def test_softlifrate_rates(plt):
gain = 0.9
bias = 1.7
tau_rc = 0.03
tau_ref = 0.002
lif = nengo.LIFRate(tau_rc=tau_rc, tau_ref=tau_ref)
softlif = SoftLIFRate(tau_rc=tau_rc, tau_ref=tau_ref, sigma=0.00001)
x = np.linspace(-2, 2, 301)
lif_r = lif.rates(x, gain, bias)
softlif_r = softlif.rates(x, gain, bias)
plt.plot(x, lif_r)
plt.plot(x, softlif_r)
assert np.allclose(softlif_r, lif_r, atol=1e-3, rtol=1e-3)
def test_softlifrate_derivative(plt):
sigma = 0.02
amplitude = 0.8
gain = 0.9
bias = 1.7
x, dx = np.linspace(-2, 2, 301, retstep=True)
x2 = 0.5 * (x[:-1] + x[1:])
neuron = SoftLIFRate(sigma=sigma, amplitude=amplitude)
r = neuron.rates(x, gain, bias)
deltar = np.diff(r) / dx
dr = neuron.derivative(x2, gain, bias)
plt.subplot(211)
plt.plot(x, r)
plt.subplot(212)
plt.plot(x2, dr)
plt.plot(x2, deltar, 'k--')
assert np.allclose(dr, deltar, atol=1e-5, rtol=1e-2)