def main():
"""
Example of Interpol object.
"""
simtime = 1.0
simdt = 0.001
model = moose.Neutral('/model')
data = moose.Neutral('/data')
interpol = moose.Interpol('/model/sin')
vec = np.sin(np.linspace(-3.14, 3.14, 100))
interpol.vector = vec
interpol.xmax = 3.14
interpol.xmin = -3.14
recorded = moose.Table('/data/output')
moose.connect(recorded, 'requestOut', interpol, 'getY')
stimtab = moose.StimulusTable('/model/x')
stimtab.stepSize = 0.0
# stimtab.startTime = 0.0
# stimtab.stopTime = simtime
stimtab.vector = np.linspace(-4, 4, 1000)
print((stimtab.vector))
print((interpol.vector))
moose.connect(stimtab, 'output', interpol, 'input')
moose.setClock(0, simdt)
moose.useClock(0, '/data/##,/model/##', 'process')
moose.reinit()
moose.start(simtime)
plt.plot(np.linspace(0, simtime, len(recorded.vector)),
recorded.vector,
'b-+',
label='interpolated')
plt.plot(np.linspace(0, simtime, len(vec)), vec, 'r-+', label='original')
plt.show()
def __init__(self, *args):
moose.SynChan.__init__(self, *args)
self.Ek = -80e-3 # V
# For event based synapses, I had a strength of 5e-6 S
# to compensate for event-based,
# but for the original graded synapses, 5e-9 S is correct.
self.Gbar = 5e-9 # S # set weight on connecting the network
self.tau1 = 100e-3 # s # this is Vpre dependent (see below)
self.tau2 = 0.0 # single first order equation
Vth = -35e-3 # V
Delta = 5e-3 # V
######## Graded synapse activation
inhsyntable = moose.Interpol(self.path + "/graded_table")
graded = moose.Mstring(self.path + '/graded')
graded.value = 'True'
mgblock = moose.Mstring(self.path + '/mgblockStr')
mgblock.value = 'False'
# also needs a synhandler
moosesynhandler = moose.SimpleSynHandler(self.path + '/handler')
# connect the SimpleSynHandler or the STDPSynHandler to the SynChan (double exp)
moose.connect(moosesynhandler, 'activationOut', self, 'activation')
# ds/dt = s_inf/tau - s/tau = A - Bs
# where A=s_inf/tau is activation, B is 1/tau
# Fill up the activation and tau tables
# Graded synapse tau
inhtautable = moose.Interpol(self.path + "/tau_table")
inhtautable.xmin = -70e-3 # V
inhtautable.xmax = 0e-3 # V
tau = [self.tau1] # at -70 mV
tau.extend( [self.tau1*(1. - 1./(1+math.exp((Vth-vm)/Delta))) \
for vm in arange(-70e-3,0.00001e-3,70e-3/1000.)] )
inhtautable.vector = array(tau)
inhtautable.connect("lookupOut", self, "setTau1")
# Graded synapse activation
inhsyntable.xmin = -70e-3 # V
inhsyntable.xmax = 0e-3 # V
act = [0.0] # at -70 mV
act.extend( [1/(1+math.exp((Vth-vm)/Delta)) \
for vm in arange(-70e-3,0.00001e-3,70e-3/1000.)] )
act = array(act) / array(
tau) # element-wise division # NOTE: A = s_inf/tau
inhsyntable.vector = array(act)
inhsyntable.connect("lookupOut", self, "activation")
"""Example of Interpol object."""
__author__ = 'Subhasis Ray'
import sys
import math
import numpy as np
from matplotlib import pyplot as plt
sys.path.append('../../python')
import moose
simtime = 1.0
simdt = 0.001
model = moose.Neutral('/model')
data = moose.Neutral('/data')
interpol = moose.Interpol('/model/sin')
vec = np.sin(np.linspace(-3.14, 3.14, 100))
interpol.vector = vec
interpol.xmax = 3.14
interpol.xmin = -3.14
recorded = moose.Table('/data/output')
moose.connect(recorded, 'requestOut', interpol, 'getY')
stimtab = moose.StimulusTable('/model/x')
stimtab.stepSize = 0.0
# stimtab.startTime = 0.0
# stimtab.stopTime = simtime
stimtab.vector = np.linspace(-4, 4, 1000)
print((stimtab.vector))
print((interpol.vector))
moose.connect(stimtab, 'output', interpol, 'input')