  We study a reinforcement learning for temporal coding with neural network
consisting of stochastic spiking neurons. In neural networks, information can
be coded by characteristics of the timing of each neuronal firing, including
the order of firing or the relative phase differences of firing. We derive the
learning rule for this network and show that the network consisting of
Hodgkin-Huxley neurons with the dynamical synaptic kinetics can learn the
appropriate timing of each neuronal firing. We also investigate the system size
dependence of learning efficiency.
