def _test_lif_base(self, cls=nengo.LIF):
"""Test that the dynamic model approximately matches the rates"""
rng = np.random.RandomState(85243)
dt = 0.001
d = 1
n = 5000
m = nengo.Model("")
ins = Signal(0.5 * np.ones(d), name='ins')
lif = cls(n)
lif.set_gain_bias(max_rates=rng.uniform(low=10, high=200, size=n),
intercepts=rng.uniform(low=-1, high=1, size=n))
m.operators = []
b = Builder()
b.model = m
b._builders[cls](lif)
m.operators += [DotInc(Signal(np.ones((n, d))), ins, lif.input_signal)]
sim = self.Simulator(m, dt=dt, builder=testbuilder)
t_final = 1.0
spikes = np.zeros(n)
for i in range(int(np.round(t_final / dt))):
sim.step()
spikes += sim.signals[lif.output_signal]
math_rates = lif.rates(sim.signals[lif.input_signal] - lif.bias)
sim_rates = spikes / t_final
logger.debug("ME = %f", (sim_rates - math_rates).mean())
logger.debug("RMSE = %f",
rms(sim_rates - math_rates) / (rms(math_rates) + 1e-20))
self.assertTrue(np.sum(math_rates > 0) > 0.5*n,
"At least 50% of neurons must fire")
self.assertTrue(np.allclose(sim_rates, math_rates, atol=1, rtol=0.02))
def _test_lif_base(self, cls=LIF):
"""Test that the dynamic model approximately matches the rates"""
rng = np.random.RandomState(85243)
dt = 0.001
d = 1
n = 5e3
m = nengo.Model("")
ins = Signal(n=d, name='ins')
lif = cls(n)
lif.set_gain_bias(max_rates=rng.uniform(low=10, high=200, size=n),
intercepts=rng.uniform(low=-1, high=1, size=n))
m.signals = [ins]
m.operators = []
b = Builder()
b._builders[cls](lif, m, dt)
m.operators += [
DotInc(Constant(np.ones((n, d))), ins, lif.input_signal)
]
sim = m.simulator(sim_class=self.Simulator, dt=dt, builder=testbuilder)
sim.signals[ins] = 0.5 * np.ones(d)
t_final = 1.0
spikes = np.zeros(n)
for i in xrange(int(np.round(t_final / dt))):
sim.step()
spikes += sim.signals[lif.output_signal]
math_rates = lif.rates(sim.signals[lif.input_signal] - lif.bias)
sim_rates = spikes / t_final
logger.debug("ME = %f", (sim_rates - math_rates).mean())
logger.debug("RMSE = %f",
rms(sim_rates - math_rates) / (rms(math_rates) + 1e-20))
self.assertTrue(
np.sum(math_rates > 0) > 0.5 * n,
"At least 50% of neurons must fire")
self.assertTrue(np.allclose(sim_rates, math_rates, atol=1, rtol=0.02))
