  In recent years, spiking neural networks (SNNs) have received extensive
attention in brain-inspired intelligence due to their rich spatially-temporal
dynamics, various encoding methods, and event-driven characteristics that
naturally fit the neuromorphic hardware. With the development of SNNs,
brain-inspired intelligence, an emerging research field inspired by brain
science achievements and aiming at artificial general intelligence, is becoming
hot. This paper reviews recent advances and discusses new frontiers in SNNs
from five major research topics, including essential elements (i.e., spiking
neuron models, encoding methods, and topology structures), neuromorphic
datasets, optimization algorithms, software, and hardware frameworks. We hope
our survey can help researchers understand SNNs better and inspire new works to
advance this field.
