Research on handwritten digits recognition system based on spiking neuron network

Abstract

In the 21st century, deep learning has revolutionized the fields of machine learning and computer science, attaining high accuracy in tasks such as image recognition. More layers and more parameters are stuffed into the network to achieve higher performance, making the network extremely large. A new, radically different approach was proposed to complete the tasks, such as image recognition, using a spiking neural network(SNN). The spiking neural network is event-driven rather than data-driven, which makes it more physiologically realistic and uses a lot less power. This study reviews the development of spiking neural networks and their differences from non-spiking neural networks, as well as the different encoding methods, neuronal models and update rules that have an impact on the performance of the network. It can be concluded that though SNNs can hardly achieve the same accuracy as artificial neural networks(ANN), the gap is narrowing. More strategies from ANN such as back-propagation and convolutional layers have been applied to SNNs, making it more accurate, stable and comprehensive.