  In networks of periodically firing spiking neurons that are interconnected
with chemical synapses, we analyze cluster state, where an ensemble of neurons
are subdivided into a few clusters, in each of which neurons exhibit perfect
synchronization. To clarify stability of cluster state, we decompose linear
stability of the solution into two types of stabilities: stability of mean
state and stabilities of clusters. Computing Floquet matrices for these
stabilities, we clarify the total stability of cluster state for any types of
neurons and any strength of interactions even if the size of networks is
infinitely large. First, we apply this stability analysis to investigating
synchronization in the large ensemble of integrate-and-fire (IF) neurons. In
one-cluster state we find the change of stability of a cluster, which
elucidates that in-phase synchronization of IF neurons occurs with only
inhibitory synapses. Then, we investigate entrainment of two clusters of IF
neurons with different excitability. IF neurons with fast decaying synapses
show the low entrainment capability, which is explained by a pitchfork
bifurcation appearing in two-cluster state with change of synapse decay time
constant. Second, we analyze one-cluster state of Hodgkin-Huxley (HH) neurons
and discuss the difference in synchronization properties between IF neurons and
HH neurons.
