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_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)
dt = 0.0001 # use finer resolution cz of high bursting
with nengo.Network() as model:
# parameters tuned for EBN/IBN
neuron = nengo.Izhikevich(reset_voltage=-70, reset_recovery=2,
tau_recovery=0.005)
ens = nengo.Ensemble(n_neurons=1, dimensions=1, encoders=[[1]],
intercepts=[-0.9], max_rates=[150],
neuron_type=neuron)
# ens = nengo.Ensemble(n_neurons=1, dimensions=1, encoders=[[1]],
# intercepts=[-0.9])
node = nengo.Node(output=lambda t: 1 if t < dur else -1)
nengo.Connection(node, ens)
sp = nengo.Probe(ens.neurons, attr='spikes')
cp = nengo.Probe(ens, attr='input')
s = nengo.Simulator(model, dt=dt)
s.run(0.1)
plt.figure()
plt.subplot(221)
plt.plot(s.trange(), s.data[sp])
plt.subplot(222)
plt.plot(s.trange(), rates_isi(s.trange(), s.data[sp]))
plt.subplot(223)
plt.plot(s.trange(), s.data[cp])
plt.subplot(224)
plt.plot(*tuning_curves(ens, s))
plt.show()
