def test_ConnectOptions(self):
"""ConnectOptions"""
nest.ResetKernel()
copts = nest.sli_func('GetOptions', '/RandomConvergentConnect', litconv=True)
dopts = nest.sli_func('GetOptions', '/RandomDivergentConnect', litconv=True)
ncopts = dict([(k, not v) for k,v in copts.items() if k != 'DefaultOptions'])
ndopts = dict([(k, not v) for k,v in dopts.items() if k != 'DefaultOptions'])
n = nest.Create('iaf_neuron', 3)
nest.RandomConvergentConnect(n, n, 1, options=ncopts)
nest.RandomDivergentConnect (n, n, 1, options=ndopts)
self.assertEqual(copts,
nest.sli_func('GetOptions', '/RandomConvergentConnect', litconv=True))
self.assertEqual(dopts,
nest.sli_func('GetOptions', '/RandomDivergentConnect', litconv=True))
def ComputePSPnorm(tauMem, CMem, tauSyn):
"""Compute the maximum of postsynaptic potential
for a synaptic input current of unit amplitude
(1 pA)"""
a = (tauMem / tauSyn)
b = (1.0 / tauSyn - 1.0 / tauMem)
# time of maximum
t_max = 1.0/b * ( -nest.sli_func('LambertWm1',-exp(-1.0/a)/a) - 1.0/a )
# maximum of PSP for current of unit amplitude
return exp(1.0)/(tauSyn*CMem*b) * ((exp(-t_max/tauMem) - exp(-t_max/tauSyn)) / b - t_max*exp(-t_max/tauSyn))
