An analysis on dynamics of pulse propagation networks

We study pulse propagation networks (PPN), in which every action potential is important, rather than average firing rates as in conventional neural networks. As we use pulses of action potential as the information signals propagating in this networks, the PPN are essentially a continuous time model. However, the internal variable of each neuron changes if and only if the neuron has received a pulse. Therefore, the PPN have event discrete dynamics. We adopt a kind of an excitable neuron model called "Z-model" as the fundamental element in the PPN and numerically analyze dynamics of some simple PPN.