def test_lift_without_state_or_parameters():
data = torch.ones(3, 2, 1)
lifted = lift(lif_feed_forward_step)
z, s = lifted(data)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
assert s.i.shape == (2, 1)
def test_lift_without_state_with_parameters():
data = torch.ones(3, 2, 1)
lifted = lift(lif_feed_forward_step,
p=LIFParameters(v_th=torch.as_tensor(0.3), method="tanh"))
z, s = lifted(data)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
assert s.i.shape == (2, 1)
def test_lift_with_state_without_parameters():
data = torch.ones(3, 2, 1)
lifted = lift(lif_feed_forward_step)
z, s = lifted(
data,
state=LIFFeedForwardState(torch.zeros_like(data[0]),
torch.zeros_like(data[0])),
)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
assert s.i.shape == (2, 1)
def test_lift_with_state_and_parameters():
data = torch.ones(3, 2, 1)
lifted = lift(lif_feed_forward_step,
p=LIFParameters(v_th=torch.as_tensor(0.3), method="tanh"))
z, s = lifted(
data,
state=LIFFeedForwardState(torch.zeros_like(data[0]),
torch.zeros_like(data[0])),
)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
assert s.i.shape == (2, 1)
def test_lift_with_leaky_integrator():
data = torch.ones(3, 2, 1)
lifted = lift(li_step)
z, s = lifted(
data,
state=LIState(
v=torch.zeros(2, 1),
i=torch.zeros(2, 1),
),
input_weights=torch.ones(1, 1),
)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
def test_lift_with_lift_step():
data = torch.ones(3, 2, 1)
lifted = lift(lif_step)
z, s = lifted(
data,
state=LIFState(
v=torch.zeros(2, 1),
i=torch.zeros(2, 1),
z=torch.zeros(2, 1),
),
input_weights=torch.ones(1, 1),
recurrent_weights=torch.ones(1, 1),
)
assert z.shape == (3, 2, 1)
assert s.v.shape == (2, 1)
def lif_feed_forward_integral(
input_tensor: torch.Tensor,
state: LIFFeedForwardState,
p: LIFParameters = LIFParameters(),
dt: float = 0.001,
) -> Tuple[torch.Tensor, LIFState]:
r"""Computes multiple euler-integration steps of a LIF neuron-model. More
specifically it integrates the following ODE
.. math::
\begin{align*}
\dot{v} &= 1/\tau_{\text{mem}} (v_{\text{leak}} - v + i) \\
\dot{i} &= -1/\tau_{\text{syn}} i
\end{align*}
together with the jump condition
.. math::
z = \Theta(v - v_{\text{th}})
and transition equations
.. math::
\begin{align*}
v &= (1-z) v + z v_{\text{reset}} \\
i &= i + i_{\text{in}}
\end{align*}
Parameters:
input_tensor (torch.Tensor): the input spikes with the outer dimension assumed to be timesteps
s (LIFState): current state of the LIF neuron
p (LIFParameters): parameters of a leaky integrate and fire neuron
dt (float): Integration timestep to use
"""
if norse.utils.IS_OPS_LOADED:
try:
z, v, i = norse_op.lif_super_feed_forward_integral(
input_tensor, state, p, dt)
return z, LIFState(z=z, v=v, i=i)
except NameError:
pass
return lift(lif_feed_forward_step)(input_tensor=input_tensor,
state=state,
p=p,
dt=dt)
def lif_step_integral(
input_tensor: torch.Tensor,
state: LIFState,
input_weights: torch.Tensor,
recurrent_weights: torch.Tensor,
p: LIFParameters = LIFParameters(),
dt: float = 0.001,
) -> Tuple[torch.Tensor, LIFState]:
r"""Computes multiple euler-integration steps of a LIF neuron-model. More
specifically it integrates the following ODE
.. math::
\begin{align*}
\dot{v} &= 1/\tau_{\text{mem}} (v_{\text{leak}} - v + i) \\
\dot{i} &= -1/\tau_{\text{syn}} i
\end{align*}
together with the jump condition
.. math::
z = \Theta(v - v_{\text{th}})
and transition equations
.. math::
\begin{align*}
v &= (1-z) v + z v_{\text{reset}} \\
i &= i + w_{\text{input}} z_{\text{in}} \\
i &= i + w_{\text{rec}} z_{\text{rec}}
\end{align*}
where :math:`z_{\text{rec}}` and :math:`z_{\text{in}}` are the recurrent
and input spikes respectively.
Parameters:
input_tensor (torch.Tensor): the input spikes, assuming the outer (first) dimension is time
s (LIFState): current state of the LIF neuron
input_weights (torch.Tensor): synaptic weights for incoming spikes
recurrent_weights (torch.Tensor): synaptic weights for recurrent spikes
p (LIFParameters): parameters of a leaky integrate and fire neuron
dt (float): Integration timestep to use
Returns:
A tuple of (spike output from all timesteps, neuron state from the final timestep)
"""
if state is None:
size = input_tensor.size()[1:]
state = LIFState(
z=torch.zeros(size),
v=torch.full(size, p.v_reset),
i=torch.zeros(size),
)
if norse.utils.IS_OPS_LOADED:
try:
z, v, i = norse_op.lif_super_integral(input_tensor, state,
input_weights,
recurrent_weights, p, dt)
return z, LIFState(z=z, v=v, i=i)
except NameError:
pass
return lift(_lif_step_jit)(
input_tensor=input_tensor,
state=state,
input_weights=input_weights,
recurrent_weights=recurrent_weights,
p=p,
dt=dt,
)
