/usr/share/pyshared/brian/stp.py is in python-brian 1.4.1-2.
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Short-term synaptic plasticity.
Implements the short-term plasticity model described in:
Markram et al (1998). Differential signaling via the same axon of
neocortical pyramidal neurons, PNAS. Synaptic dynamics is
described by two variables x and u, which follows the following differential equations::
dx/dt=(1-x)/taud (depression)
du/dt=(U-u)/tauf (facilitation)
where taud, tauf are time constants and U is a parameter in 0..1. Each presynaptic
spike triggers modifications of the variables::
x<-x*(1-u)
u<-u+U*(1-u)
Synaptic weights are modulated by the product u*x (in 0..1) (before update).
'''
# See BEP-1
from network import NetworkOperation
from neurongroup import NeuronGroup
from monitor import SpikeMonitor
from scipy import zeros, exp, isscalar
from connections import DelayConnection
__all__ = ['STP']
class STPGroup(NeuronGroup):
'''
Neuron group forwarding spikes with short term plasticity modulation.
'''
def __init__(self, N, clock=None):
eqs = '''
ux : 1
x : 1
u : 1
'''
NeuronGroup.__init__(self, N, model=eqs, clock=clock)
def update(self):
pass
class STPUpdater(SpikeMonitor):
'''
Event-driven updates of STP variables.
'''
def __init__(self, source, P, taud, tauf, U, delay=0):
SpikeMonitor.__init__(self, source, record=False, delay=delay)
# P is the group with the STP variables
N = len(P)
self.P = P
self.minvtaud = -1. / taud
self.minvtauf = -1. / tauf
self.U = U
self.ux = P.ux
self.x = P.x
self.u = P.u
self.lastt = zeros(N) # last update
self.clock = P.clock
def propagate(self, spikes):
interval = self.clock.t - self.lastt[spikes]
self.u[spikes] = self.U + (self.u[spikes] - self.U) * exp(interval * self.minvtauf)
tmp = 1 - self.u[spikes]
self.x[spikes] = 1 + (self.x[spikes] - 1) * exp(interval * self.minvtaud)
self.ux[spikes] = self.u[spikes] * self.x[spikes]
self.x[spikes] *= tmp
self.u[spikes] += self.U * tmp
self.lastt[spikes] = self.clock.t
self.P.LS.push(spikes)
class STPUpdater2(STPUpdater):
'''
STP Updater where U, taud and tauf are vectors
'''
def propagate(self, spikes):
interval = self.clock.t - self.lastt[spikes]
self.u[spikes] = self.U[spikes] + (self.u[spikes] - self.U[spikes]) * exp(interval * self.minvtauf[spikes])
tmp = 1 - self.u[spikes]
self.x[spikes] = 1 + (self.x[spikes] - 1) * exp(interval * self.minvtaud[spikes])
self.ux[spikes] = self.u[spikes] * self.x[spikes]
self.x[spikes] *= tmp
self.u[spikes] += self.U[spikes] * tmp
self.lastt[spikes] = self.clock.t
self.P.LS.push(spikes)
class SynapticDepressionUpdater(SpikeMonitor):
'''
Event-driven updates of STP variables.
Special case: tauf=0*ms (synaptic depression).
dx/dt=(1-x)/taud (depression)
x<-x*(1-U)
NOT FINISHED
'''
def __init__(self, source, P, taud, tauf, U, delay=0):
SpikeMonitor.__init__(self, source, record=False, delay=delay)
# P is the group with the STP variables
N = len(P)
self.P = P
self.minvtaud = -1. / taud
self.U = U
self.ux = P.ux
self.x = P.x
self.lastt = zeros(N) # last update
self.clock = P.clock
def propagate(self, spikes):
interval = self.clock.t - self.lastt[spikes]
self.x[spikes] = 1 + (self.x[spikes] - 1) * exp(interval * self.minvtaud)
self.ux[spikes] = self.U * self.x[spikes]
self.x[spikes] *= 1 - self.U
self.lastt[spikes] = self.clock.t
self.P.LS.push(spikes)
class STP(NetworkOperation):
'''
Short-term synaptic plasticity, following the Tsodyks-Markram model.
Implements the short-term plasticity model described in Markram et al (1998).
Differential signaling via the same axon of
neocortical pyramidal neurons, PNAS.
Synaptic dynamics is described by two variables x and u, which follow
the following differential equations::
dx/dt=(1-x)/taud (depression)
du/dt=(U-u)/tauf (facilitation)
where taud, tauf are time constants and U is a parameter in 0..1. Each presynaptic
spike triggers modifications of the variables::
u<-u+U*(1-u)
x<-x*(1-u)
Synaptic weights are modulated by the product ``u*x`` (in 0..1) (before update).
Reference:
* Markram et al (1998). "Differential signaling via the same axon of
neocortical pyramidal neurons", PNAS.
'''
def __init__(self, C, taud, tauf, U):
if isinstance(C, DelayConnection):
raise AttributeError, "STP does not handle heterogeneous connections yet."
NetworkOperation.__init__(self, lambda:None, clock=C.source.clock)
N = len(C.source)
P = STPGroup(N, clock=C.source.clock)
P.x = 1
P.u = U
P.ux = U
if (isscalar(taud) & isscalar(tauf) & isscalar(U)):
updater = STPUpdater(C.source, P, taud, tauf, U, delay=C.delay * C.source.clock.dt)
else:
updater = STPUpdater2(C.source, P, taud, tauf, U, delay=C.delay * C.source.clock.dt)
self.contained_objects = [updater]
C.source = P
C.delay = 0
C._nstate_mod = 0 # modulation of synaptic weights
self.vars = P
def __call__(self):
pass
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