  A synfire chain is a simple neural network model which can propagate stable
synchronous spikes called a pulse packet and widely researched. However how
synfire chains coexist in one network remains to be elucidated. We have studied
the activity of a layered associative network of Leaky Integrate-and-Fire
neurons in which connection we embed memory patterns by the Hebbian Learning.
We analyzed their activity by the Fokker-Planck method. In our previous report,
when a half of neurons belongs to each memory pattern (memory pattern rate
$F=0.5$), the temporal profiles of the network activity is split into
temporally clustered groups called sublattices under certain input conditions.
In this study, we show that when the network is sparsely connected ($F<0.5$),
synchronous firings of the memory pattern are promoted. On the contrary, the
densely connected network ($F>0.5$) inhibit synchronous firings. The sparseness
and denseness also effect the basin of attraction and the storage capacity of
the embedded memory patterns. We show that the sparsely(densely) connected
networks enlarge(shrink) the basion of attraction and increase(decrease) the
storage capacity.
