def test_identicalprojection(self):
for pop in self.pops:
for pop_in in self.pops_in.values():
pynn.Projection(pop_in, pop, pynn.OneToOneConnector())
pynn.Projection(pynn.PopulationView(pop_in, [0]),
pop,
pynn.OneToOneConnector())
def test_number_of_spike_trains(self):
"""
Here we test that only spikes are recorded for the neurons we set in
the spike recording mode.
"""
runtime = 0.5 # ms
pop_size = 4
pops = []
pops.append(pynn.Population(pop_size, pynn.cells.HXNeuron()))
pops.append(pynn.Population(pop_size, pynn.cells.HXNeuron()))
pops.append(pynn.Population(pop_size, pynn.cells.HXNeuron()))
# Once record whole population, once subset and once no neurons
pops[0].record(['spikes'])
pynn.PopulationView(pops[1], [0]).record(['spikes'])
# Spike input
input_pop = pynn.Population(1, pynn.cells.SpikeSourceArray(
spike_times=np.arange(0.1, runtime, runtime / 10)))
for pop in pops:
pynn.Projection(input_pop, pop, pynn.AllToAllConnector(),
synapse_type=StaticSynapse(weight=63))
pynn.run(runtime)
# Assert correct number of recorded spike trains
self.assertEqual(len(pops[0].get_data().segments[0].spiketrains),
pop_size)
self.assertEqual(len(pops[1].get_data().segments[0].spiketrains), 1)
self.assertEqual(len(pops[2].get_data().segments[0].spiketrains), 0)
def test_popviewalltoall(self):
for pop in self.pops:
for pop_in in self.pops_in.values():
pynn.Projection(pop_in,
pynn.PopulationView(pop, [0]),
pynn.AllToAllConnector())
def test_popview1to1(self):
for pop in self.pops:
for pop_in in self.pops_in.values():
pynn.Projection(pop_in,
pynn.PopulationView(pop, [0]),
pynn.OneToOneConnector())
# Reset potential, range: 300-1000
reset_v_reset=400,
# Membrane capacitance, range: 0-63
membrane_capacitance_capacitance=63,
# Refractory time, range: 0-255
refractory_period_refractory_time=120,
# Enable reset on threshold crossing
threshold_enable=True,
# Reset conductance, range: 0-1022
reset_i_bias=1022,
# Enable strengthening of reset conductance
reset_enable_multiplication=True))
for neuron_id in range(len(pop)):
logger.INFO(f"Recording fixed-pattern variations: Run {neuron_id}")
p_view = pynn.PopulationView(pop, [neuron_id])
p_view.record(["v"])
pynn.run(0.1)
plot_membrane_dynamics(p_view)
pynn.reset()
pop.record(None)
# Show the recorded membrane traces of multiple different neurons. Due to
# the time-continuous nature of the system, there is no temporal alignment
# between the individual traces, so the figure shows multiple independent
# effects:
# * Temporal misalignment: From the system's view, the recording happens in
# an arbitrary time frame during the continuously evolving integration.
# Neurons are not synchronized to each other.
# * Circuit-level mismatch: Each individual neurons shows slightly
# different analog properties. The threshold is different for all traces;