A hierarchical taxonomic survey of spiking neural networks

Artificial Neural Network (ANN) has served as an important pillar of machine learning which played a crucial role in fueling the robust artificial intelligence (AI) revival experienced in the last few years. Inspired by the biological brain architecture of living things, ANN has shown widespread success in pattern recognition, data analysis and classification tasks. Among the many models of neural networks conceptualized and developed over the years, the Spiking Neural Network (SNN) which was initiated in 1996 has shown great promise in the current push towards compact embedded AI. By combining both spatial and temporal information as features in the training and testing process, many inherent shortcomings of traditional ANNs can be overcome. With temporal features and event-driven updating of the network, SNNs hold the potential of improving computational and energy efficiency. In SNN, the most basic signal carrier element is the spike, bringing about a revolution in neural network weights updating compared to traditional methods that are widely applied in ANNs. In literature, there have been numerous SNN weights updating algorithms developed in recent years. With the active and dynamic research work on SNN, a consolidation of the state-of-the-art SNN research is beneficial. This paper is aimed at reviewing and surveying the current status of research pertaining to SNN, in particular highlighting the various novel SNN training techniques coupled with an objective comparison of the techniques. SNN applications and associated neuromorphic hardware systems are also covered in this survey, with some thoughts on the challenges in developing new SNN training algorithms and discussion on potential future research trends are presented in this survey.

Automated summary

This paper reviews the importance of Artificial Neural Network (ANN) in machine learning and artificial intelligence, explores the potential of Spiking Neural Network (SNN) in compact embedded AI, and discusses the current research status of SNN training techniques and weight updating algorithms.