def test_dendritic_input():
torch.manual_seed(SEED)
r_in = torch.Tensor(1, 5).normal_(std=0.1) + 5.0
model = AbstractConvexCell([3, 2], 1)
u_in = model.f_inv(r_in)
assert model.dendritic_input(u_in, 0)[0].tolist() == pytest.approx(
r_in[0, :3].tolist())
assert model.dendritic_input(u_in, 1)[0].tolist() == pytest.approx(
r_in[0, 3:3 + 2].tolist())
def test_gff0_and_uff0():
torch.manual_seed(SEED)
gL0 = 0.333
EL = -72.0
model = AbstractConvexCell([1], 1)
model.gL0 = gL0
model.EL = EL
gff0, uff0 = model.compute_gff0_and_uff0()
assert gff0 == pytest.approx(gL0)
assert uff0 == pytest.approx(EL)
def test_scale_weightsI():
torch.manual_seed(SEED)
wE = torch.Tensor([[1.618]])
wI = torch.Tensor([[3.141]])
scale = 0.1
model = AbstractConvexCell([1], 1)
model.set_weightsE(0, wE)
model.set_weightsI(0, wI)
model.scale_weightsI(scale)
assert model.weightsE(0).item() == pytest.approx(wE.item())
assert model.weightsI(0).item() == pytest.approx(scale * wI.item())
def test_weights_from_omega_omega_from_weights():
torch.manual_seed(SEED)
model = AbstractConvexCell([1], 1)
assert model.weightsE(0).item() == pytest.approx(
model.weights_from_omega(model.omega_from_weights(
model.weightsE(0))).item()), SEED
def test_set_weights():
torch.manual_seed(SEED)
w0 = torch.Tensor([[1.618]])
w1 = torch.Tensor([[3.141]])
model = AbstractConvexCell([1, 1], 1)
model.set_weightsE(0, w0)
model.set_weightsE(1, w1)
model.set_weightsI(0, w0)
model.set_weightsI(1, w1)
assert model.weightsE(0).item() == pytest.approx(w0.item())
assert model.weightsE(1).item() == pytest.approx(w1.item())
assert model.weightsI(0).item() == pytest.approx(w0.item())
assert model.weightsI(1).item() == pytest.approx(w1.item())
def test_input_scale():
torch.manual_seed(SEED)
scale_0 = 0.15
scale_1 = 1.05
r_in = torch.Tensor(1, 5).normal_(std=0.1) + 5.0
model = AbstractConvexCell([3, 2], 1)
model.set_input_scale(0, scale_0)
model.set_input_scale(1, scale_1)
u_in = model.f_inv(r_in)
assert model.dendritic_input(u_in, 0)[0].tolist() == pytest.approx(
(scale_0 * r_in[0, :3]).tolist())
assert model.dendritic_input(u_in, 1)[0].tolist() == pytest.approx(
(scale_1 * r_in[0, 3:3 + 2]).tolist())
def test_copy_omegaE_omegaI_from():
torch.manual_seed(SEED)
model = AbstractConvexCell([1], 1)
model_other = AbstractConvexCell([1], 1)
assert model.weightsE(0).item() != pytest.approx(
model_other.weightsE(0).item())
assert model.weightsI(0).item() != pytest.approx(
model_other.weightsI(0).item())
model.copy_omegaE_omegaI_from(model_other)
assert model.weightsE(0).item() == pytest.approx(
model_other.weightsE(0).item())
assert model.weightsI(0).item() == pytest.approx(
model_other.weightsI(0).item())
def test_gffd_and_uffd_w_input():
torch.manual_seed(SEED)
gLd = 0.333
EL = -68.0
EE = -10.0
EI = -86.0
wE = torch.Tensor([[[1.618]]])
wI = torch.Tensor([[[3.141]]])
r_in = torch.Tensor(1, 1).normal_(std=0.1) + 5.0
gffd_expected = gLd + wE * r_in + wI * r_in
uffd_expected = (gLd * EL + wE * r_in * EE +
wI * r_in * EI) / gffd_expected
model = AbstractConvexCell([1], 1)
model.gLd[0] = gLd
model.EL = EL
model.EE = EE
model.EI = EI
model.set_weightsE(0, wE[0])
model.set_weightsI(0, wI[0])
u_in = model.f_inv(r_in)
gffd, uffd = model.compute_gffd_and_uffd(u_in)
assert gffd[0][0].tolist() == pytest.approx(gffd_expected[0][0].tolist())
assert uffd[0][0].tolist() == pytest.approx(uffd_expected[0][0].tolist())