Online versus offline learning for spiking neural networks: A review and new strategies

Abstract

Spiking Neural Networks (SNNs) are considered to be the third generation of neural networks, and have proved more powerful than classical artificial neural networks from the previous generations. The main reason for studying SNNs lies in their close resemblance with biological neural networks. However their applicability in real world applications has been limited due to the lack of efficient training methods. For training large networks on large data sets, online learning is the more natural approach for learning non-stationary tasks. In this paper, existing offline and online learning algorithms for SNNs will be reviewed, the issue that online learning algorithms for SNNs were less developed will be highlighted, and future lines of research related to online training of SNNs will be presented.