  Spiking Neural Networks (SNNs) have been studied over decades to incorporate
their biological plausibility and leverage their promising energy efficiency.
Throughout existing SNNs, the leaky integrate-and-fire (LIF) model is commonly
adopted to formulate the spiking neuron and evolves into numerous variants with
different biological features. However, most LIF-based neurons support only
single biological feature in different neuronal behaviors, limiting their
expressiveness and neuronal dynamic diversity. In this paper, we propose GLIF,
a unified spiking neuron, to fuse different bio-features in different neuronal
behaviors, enlarging the representation space of spiking neurons. In GLIF,
gating factors, which are exploited to determine the proportion of the fused
bio-features, are learnable during training. Combining all learnable
membrane-related parameters, our method can make spiking neurons different and
constantly changing, thus increasing the heterogeneity and adaptivity of
spiking neurons. Extensive experiments on a variety of datasets demonstrate
that our method obtains superior performance compared with other SNNs by simply
changing their neuronal formulations to GLIF. In particular, we train a spiking
ResNet-19 with GLIF and achieve $77.35\%$ top-1 accuracy with six time steps on
CIFAR-100, which has advanced the state-of-the-art. Codes are available at
\url{https://github.com/Ikarosy/Gated-LIF}.
