s
U = %(U)s
""" % {
'tau_rec': tau_rec,
'tau_facil': tau_facil,
'U': U
}
equations = """
dx/dt = (1 - x)/tau_rec : init = 1.0, event-driven
du/dt = (U - u)/tau_facil : init = %(U)s, event-driven
""" % {
'tau_rec': tau_rec,
'tau_facil': tau_facil,
'U': U
}
pre_spike = """
g_target += w * u * x
x *= (1 - u)
u += U * (1 - u)
"""
Synapse.__init__(
self,
parameters=parameters,
equations=equations,
pre_spike=pre_spike,
name="Short-term plasticity",
description=
"Synapse exhibiting short-term facilitation and depression, implemented using the model of Tsodyks, Markram et al."
)
# For reporting
self._instantiated.append(True)
def __init__(self, eta=0.01, alpha=1.0):
parameters = """
eta = %(eta)s : projection
alpha = %(alpha)s : projection
""" % {
'eta': eta,
'alpha': alpha
}
equations = """
dw/dt = eta * ( pre.r * post.r - alpha * post.r^2 * w ) : min=0.0, explicit
"""
Synapse.__init__(self,
parameters=parameters,
equations=equations,
name="Oja plasticity",
description="Regularized Hebbian learning rule.")
# For reporting
self._instantiated.append(True)
def __init__(self, eta=0.01, tau=2000.0):
parameters = """
eta = %(eta)s : projection
tau = %(tau)s : projection
""" % {
'eta': eta,
'tau': tau
}
equations = """
tau * dtheta/dt + theta = post.r^2 : postsynaptic, exponential
dw/dt = eta * post.r * (post.r - theta) * pre.r : min=0.0, explicit
"""
Synapse.__init__(
self,
parameters=parameters,
equations=equations,
name="IBCM",
description="Intrator and Cooper (1992) learning rule.")
# For reporting
self._instantiated.append(True)