class sinabs.layers.LIF(tau_mem: typing.Union[float, torch.Tensor], tau_syn: typing.Optional[typing.Union[float, torch.Tensor]] = None, spike_threshold: float = 1.0, spike_fn: typing.Callable = <class 'sinabs.activation.spike_generation.MultiSpike'>, reset_fn: typing.Callable = MembraneSubtract(subtract_value=None), surrogate_grad_fn: typing.Callable = SingleExponential(grad_width=0.5, grad_scale=1.0), min_v_mem: typing.Optional[float] = None, train_alphas: bool = False, shape: typing.Optional[torch.Size] = None, norm_input: bool = True, record_states: bool = False)[source]#

Leaky Integrate and Fire neuron layer.

Neuron dynamics in discrete time:

\[ \begin{align}\begin{aligned}V_{mem}(t+1) = \alpha V_{mem}(t) + (1-\alpha)\sum z(t)\\\text{if } V_{mem}(t) >= V_{th} \text{, then } V_{mem} \rightarrow V_{reset}\end{aligned}\end{align} \]

where \(\alpha = e^{-1/tau_{mem}}\) and \(\sum z(t)\) represents the sum of all input currents at time \(t\).

  • tau_mem (float) – Membrane potential time constant.

  • tau_syn (float) – Synaptic decay time constants. If None, no synaptic dynamics are used, which is the default.

  • spike_threshold (float) – Spikes are emitted if v_mem is above that threshold. By default set to 1.0.

  • spike_fn (torch.autograd.Function) – Choose a Sinabs or custom torch.autograd.Function that takes a dict of states, a spike threshold and a surrogate gradient function and returns spikes. Be aware that the class itself is passed here (because torch.autograd methods are static) rather than an object instance.

  • reset_fn (Callable) – A function that defines how the membrane potential is reset after a spike.

  • surrogate_grad_fn (Callable) – Choose how to define gradients for the spiking non-linearity during the backward pass. This is a function of membrane potential.

  • min_v_mem (float or None) – Lower bound for membrane potential v_mem, clipped at every time step.

  • train_alphas (bool) – When True, the discrete decay factor exp(-1/tau) is used for training rather than tau itself.

  • shape (torch.Size) – Optionally initialise the layer state with given shape. If None, will be inferred from input_size.

  • norm_input (bool) – When True, normalise input current by tau. This helps when training time constants.

  • record_states (bool) – When True, will record all internal states such as v_mem or i_syn in a dictionary attribute recordings. Default is False.

forward(input_data: torch.Tensor)[source]#

Forward pass with given data.


input_current – torch.Tensor Data to be processed. Expected shape: (batch, time, …)



Output data. Same shape as input_data.