  Emergence of deep neural networks (DNNs) has raised enormous attention
towards artificial neural networks (ANNs) once again. They have become the
state-of-the-art models and have won different machine learning challenges.
Although these networks are inspired by the brain, they lack biological
plausibility, and they have structural differences compared to the brain.
Spiking neural networks (SNNs) have been around for a long time, and they have
been investigated to understand the dynamics of the brain. However, their
application in real-world and complicated machine learning tasks were limited.
Recently, they have shown great potential in solving such tasks. Due to their
energy efficiency and temporal dynamics there are many promises in their future
development. In this work, we reviewed the structures and performances of SNNs
on image classification tasks. The comparisons illustrate that these networks
show great capabilities for more complicated problems. Furthermore, the simple
learning rules developed for SNNs, such as STDP and R-STDP, can be a potential
alternative to replace the backpropagation algorithm used in DNNs.

Neural and Evolutionary Computing;Computer Vision and Pattern Recognition;Neurons and Cognition