Over the past few years, spiking neural networks(SNNs) originated from computational neuroscience have received extensive attention in the field of neuromorphic engineering and brain inspired computing due to their rich spatio-temporal dynamics, diverse coding schemes, and event-driven characteristics that naturally fit the neuromorphic hardware. Combining neuroscience-oriented spiking neural networks with computer-science-oriented artificial neural networks such as deep convolutional neural networks, is considered as a promising approach to artificial general intelligence. In this survey, we review the recent advances of spiking neural networks and focus on five major research areas, which we define as spiking neuron models, SNN algorithms, programming frameworks, datasets and neuromorphic hardware, and conclude with a broad discussion on the opportunities and challenges. The goals of this work are to provide an exhaustive review of spiking neural networks, attract researchers in different disciplines and motivate new works in this field through interdisciplinary exchange of ideas and collaborative research.