Class Input

bindsnet/network/nodes.py:172–228

Layer of nodes with user-specified spiking behavior.

class Input(Nodes, AbstractInput):
    # language=rst
    """
    Layer of nodes with user-specified spiking behavior.
    """

    def __init__(
        self,
        n: Optional[int] = None,
        shape: Optional[Iterable[int]] = None,
        traces: bool = False,
        traces_additive: bool = False,
        tc_trace: Union[float, torch.Tensor] = 20.0,
        trace_scale: Union[float, torch.Tensor] = 1.0,
        sum_input: bool = False,
        **kwargs,
    ) -> None:
        # language=rst
        """
        Instantiates a layer of input neurons.

        :param n: The number of neurons in the layer.
        :param shape: The dimensionality of the layer.
        :param traces: Whether to record decaying spike traces.
        :param traces_additive: Whether to record spike traces additively.
        :param tc_trace: Time constant of spike trace decay.
        :param trace_scale: Scaling factor for spike trace.
        :param sum_input: Whether to sum all inputs.
        """
        super().__init__(
            n=n,
            shape=shape,
            traces=traces,
            traces_additive=traces_additive,
            tc_trace=tc_trace,
            trace_scale=trace_scale,
            sum_input=sum_input,
        )

    def forward(self, x: torch.Tensor) -> None:
        # language=rst
        """
        On each simulation step, set the spikes of the population equal to the inputs.

        :param x: Inputs to the layer.
        """
        # Set spike occurrences to input values.
        self.s = x

        super().forward(x)

    def reset_state_variables(self) -> None:
        # language=rst
        """
        Resets relevant state variables.
        """
        super().reset_state_variables()