Throughout the central nervous system (CNS), the information communicated between
neurons is mainly implemented by the action potentials (or spikes). Although the spike-timing
based neuronal codes have significant computational advantages over rate encoding scheme, the
exact spike timing-based learning mechanism in the brain remains an open question. To close this
gap, many weight-based supervised learning algorithms have been proposed for spiking neural
networks. However, it is insufficient to consider only synaptic weight plasticity, and biological
evidence suggest that the synaptic delay plasticity also plays an important role in the learning
progress in biological neural networks. Recently, many learning algorithms have been proposed to
consider both the synaptic weight plasticity and synaptic delay plasticity. The goal of this paper is
to give an overview of the existing synaptic delay-based learning algorithms in spiking neural
networks. We described the typical learning algorithms and reported the experimental results.
Finally, we discussed the properties and limitations of each algorithm and made a comparison
among them.
Supervised learning in spiking neural networks with synaptic delay-weight plasticity
