This paper studies synchronization phenomena in a ring-coupled system of digital spiking neurons. The neuron consists of two shift registers connected by a wiring circuit and can generate various spike-trains. Applying a spike based connection, the ring-coupled system is constructed. The ring-coupled system can generate multi-phase synchronization phenomena of various periodic spike-trains. Using a simple dynamic model, existence and stability of the synchronization phenomena are analyzed. Presenting a FPGA based test circuit, typical synchronization phenomena are confirmed experimentally.

This paper presents a digital spike map and its learning algorithm of spike-trains. The map is characterized by a swarm of particles on lattice points. As a teacher signal is applied, the algorithm finds a winner particle. The winner and its neighbor particles move in a similar way to the self-organizing maps. A new particle can born and the particle swarm can grow depending on the property of teacher signals. If learning parameters are selected suitably, the map can evolve to approximate a class of teacher signals. Performing basic numerical experiments, the algorithm efficiency is confirmed.