def setUp(self):
neuron.Population.nPop = 0
self.net = neuron.Population((3, 3), neuron.IF_curr_alpha)
self.net2 = neuron.Population((5, ), 'StandardIF', {
'syn_type': 'current',
'syn_shape': 'exp'
})
def setUp(self):
neuron.Population.nPop = 0
self.net1 = neuron.Population((10, ), neuron.IF_curr_alpha)
self.net2 = neuron.Population((2, 4, 3), neuron.IF_curr_exp)
self.net3 = neuron.Population((2, 2, 1), neuron.SpikeSourceArray)
self.net4 = neuron.Population((1, 2, 1), neuron.SpikeSourceArray)
self.net5 = neuron.Population((3, 3), neuron.IF_cond_alpha)
def setUp(self):
self.target = neuron.Population((3, 3), neuron.IF_curr_alpha)
self.source = neuron.Population((3, 3), neuron.SpikeSourcePoisson,
{'rate': 200})
self.distrib_Numpy = RandomDistribution(rng=NumpyRNG(12345),
distribution='uniform',
parameters=(0, 1))
self.distrib_Native = RandomDistribution(rng=NativeRNG(12345),
distribution='uniform',
parameters=(0, 1))
def testSimpleInit(self):
"""Population.__init__(): the cell list in hoc should have the same length as the population size."""
net = neuron.Population((3, 3), neuron.IF_curr_alpha)
n_cells = getattr(h, net.label).count()
n_cells_lower = int(getattr(h, net.label).count())
# round-robin distribution
assert 9 / neuron.nhost <= n_cells_lower <= 9 / neuron.nhost + 1, "%d not between %d and %d" % (
n_cells_lower, 9 / neuron.nhost, 9 / neuron.nhost + 1)
def testInitWithParams(self):
"""Population.__init__(): Parameters set on creation should be the same as
retrieved with the top-level HocObject"""
net = neuron.Population((3, 3), neuron.IF_curr_alpha,
{'tau_syn_E': 3.141592654})
cell_list = getattr(h, net.label)
for i in range(int(cell_list.count())):
tau_syn = cell_list.object(i).esyn.tau
self.assertAlmostEqual(tau_syn, 3.141592654, places=5)
def testInitWithNonStandardModel(self):
"""Population.__init__(): the cell list in hoc should have the same length as the population size."""
net = neuron.Population((3, 3), 'StandardIF', {
'syn_type': 'current',
'syn_shape': 'exp'
})
n_cells = getattr(h, net.label).count()
n_cells_lower = int(getattr(h, net.label).count())
# round-robin distribution
assert 9 / neuron.nhost <= n_cells_lower <= 9 / neuron.nhost + 1, "%d not between %d and %d" % (
n_cells_lower, 9 / neuron.nhost, 9 / neuron.nhost + 1)
def testRecordWithSpikeTimesGreaterThanSimTime(self):
"""
If a `SpikeSourceArray` is initialized with spike times greater than the
simulation time, only those spikes that actually occurred should be
written to file or returned by getSpikes().
"""
spike_times = numpy.arange(10.0, 200.0, 10.0)
spike_source = neuron.Population(1, neuron.SpikeSourceArray,
{'spike_times': spike_times})
spike_source.record()
neuron.running = False
neuron.run(100.0)
spikes = spike_source.getSpikes()[:, 1]
if neuron.myid == 0:
self.assert_(max(spikes) == 100.0, str(spikes))
def setUp(self):
neuron.Population.nPop = 0
neuron.Projection.nProj = 0
self.target33 = neuron.Population((3, 3), neuron.IF_curr_alpha)
self.target6 = neuron.Population((6, ), neuron.IF_curr_alpha)
self.source5 = neuron.Population((5, ), neuron.SpikeSourcePoisson)
self.source22 = neuron.Population((2, 2), neuron.SpikeSourcePoisson)
self.source33 = neuron.Population((3, 3), neuron.SpikeSourcePoisson)
self.expoisson33 = neuron.Population((3, 3), neuron.SpikeSourcePoisson,
{'rate': 100})
def setUp(self):
neuron.Population.nPop = 0
self.pop1 = neuron.Population((5, ), neuron.IF_curr_alpha,
{'tau_m': 10.0})
self.pop2 = neuron.Population((5, 4), neuron.IF_curr_exp,
{'v_reset': -60.0})
def setUp(self):
neuron.Population.nPop = 0
self.pop1 = neuron.Population((3, 3), neuron.SpikeSourcePoisson,
{'rate': 20})
self.pop2 = neuron.Population((3, 3), neuron.IF_curr_alpha)
def testInitWithLabel(self):
"""Population.__init__(): A label set on initialisation should be retrievable with the Population.label attribute."""
net = neuron.Population((3, 3),
neuron.IF_curr_alpha,
label='iurghiushrg')
assert net.label == 'iurghiushrg'