A comparative study on spiking neural network encoding schema: implemented with cloud computing

Spiking neural networks (SNN) represents the third generation of neural network models, it differs significantly from the early neural network generation. The time is becoming the most important input. The presence and precise timing of spikes encapsulate have a meaning such as human brain behavior. However, deferent techniques are therefore required to submit a stimulus to the neural network to build the timing spike. The characteristics of these spikes are based on their firing time because of the stereotypical nature of the human brain. Neural networks (NN) as engineering tools Operate on analog quantities (analog input, analog output), SNN More powerful than classic NN Interesting to implement in hardware. But the Problem that is internally work with spike trains unequal analog signal, so this algorithm design to firstly convert analog function into spike trains which calling encoding (E) then Convert spike trains into analog function: which calling decoding (D), so to use spiking NN as engineering tool: communication problem must be solved using some international encoding algorithms. This paper discusses techniques of transforming data into a suitable form for SNN submission. We present a comparative study on SNN encoding schema that effect on SNN performance in hardware and software implementation, however, this is the first comprehensive study to discuss encoding algorithms in SNNs in details, which involved the advantages, disadvantages and when and where we can use and implements the encoding algorithms, with focusing on some examples implement SNN in cloud computing generally, and which algorithms still unused in the world of cloud computing to make the door open for new researcher.