  A semi-supervised learning method for spiking neural networks is proposed.
The proposed method consists of supervised learning by backpropagation and
subsequent unsupervised learning by spike-timing-dependent plasticity (STDP),
which is a biologically plausible learning rule. Numerical experiments show
that the proposed method improves the accuracy without additional labeling when
a small amount of labeled data is used. This feature has not been achieved by
existing semi-supervised learning methods of discriminative models. It is
possible to implement the proposed learning method for event-driven systems.
Hence, it would be highly efficient in real-time problems if it were
implemented on neuromorphic hardware. The results suggest that STDP plays an
important role other than self-organization when applied after supervised
learning, which differs from the previous method of using STDP as pre-training
interpreted as self-organization.
