IAF
IAF#
- class sinabs.layers.IAF(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), tau_syn: typing.Optional[float] = None, min_v_mem: typing.Optional[float] = None, shape: typing.Optional[torch.Size] = None, record_states: bool = False)[source]#
Integrate and Fire neuron layer.
Neuron dynamics in discrete time:
\[ \begin{align}\begin{aligned}V_{mem}(t+1) = V_{mem}(t) + \sum z(t)\\\text{if } V_{mem}(t) >= V_{th} \text{, then } V_{mem} \rightarrow V_{reset}\end{aligned}\end{align} \]where \(\sum z(t)\) represents the sum of all input currents at time \(t\).
- Parameters
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.
tau_syn (float) – Synaptic decay time constants. If None, no synaptic dynamics are used, which is the default.
min_v_mem (float or None) – Lower bound for membrane potential v_mem, clipped at every time step.
shape (torch.Size) – Optionally initialise the layer state with given shape. If None, will be inferred from input_size.
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)#
Forward pass with given data.
- Parameters
input_current – torch.Tensor Data to be processed. Expected shape: (batch, time, …)
- Returns
- torch.Tensor
Output data. Same shape as input_data.