def test_alif(Simulator):
"""Test ALIF and ALIFRate by comparing them to each other"""
n = 100
max_rates = 50 * np.ones(n)
intercepts = np.linspace(-0.99, 0.99, n)
encoders = np.ones((n, 1))
nparams = dict(tau_n=1, inc_n=10e-3)
eparams = dict(n_neurons=n,
max_rates=max_rates,
intercepts=intercepts,
encoders=encoders)
model = nengo.Network()
with model:
u = nengo.Node(output=0.5)
a = nengo.Ensemble(neuron_type=nengo.AdaptiveLIFRate(**nparams),
dimensions=1,
**eparams)
b = nengo.Ensemble(neuron_type=nengo.AdaptiveLIF(**nparams),
dimensions=1,
**eparams)
nengo.Connection(u, a, synapse=0)
nengo.Connection(u, b, synapse=0)
ap = nengo.Probe(a, "spikes", synapse=0)
bp = nengo.Probe(b, "spikes", synapse=0)
dt = 1e-3
sim = Simulator(model, dt=dt)
sim.run(2.)
t = sim.trange()
a_rates = sim.data[ap] / dt
spikes = sim.data[bp]
b_rates = rates_kernel(t, spikes)
tmask = (t > 0.1) & (t < 1.7)
rel_rmse = rms(b_rates[tmask] - a_rates[tmask]) / rms(a_rates[tmask])
with Plotter(Simulator) as plt:
ax = plt.subplot(311)
implot(plt, t, intercepts[::-1], a_rates.T, ax=ax)
ax.set_ylabel('input')
ax = plt.subplot(312)
implot(plt, t, intercepts[::-1], b_rates.T, ax=ax)
ax.set_ylabel('input')
ax = plt.subplot(313)
implot(plt, t, intercepts[::-1], (b_rates - a_rates)[tmask].T, ax=ax)
ax.set_xlabel('time [s]')
ax.set_ylabel('input')
plt.savefig('test_neurons.test_alif.pdf')
plt.close()
assert rel_rmse < 0.07
def test_rates(Simulator):
functions = [
('isi_zero', lambda t, s: rates_isi(
t, s, midpoint=False, interp='zero')),
('isi_midzero', lambda t, s: rates_isi(
t, s, midpoint=True, interp='zero')),
('isi_linear', lambda t, s: rates_isi(
t, s, midpoint=False, interp='linear')),
('isi_midlinear', lambda t, s: rates_isi(
t, s, midpoint=True, interp='linear')),
('kernel_expon', lambda t, s: rates_kernel(t, s, kind='expon')),
('kernel_gauss', lambda t, s: rates_kernel(t, s, kind='gauss')),
('kernel_expogauss', lambda t, s: rates_kernel(
t, s, kind='expogauss')),
('kernel_alpha', lambda t, s: rates_kernel(t, s, kind='alpha')),
]
print("\ntest_rates:")
for name, function in functions:
rel_rmse = _test_rates(Simulator, function, name)
print("%20s relative rmse: %0.3f" % (name, rel_rmse))
def test_rates(Simulator, plt, seed):
functions = [
('isi_zero', lambda t, s: rates_isi(
t, s, midpoint=False, interp='zero')),
('isi_midzero', lambda t, s: rates_isi(
t, s, midpoint=True, interp='zero')),
('isi_linear', lambda t, s: rates_isi(
t, s, midpoint=False, interp='linear')),
('isi_midlinear', lambda t, s: rates_isi(
t, s, midpoint=True, interp='linear')),
('kernel_expon', lambda t, s: rates_kernel(t, s, kind='expon')),
('kernel_gauss', lambda t, s: rates_kernel(t, s, kind='gauss')),
('kernel_expogauss', lambda t, s: rates_kernel(
t, s, kind='expogauss')),
('kernel_alpha', lambda t, s: rates_kernel(t, s, kind='alpha')),
]
print("\ntest_rates:")
for name, function in functions:
rel_rmse = _test_rates(Simulator, function, plt, seed, name)
print("%20s relative rmse: %0.3f" % (name, rel_rmse))
def test_alif(Simulator):
"""Test ALIF and ALIFRate by comparing them to each other"""
n = 100
max_rates = 50 * np.ones(n)
intercepts = np.linspace(-0.99, 0.99, n)
encoders = np.ones((n, 1))
nparams = dict(tau_n=1, inc_n=10e-3)
eparams = dict(n_neurons=n, max_rates=max_rates,
intercepts=intercepts, encoders=encoders)
model = nengo.Network()
with model:
u = nengo.Node(output=0.5)
a = nengo.Ensemble(neuron_type=nengo.AdaptiveLIFRate(**nparams),
dimensions=1,
**eparams)
b = nengo.Ensemble(neuron_type=nengo.AdaptiveLIF(**nparams),
dimensions=1,
**eparams)
nengo.Connection(u, a, synapse=0)
nengo.Connection(u, b, synapse=0)
ap = nengo.Probe(a, "spikes", synapse=0)
bp = nengo.Probe(b, "spikes", synapse=0)
dt = 1e-3
sim = Simulator(model, dt=dt)
sim.run(2.)
t = sim.trange()
a_rates = sim.data[ap] / dt
spikes = sim.data[bp]
b_rates = rates_kernel(t, spikes)
tmask = (t > 0.1) & (t < 1.7)
rel_rmse = rms(b_rates[tmask] - a_rates[tmask]) / rms(a_rates[tmask])
with Plotter(Simulator) as plt:
ax = plt.subplot(311)
implot(plt, t, intercepts[::-1], a_rates.T, ax=ax)
ax.set_ylabel('input')
ax = plt.subplot(312)
implot(plt, t, intercepts[::-1], b_rates.T, ax=ax)
ax.set_ylabel('input')
ax = plt.subplot(313)
implot(plt, t, intercepts[::-1], (b_rates - a_rates)[tmask].T, ax=ax)
ax.set_xlabel('time [s]')
ax.set_ylabel('input')
plt.savefig('test_neurons.test_alif.pdf')
plt.close()
assert rel_rmse < 0.07
def test_rates(Simulator, seed, logger):
pytest.importorskip('scipy')
functions = [
('isi_zero', lambda t, s: rates_isi(
t, s, midpoint=False, interp='zero')),
('isi_midzero', lambda t, s: rates_isi(
t, s, midpoint=True, interp='zero')),
('isi_linear', lambda t, s: rates_isi(
t, s, midpoint=False, interp='linear')),
('isi_midlinear', lambda t, s: rates_isi(
t, s, midpoint=True, interp='linear')),
('kernel_expon', lambda t, s: rates_kernel(t, s, kind='expon')),
('kernel_gauss', lambda t, s: rates_kernel(t, s, kind='gauss')),
('kernel_expogauss', lambda t, s: rates_kernel(
t, s, kind='expogauss')),
('kernel_alpha', lambda t, s: rates_kernel(t, s, kind='alpha')),
]
for name, function in functions:
rel_rmse = _test_rates(Simulator, function, None, seed)
logger.info('rate estimator: %s', name)
logger.info('relative RMSE: %0.4f', rel_rmse)
def test_rates(Simulator, seed, logger):
pytest.importorskip('scipy')
functions = [
('isi_zero',
lambda t, s: rates_isi(t, s, midpoint=False, interp='zero')),
('isi_midzero',
lambda t, s: rates_isi(t, s, midpoint=True, interp='zero')),
('isi_linear',
lambda t, s: rates_isi(t, s, midpoint=False, interp='linear')),
('isi_midlinear',
lambda t, s: rates_isi(t, s, midpoint=True, interp='linear')),
('kernel_expon', lambda t, s: rates_kernel(t, s, kind='expon')),
('kernel_gauss', lambda t, s: rates_kernel(t, s, kind='gauss')),
('kernel_expogauss',
lambda t, s: rates_kernel(t, s, kind='expogauss')),
('kernel_alpha', lambda t, s: rates_kernel(t, s, kind='alpha')),
]
for name, function in functions:
rel_rmse = _test_rates(Simulator, function, None, seed)
logger.info('rate estimator: %s', name)
logger.info('relative RMSE: %0.4f', rel_rmse)
