Understanding SNN and Its Recent Advancements

Abstract

The SNNs (spiking neural networks) show what is the nature of computer model of nervous system which is like real brain activity. SNNs might actually be more like brains than the traditional neural networks do because both share an architecture similarity. Lower energy consumption and noise-detecting performance are among their uniqueness. Still, though, current SNNs might seem primitive when compared to the new ones, but their potential to be more powerful and efficient in learning is unlimited. SNNs in many forms are used for numerous functional applications. That entails, for instance, being able to recognize visuals, to perceive language or to take actions. Here, the paper gives a wide view on the SNNs, zooming into their recent successes. The article begins with the saying that neural spiking networks take inspiration of nature exactly from the cause. Another part of the text presents the SNN constituent parts and elicits some SNN advantages compared to the regular neural networks. Subsequently, the text will summarize the latest advancements in SNN, taking into account both hardware and algorithms as well. Another part of the paper will be given to future of sub-national networks.