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In recent years, the mature ANN has been used in many important areas such as finance, security and healthcare, however, the large amount of computational resources consumed makes it computationally expensive for ANN to solve simple problems. In addition, misclassification brought by invisible noise perturbation during ANN training will lead to irreparable consequences. A large number of robust ANN-based experiments [14-16] have emerged to reduce the effects of noise, but they are all accompanied by cumbersome experimental steps, so there is an urgent need for a simple and effective highly robust scheme to emerge as an alternative to ANN in noise input.
Meanwhile, a new generation of neural network, SNN, has been explored to simulate the function of human brain to achieve complex cognitive tasks under low power consumption, with the advantageous features of simulating biological neuron mechanism and propagating spatio-temporal information by spike as signal.
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IEEE/ICACT20230030 Slide.02
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