  Spiking Neural Networks (SNNs) use discrete spike sequences to transmit
information, which significantly mimics the information transmission of the
brain. Although this binarized form of representation dramatically enhances the
energy efficiency and robustness of SNNs, it also leaves a large gap between
the performance of SNNs and Artificial Neural Networks based on real values.
There are many different spike patterns in the brain, and the dynamic synergy
of these spike patterns greatly enriches the representation capability.
Inspired by spike patterns in biological neurons, this paper introduces the
dynamic Burst pattern and designs the Leaky Integrate and Fire or Burst (LIFB)
neuron that can make a trade-off between short-time performance and dynamic
temporal performance from the perspective of network information capacity. LIFB
neuron exhibits three modes, resting, Regular spike, and Burst spike. The burst
density of the neuron can be adaptively adjusted, which significantly enriches
the characterization capability. We also propose a decoupling method that can
losslessly decouple LIFB neurons into equivalent LIF neurons, which
demonstrates that LIFB neurons can be efficiently implemented on neuromorphic
hardware. We conducted experiments on the static datasets CIFAR10, CIFAR100,
and ImageNet, which showed that we greatly improved the performance of the SNNs
while significantly reducing the network latency. We also conducted experiments
on neuromorphic datasets DVS-CIFAR10 and NCALTECH101 and showed that we
achieved state-of-the-art with a small network structure.
